A Narrative Review of Systematic Reviews on the Applications of Social and Assistive Support Robots in the Health Domain

Abstract

As the interest in social and assistive support robots (SASRs) grows, a review of 17 systematic reviews was conducted to assess their use in healthcare, emotional well-being, and therapy for diverse populations, including older adults, children, and individuals with autism and dementia. SASRs have demonstrated potential in alleviating depression, loneliness, anxiety, and stress, while also improving sleep and cognitive function. Despite these promising outcomes, challenges remain in identifying the most effective interventions, refining robot designs, and evaluating long-term impacts. User acceptance hinges on trustworthiness and empathy-driven design. Compared to earlier review studies, recent research emphasizes the ongoing significance of emotional engagement, the refinement of robot functionalities, and the need to address ethical issues such as privacy and autonomy through robust cybersecurity and data privacy measures. The field is gradually shifting towards a user-centered design approach, focusing on robots as tools to augment, rather than replace, human care. While SASRs offer substantial benefits for emotional well-being and therapeutic support, further research is crucial to enhance their effectiveness and address concerns about replacing human care. Algorethics (AI ethics), interdisciplinary collaboration, and standardization and training emerge as key priorities to ensure the responsible and sustainable deployment of SASRs in healthcare settings, reinforcing the importance of rigorous methodologies and ethical safeguards.

1. Introduction

Among the most extraordinary and disruptive technological innovations of recent years, social robotics stands out for its potential societal impact. Social robots represent a field of research of primary importance from both a technological and clinical perspective [1].

The integration of social robotics in the fields of assistance and rehabilitation is developing in various directions, including the following [1]:

• Investing in the development of social robots for rehabilitation and assistance support, such as devices designed to assist elderly individuals, promoting motor, cognitive, and emotional recovery [2].
• Creating social robots as cultural mediators and assistants in communication and therapy, with a particular focus on autism spectrum disorders and other conditions that require support in socialization [3,4].
• Addressing the theme of empathy in social robots and seeking solutions to make interactions with these devices more natural (for example, using pet-like robots) and effective from a human perspective [5].

Building on the experiences of collaborative robotics, social robots are opening new perspectives in the field of rehabilitation, assistance, and caregiving for the most vulnerable individuals, with challenges ranging from neuromotor disabilities to communication and psychological issues. The COVID-19 pandemic emergency acted as a catalyst for the sector, accelerating research and experimentation of social robots as tools to ensure continuity of care and communication support while respecting social distancing [6]. Consequently, the current debate revolves around two opposing views: on one hand, the futuristic perspective of a potential replacement of human care, and on the other, a more realistic and ethically acceptable approach, seeing social robots as mediators and facilitators of human interactions in rehabilitative, caregiving, and assistance contexts.

The evolution of social robotics has paved the way for the integration of broader functionalities, transitioning from devices primarily aimed at rehabilitation and emotional assistance to technologies that also address patients’ practical needs. Initially, social robots focused on social interaction and improving psychological well-being. However, recent advancements in research have led to the development of robots capable of providing physical support and assisting with daily tasks. This expansion allows them to offer both emotional and practical benefits in healthcare settings, significantly improving the overall quality of care.

Social robots, primarily designed for engaging in social interactions [7,8], have evolved to perform a broader range of tasks, including assistance with physical support and daily activities [9]. This dual capability allows them to offer both emotional and practical benefits in healthcare settings. In addition to providing companionship and fostering social engagement, these robots contribute to the physical well-being of individuals by assisting with mobility, helping with medication reminders, and supporting other essential care duties.

These robots serve a dual purpose: addressing the emotional well-being of individuals through companionship and social interaction, while also fulfilling key practical roles in caregiving. Their ability to integrate both social and assistive functions makes them particularly valuable in healthcare environments, where both emotional support and physical care are crucial for improving the quality of life and overall well-being of patients. By combining emotional support with practical assistance, social robots represent a promising solution to address the multifaceted needs of patients, particularly in settings where staffing limitations or resource constraints may pose challenges.

We can refer to these robots as social and assistive support robots (SASRs). This term highlights robots designed not only to provide social engagement and emotional support but also to assist with physical tasks and daily care activities. It emphasizes the dual role these robots play in healthcare and caregiving environments, combining emotional well-being and functional support within a single system. SASRs can include one or both of these aspects, offering a holistic approach to improving the quality of care in various settings. Whether providing companionship and reducing isolation or assisting with mobility and medication reminders, SASRs play a crucial role in enhancing patient care.

The increasing integration of digital health in physiotherapy and the growing advancements in social robots, particularly in their ability to interact socially through artificial intelligence, are two key trends shaping the future of rehabilitation and assistance [10]. As these fields converge, it is crucial to explore how social robots can be utilized in rehabilitation and assistance. A proposed study on the actors of the health domain [10] assessed the consensus among professionals in the field regarding the introduction of social robots. Two groups of professionals, one in training and the other in practice, were surveyed to understand their views on the potential role of social robots in enhancing rehabilitation and assistance.

The results indicated that professionals view social robots as valuable, complementary tools rather than replacements for human workers. They believe social robots can improve working capacity, assist in performance monitoring, and facilitate integration into clinical practices. Furthermore, there is a consensus that physiotherapists will play a central role in managing and overseeing the use of these devices. The study highlights the need for stakeholders to embrace these technologies and invest in training and initiatives to build consensus, especially as the population ages and the demand for care increases.

As scholars and practitioners in the fields of rehabilitation, assistance, and caregiving, it is crucial to address these broad, interdisciplinary questions. They encompass not only mechatronics, neuroscience, and AI, but also bioengineering, ethics, economics, and regulatory policies. Understanding the evolution of social robotics, from its collaborative robotics roots to its current application in healthcare, is essential for developing a comprehensive conceptual framework. This framework will guide the responsible development and integration of these technologies, ensuring that their benefits are maximized while their risks are carefully managed.

Assessing how the field of social and assistive robotics is stabilizing and evolving is crucial in today’s rapidly advancing technological landscape, making an analysis of systematic reviews strategically important.

In this context, the purpose of this study is to develop a narrative overview of systematic reviews to analyze the evolution and current state of integration of the SARSs in the health domain.

The specific objectives of the study are as follows:

• Analyze Overall Bibliometric Trends:
  Provide a comprehensive bibliometric analysis of research output in the field of social and assistance robotics, focusing on trends, key publications, and developments over time.

• Identify Established Themes and Categories:
  Map out the key areas of focus within the systematic reviews, such as technological innovations, application domains (e.g., rehabilitation, assistance, and caregiving), and evaluation methodologies.

• Examine Opportunities and limitations:
  Investigate the potential benefits and limitations of integrating robots into healthcare and caregiving settings. This includes exploring advancements in artificial intelligence and workflow efficiency, as well as identifying barriers related to infrastructure, regulatory frameworks, and professional training.

By addressing these objectives, this overview aims to develop a robust conceptual framework that captures the dynamic evolution of social and assistance robotics and highlights the opportunities and challenges ahead.

2. Approach to Study Selection

The narrative synthesis of the overview of systematic reviews follows a structured methodology tailored for narrative reviews based on the ANDJ narrative cehcklist.

The narrative review was conducted using specific, targeted searches across PubMed and Scopus, focusing on peer-reviewed journal articles related to the medical applications of socially assistive robots. To ensure methodological rigor, conference proceedings, dissertations, and non-English documents were excluded. The selection criteria focused on studies with applications in the health domain, including—but not limited to—clinical implementation, therapeutic impact, and patient interactions with these technologies in healthcare settings. By restricting the search to medical applications, the review aimed to provide a comprehensive and reliable synthesis of the current evidence on the role of socially assistive robots in the health domain.

In addition, a qualification framework was applied, based on predefined quality criteria, to assess the inclusion of studies in the review. See Algorithm 1 for the review process used in this overview.

Algorithm 1: Framework for Review Selection

This search strategy was designed to ensure both comprehensiveness and precision in retrieving relevant studies. Targeted searches were conducted across PubMed and Scopus, focusing on peer-reviewed journal articles related to the medical applications of socially assistive robots. This approach ensured the inclusion of high-quality, rigorously vetted studies. The search query was carefully structured using the keywords listed in Table 1, which were systematically combined with Boolean operators (AND, OR) to refine and optimize the search strategy. This allowed for a targeted yet extensive retrieval of the relevant literature. The selected keywords were applied to both the titles and abstracts of full article searches. The selection criteria specifically targeted studies with applications in the health domain, including—but not limited to—clinical implementation, therapeutic impact, and patient interactions with these technologies in healthcare settings. Table 1. Keywords used in the search. Keyword Justification “Social Robots” A broad term for robots designed to engage in social interactions with humans, including communication, emotional expression, and companionship. They are used in healthcare, education, and customer service. “Assistance Robots” Robots designed to aid humans in various tasks, including personal assistance, healthcare support, household help, and workplace automation. They can assist individuals with disabilities, elderly people, or workers in logistics and service industries. “Humanoid Robots” Robots with human-like appearance and behavior, designed to facilitate natural interaction through gestures, speech, and facial expressions. They are frequently used in research on human–robot interactions. “Assistive Robotics” Robots that support individuals with disabilities, elderly populations, or patients in rehabilitation. These robots provide physical or cognitive assistance and improve quality of life. “Human–Robot Interaction” The study of how humans and robots communicate, collaborate, and build relationships. A crucial field in assessing usability, acceptance, and ethical concerns of robotic systems. “Companion Robots” Robots designed to provide emotional and social support, often used to alleviate loneliness in elderly individuals or offer companionship in therapeutic settings. “Healthcare Robotics” Application of robots in medical environments, including patient care, surgical assistance, rehabilitation, and mental health support. It improves healthcare efficiency and accessibility. “Therapeutic Robots” Robots used in psychological and medical therapy, such as reducing anxiety in children with autism or improving cognitive engagement in dementia patients. “Educational Robotics” The use of robots as teaching tools in classrooms or for personalized learning, especially in STEM education and early childhood development. “Trust in Robots” The study of human confidence in robotic systems, influenced by reliability, transparency, and social behaviors of robots. It is critical for integration into daily life. This methodology was carefully designed to strike a balance between specificity and breadth. On one hand, it enabled the precise identification of studies directly related to our research focus, reducing the risk of including irrelevant literature. On the other hand, it broadened the scope enough to capture relevant discussions that might not be immediately evident from a title-based search alone. This strategic approach allowed us to construct a high-quality literature base, ensuring a well-founded and thorough analysis of the role of socially assistive robots in healthcare. Perform targeted searches on PubMed and Scopus using the query from Step 1. Filter studies published in peer-reviewed journals that focus specifically on the social and assistive robotics field. Evaluate each study based on the following criteria: ○ N1: Clarity of the study’s rationale in the introduction ○ N2: Appropriateness of the study design ○ N3: Clear description of methodology ○ N4: Clarity of results presentation ○ N5: Justification of conclusions based on results ○ N6: Disclosure of conflicts of interest Assign a score from 1 (lowest) to 5 (highest) for parameters N1–N5. Assign a binary assessment (Yes/No) for N6 regarding conflict-of-interest disclosure. Select studies meeting the following criteria: ○ N6 must be “Yes” ○ N1–N5 must each score greater than 3. Include the preselected studies in the overview.

2.1. Assessment Process

Each study included in the analysis was reviewed by two independent assessors (A.L. and D.G.), who evaluated the studies based on their focus on the integration of SASRs in medical applications. The predefined assessment criteria included Clarity of Rationale, Study Design Appropriateness, methodological rigor, Result Presentation, Justification of Conclusions, and disclosure of conflicts of interest. Each criterion was rated on a predefined scale to provide a quantitative measure of the quality and relevance of the analyzed studies. The assessors independently reviewed the studies and assigned scores to each parameter, ensuring that evaluations were conducted based on standardized guidelines. This dual-assessment approach was designed to enhance the reliability of the review by incorporating different perspectives and reducing the risk of individual bias influencing the evaluation process.

In cases where discrepancies arose in scores or study inclusion decisions, a third assessor (selected on a rotating basis from A.P or A.I.) was involved to adjudicate the final decision. The role of the third assessor was crucial in resolving conflicts and ensuring that decisions were fair and well founded. This additional level of scrutiny helped balance differing opinions and reinforced the integrity of the review process.

A multi-assessor approach was implemented to minimize bias and ensure a rigorous and balanced evaluation of the literature. By integrating multiple viewpoints and providing a structured mechanism for resolving disagreements, the review aimed to offer a comprehensive and objective assessment of SASRs in healthcare settings.

2.2. Managing Bias in the Review

To uphold the objectivity and methodological rigor of the review, several strategies were implemented to manage and minimize bias throughout the assessment process:

• Diverse Assessors: Each study was reviewed by two assessors with different academic backgrounds and expertise in both assistive robotics and healthcare. This diversity ensured a broad range of perspectives and minimized the risk of individual biases influencing the evaluation.
• Clear Assessment Criteria: The studies were analyzed using predefined criteria, including Clarity of Rationale, Study Design Appropriateness, methodological rigor, Result Presentation, Justification of Conclusions, and disclosure of conflicts of interest. Furthermore, data were presented based on a standardized checklist, reducing the risk of subjective interpretation.
• Scoring System: Each parameter was rated on a scale from 1 to 5, while the disclosure of conflicts of interest was assessed using a binary evaluation (Yes/No). This quantitative approach ensured consistent evaluations across studies and provided a transparent mechanism for comparing study quality.
• Independent Review: The primary assessors reviewed the studies independently, assigning scores without prior discussion. This independence ensured that individual judgments were based solely on the study’s merit and predefined criteria, minimizing groupthink or shared biases.
• Dispute Resolution: In cases where the two assessors disagreed on scores or study inclusion, a third assessor was consulted to provide an impartial judgment. This adjudication helped resolve conflicts fairly and ensured balanced decision making.
• Structured Mechanism for Disagreements: The process for resolving disagreements was formalized and structured. The third assessor reviewed the initial evaluations and provided a reasoned judgment to reconcile differences. This structured approach ensured that conflicts were systematically addressed and that final decisions were based on a comprehensive evaluation.
• Transparency: The use of a standardized checklist for data presentation and a clear scoring system enhanced transparency in the assessment process. By documenting the criteria and scoring rationale, the review process became more traceable and reproducible, reducing the potential for undisclosed biases.

By incorporating these strategies, this review aimed to provide a thorough and balanced evaluation of the literature. The multi-assessor approach, combined with structured criteria and a formal dispute resolution mechanism, was designed to minimize bias and enhance the reliability and objectivity of the review process.

2.3. Selected Studies

Based on an initial selection of systematic reviews in the medical field, pre-screened according to the preliminary requirements (including the medical field only, excluding conference proceedings, ensuring peer-reviewed articles, and restricting to English-language publications), a total of 98 studies were considered. After thoroughly examining the robust and non-marginal focus on SASRs within the medical context, 48 systematic review studies were selected for a more in-depth evaluation. To ensure methodological rigor and consistency, the framework outlined in Algorithm 1 was applied, which further narrowed down the selection. As a result of this rigorous filtering process, only 17 studies remained for final inclusion in the review. This method allowed for a focused and reliable synthesis of the current evidence on the role of SASRs in the medical field.

3. Results

The results are structured into three distinct sections, each addressing a specific aspect of the analysis: Section 1 focuses on tracing the evolution of bibliometric trends within these fields, providing a comprehensive overview of how research activity and focus areas have developed over time. Section 2 delves into a detailed categorization of the studies, offering a systematic organization of the literature and presenting a unifying message that emerges from the analysis, shedding light on shared themes or conclusions. Lastly, Section 3 explores the opportunities and limitations identified through the analysis, discussing their implications and potential directions for future research and practice in the field.

3.1. Trends

Focusing on the first two identified keywords in Table 1 to explore articles discussing both the emotional benefits of robots, such as companionship and reducing isolation, as well as their functional benefits, such as aiding with daily tasks, a bibliometric analysis was conducted on PubMed based on title and abstract. The search reveals several notable trends and insights:

• Historical and Temporal Trends:
  The earliest studies in this domain date back to 2006, and since then, a total of 400 studies have been published (see Figure 1). Notably, in the last 10 years, 379 studies have been produced, accounting for approximately 95% of the total publications in this field. This remarkable surge indicates a growing research interest and rapid development in social and assistance robotics. Moreover, in the past five years—especially following the onset of the COVID-19 pandemic—305 studies have been published, representing around 76% of the overall output (see Figure 1). This recent spike suggests that the current global challenges and advancements in digital health have significantly accelerated research activity.
  

  Figure 1. Temporal trends in publications on social and assistance robotics (2006–Present).

• Review Studies:
  Among the 400 publications (Figure 2), there are the following:
  

  Figure 2. Breakdown of review studies in SASR.
  A total of 18 reviews, which account for approximately 4.5% of the total.
  A total of 12 systematic reviews and/or methanalysys, representing about 3% of total.
  Collectively, these 30 review-type studies constitute around 8% of the overall publication output (see Figure 2). Review-type studies began to emerge in 2017, and in the last five years, 22 out of the total 30 review publications have been developed, which accounts for roughly 73% of the review literature. This concentration of review studies in recent years reflects a maturing field where researchers are beginning to synthesize the existing knowledge and critically evaluate emerging trends and methodologies.

• Comparative Scope within Robotics Literature:
  When broadening the search using the keyword (robot[Title/Abstract]), a total of 36,977 results were retrieved. This extensive number illustrates that while the broader field of robotics is vast, studies specifically focused on social and assistance robotics represent only about 1.1% of the overall literature. This comparatively small percentage highlights that social and assistance robotics, despite their potential, remain a relatively niche and emerging area within the larger robotics research landscape.

Overall, these findings underscore that the field of social and assistance robotics is rapidly evolving. The significant increase in publications over the last decade—and especially in the past five years—points to an accelerating interest in exploring the integration of these technologies into digital health and cytopathology. This trend emphasizes a promising research opportunity to further develop conceptual designs, innovative applications, and comprehensive models for social and assistance robots. Such advancements are crucial for transforming healthcare practices and enhancing workflow efficiency in digital pathology, while also addressing the unique challenges and opportunities presented by this emerging field.

3.2. Themes and Categorization

Seventeen systematic reviews were selected [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. An analytical summary of each study, organized by objectives, methods, results, and conclusions, is provided in Supplementary Materials. The synthesis process highlighted key aspects for the overview. Table 2 presents a summary of important studies on SASRs, including essential details for each study. The reference column provides citations, the study focus column describes the main research area (e.g., emotional support, therapy for disabilities, and cognitive enhancement), and the objective column outlines the research goals (e.g., assessing robot effectiveness in healthcare or evaluating patient satisfaction). The population column identifies the target group (e.g., elderly people, children with autism, and individuals with dementia), while the technological aspects column describes robot features (e.g., anthropomorphic design and AI capabilities). The main effects column summarizes observed results, such as improvements in emotional well-being or cognitive function. Finally, the limitations column highlights study constraints (e.g., small sample sizes and the need for more rigorous trials). Table 3 offers a brief summary of studies on SASRs in healthcare, focusing on their contributions to improving patient care and treatment outcomes.

Table 2. Overview of key studies on SASRs.

Reference	Study Focus	Objective	Population	Technological Aspects	Main Effects	Limitations in the Studies of the Systematic Review
[11]	SASRs for depression and loneliness in the elderly	Assess the effectiveness of SASRs in reducing depression and loneliness	Elderly individuals in assisted living facilities	SASRs with physical embodiment, voice interaction, and motion-based engagement	Significant reduction in depression and loneliness, especially in group settings	Small sample size; short follow-up period
[12]	Robotic interventions for sleep management in adults	Evaluate whether robots improve sleep quality	Adults with sleep disorders	Companion robots with tactile interaction and programmed routines	No significant improvement over traditional sleep aids	No substantial benefit compared to usual care
[13]	Empathy in human–robot interaction	Investigate how SASRs can foster empathy in human users	Healthy adults interacting with AI-based robots	Emotion recognition, multimodal interaction (voice, gestures, and expressions), and adaptive response systems	Defined empathy framework for SASRs	Limited generalizability beyond controlled settings
[14]	SASRs for autism therapy and care	Examine how robots assist in autism therapy	Children with Autism Spectrum Disorder (ASD)	Humanoid and non-humanoid models, speech synthesis, and interactive games	Moderate improvement in social interactions	High variability in individual responses
[15]	Reducing anxiety in children in healthcare settings	Explore SASRs’ role in alleviating anxiety in pediatric healthcare	Children undergoing medical procedures	Interactive robots with speech, movement, and music	Lower anxiety levels during medical interventions	Short-term study; long-term effects unknown
[16]	SASRs in dementia care	Examine the impact of SASRs on emotional and cognitive well-being in dementia patients	Dementia patients in long-term care facilities	Pet-like robots (e.g., PARO) with life-like tactile responses and emotional AI	Improved behavioral and emotional outcomes; limited cognitive benefits	Inconsistent cognitive improvements; ethical concerns regarding deception
[17]	Trust in AI-based SASRs	Analyze design factors that enhance trust in SASRs	Studies on human–robot trust	Facial anthropomorphism, dynamic expressions, real-time gaze adaptation	Identified key facial features enhancing trust	Theoretical framework; lacks empirical validation
[18]	Effects of non-facilitated meaningful activities for dementia patients	Evaluate the effectiveness of non-facilitated robotic activities in dementia care	Dementia patients in residential care	Animal-like robots (e.g., PARO), life-like dolls with programmed behavioral responses	Positive effects on agitation, well-being, and sleep	Heterogeneity in study designs; limited long-term data
[19]	Cognitive support for the elderly with early-stage dementia	Investigate how robots enhance cognitive function in early dementia	Early-stage dementia patients	Assistive robots for memory and task completion	Modest cognitive benefits observed	Short study duration; no control group
[20]	SASRs to support children’s well-being under medical treatment	Assess how SASRs reduce anxiety in hospitalized children	Pediatric patients undergoing medical procedures	Companion robots with interactive storytelling, play-based interactions	Reduced stress and improved coping mechanisms	Variability in engagement levels across children
[21]	SASRs for depression in older adults	Investigate how SASRs aid in depression management	Older adults diagnosed with depression	AI-driven emotional interaction and adaptive dialog systems	Moderate reduction in depressive symptoms	No long-term data on sustained benefits
[22]	The effectiveness of SASRs for older adults	Summarize evidence on the efficacy of SASRs for elderly care	Elderly individuals in residential care	Various types of SASRs, including companion and therapeutic robots	Positive impact on emotional well-being, engagement, and quality of life	Variability in the effectiveness depending on robot design and implementation
[23]	SASRs in adult psychiatry	Overview of the use, effects, and acceptability of SASRs in adult psychiatry	Adult psychiatric patients	Social robots equipped with AI-driven interaction, speech recognition, and emotional response capabilities, used in psychiatric therapy.	Symptom reduction, functional improvements, high patient acceptance and enjoyment.	Small sample sizes, limited generalizability, need for larger randomized controlled trials (RCTs).
[24]	AI SAR for cognitive function in older adults	Examine the effect of AI SAR on cognitive function in older adults	Adults aged ≥65 years	AI-powered socially assistive robots (SAR) with adaptive learning algorithms, sensor-based interaction, and anthropomorphic design for cognitive engagement.	Improved cognitive function, with anthropomorphic SAR proving more effective.	Ethical concerns, challenges related to low digital literacy among older adults.
[25]	Non-pharmacological pain management for dementia patients	Synthesize evidence on non-pharmacological pain interventions for dementia	People with dementia	Personal assistive robots with multimodal sensory inputs (voice, touch, motion tracking) integrated into non-pharmacological pain management strategies for dementia patients.	Pain reduction through non-pharmacological interventions, including assistive robots.	Variable methodological quality, studies focused only on mild to moderate pain.
[26]	SASRs in dementia care	Measure the effects of socially assistive robots in dementia care	Older adults with dementia	Pet-type socially assistive robots with AI-driven behavior simulation, tactile feedback, and emotional interaction capabilities for dementia care.	Significant reduction in agitation and depression, effect dependent on exposure duration.	No significant effect on quality of life, need for further research on long-term benefits.
[27]	Rehabilitation and Assistive Robots Assessment	To classify outcome measures for assessing rehabilitation or assistive robots.	Users of rehabilitation/assistive robots	Rehabilitation and assistive robots with motion tracking, haptic feedback, and AI-based user adaptation, evaluated with PYTHEIA and other measurement tools.	Identification of validated tools for subjective assessment of assistive technologies.	Lack of standardized and validated outcome measures to compare device performance.

Table 3. Study overview: brief description and contribution to the health domain.

Study	Brief Description	Contribution to the Health Domain
[11]	This study is a systematic review and meta-analysis that explores the impact of SASRs on depression and loneliness among older residents in long-term care facilities. The research synthesizes randomized controlled trials to assess the effectiveness of SASRs as non-pharmacological interventions for improving mental health in elderly populations.	SASRs offer a promising alternative to traditional interventions for reducing depression and loneliness in elderly care settings. By encouraging social engagement and interactions, these robots contribute to the psychosocial well-being of older adults, suggesting their potential for widespread use in long-term care facilities.
[12]	This systematic review and network meta-analysis compare the effects of robots, plush toys, and usual treatments on sleep in adults, particularly older adults with or without dementia living in nursing homes. Despite mixed results, the study evaluates sleep quality and total sleep time as common sleep measures in trials involving robotic and non-robotic interventions.	While the analysis found no significant improvement in sleep quality due to robot interventions, it suggests that sleep-specific robots may offer benefits. The findings highlight the need for targeted robotic designs aimed at improving sleep quality and the importance of excluding participants with adequate sleep to avoid bias.
[13]	This review examines the concept of empathy in human–robot interactions, focusing on the ability of robots to recognize human emotions and respond appropriately to foster positive human perceptions. It also defines empathy in the context of SASRs and suggests a conceptual framework for designing robots that can empathize with their users.	Empathy is a critical design factor for robots intended for healthcare and daily interactions. The study highlights how robots’ ability to understand and respond to emotional cues could improve human–robot relationships, which is essential for their integration in therapeutic and caregiving environments.
[14]	This systematic review evaluates randomized controlled trials assessing the use of SASRs in autism therapy. The study examines various robots used in therapy for individuals with autism, with a focus on how robots serve as engagement tools and their ability to improve various behavioral and cognitive outcomes.	The research shows that SASRs have the potential to improve engagement and behavior in people with autism, particularly in therapeutic settings. While the robots are still in the early stages of development for therapeutic use, the study underlines their promise in enhancing interaction and providing emotional support for individuals with autism.
[15]	This review assesses the role of SASRs in reducing anxiety and distress in children visiting hospitals or clinical environments. The study investigates different types of robots and their effectiveness in managing children’s emotional responses during medical visits.	The evidence supports the potential of SASRs to reduce anxiety and distress in children during hospital visits. These robots engage children through interaction, music, and movement, showing promise in improving emotional well-being in clinical settings. However, more extensive studies are needed to strengthen the evidence and refine their usage.
[16]	This systematic review investigates the effects of SASR interventions on individuals with dementia, focusing on behavioral, emotional, and functional outcomes. The study analyzes various robot types, including pet robots, to evaluate their impact on dementia patients’ well-being.	SASRs offer non-pharmacological interventions for people with dementia, promoting engagement and emotional support. However, the results are mixed, and the study calls for further research on the optimal use of robots based on the severity of dementia and the specific characteristics of the intervention.
[17]	This review focuses on facial anthropomorphic trustworthiness in SASRs, specifically examining static and dynamic facial features and emotional expressions that could improve trust in robots. The study draws from the human facial perception and robot design literature.	The research highlights the importance of facial features in designing SASRs that are trustworthy and relatable. Trust is essential for the acceptance and effectiveness of robots in healthcare, as users need to feel comfortable interacting with robots, especially in sensitive environments like healthcare.
[18]	This review evaluates the effectiveness of non-facilitated meaningful activities for people with dementia in long-term care facilities, including the use of SASRs, toys, and music. The study investigates the psychological and physiological impacts of such activities on dementia patients.	Non-facilitated activities involving SASRs, such as the PARO robot, have been shown to improve engagement, reduce agitation, and enhance emotional well-being in people with dementia. While the evidence is promising, more robust studies are needed to confirm the long-term benefits of such interventions in dementia care.
[19]	This systematic review synthesizes evidence from randomized controlled trials (RCTs) on psychosocial interventions by SASRs. It focuses on the effects of SASR interventions on health and well-being outcomes, covering a range of health domains where SASRs have been tested.	While controlled research on SASRs is still in its early stages, the study emphasizes the need for large-scale RCTs with sophisticated methodologies to increase confidence in the efficacy of SASRs. It highlights the potential for robots to improve healthcare access and outcomes but underscores the importance of further studies to establish their clinical effectiveness.
[20]	This systematic review examines the use of socially assistive robots (SARs) to support children’s well-being during hospitalization. The study reviews ten publications on SARs used for emotional support, distraction during medical procedures, and overall well-being improvement.	The review suggests that SARs can have a positive effect on children’s well-being by reducing stress, pain, and anxiety during medical treatments. It highlights the potential benefits of integrating SARs into hospital routines, though further research is necessary to refine their application and improve outcomes.
[21]	This systematic review investigates the effectiveness of SASR interventions for depression in older adults. It assesses the impact of companion, communication, and health-monitoring robots in reducing depressive symptoms in older adults.	The study suggests that SASRs have potential in alleviating depressive symptoms among older adults, particularly through companionship and communication. However, the evidence is not yet strong enough to make definitive clinical recommendations, and more research is needed to assess long-term effects and effectiveness.
[22]	This systematic review and meta-analysis examines the effectiveness of SASRs on older adults’ psychological, physiological, and quality-of-life outcomes from randomized controlled trials.	The review indicates that SASRs may improve emotional support, reduce agitation, anxiety, and loneliness, and enhance the overall quality of life in older adults. However, the lack of high-quality studies calls for further RCTs to better understand the impact of SASRs on older adults’ health and well-being.
[23]	This scoping review explores the use of SASRs in adult psychiatry, focusing on their impact on mental health conditions like schizophrenia, autism spectrum disorder, and intellectual disability. The study suggests that SASRs can reduce symptoms, improve functioning, and provide insights into these conditions, with positive user feedback on their acceptance.	This study highlights the potential of SASRs to improve mental healthcare, particularly in adult psychiatry, where their use is currently limited. It suggests that further research with larger, randomized trials could establish more concrete evidence of their benefits.
[24]	This meta-analysis examines the effect of AI socially assistive robots (SAR) on the cognitive function of older adults. Nine studies were included, and the results suggest that AI SAR in anthropomorphic form can improve cognitive function.	The study shows that AI SAR can effectively support cognitive function in older adults, offering a non-pharmacological intervention with the potential to enhance the quality of care and reduce caregiver burden, although challenges in implementation remain.
[25]	This systematic review investigates non-pharmacological interventions for pain management in people living with dementia. It highlights interventions like ear acupressure, music therapy, and personal assistive robots as effective options for managing pain.	The findings suggest that non-pharmacological interventions, including SASRs, can play a role in pain management for people with dementia, offering safer alternatives to pharmacological treatments. However, more rigorous studies are needed for validation.
[26]	This study evaluates the effect of exposure duration to socially assistive robots on older adults with dementia, focusing on outcomes like agitation, depression, and quality of life. It found that pet-type robots helped reduce agitation and depression.	The study supports the use of pet-type robots in dementia care, especially in long-term care facilities, showing benefits in reducing agitation and depression. The findings call for further research to develop comprehensive intervention plans for their use.
[27]	This systematic review identifies and classifies outcome measures used to assess rehabilitation or assistive robot devices. The study finds a lack of standardized measures, which complicates comparisons across studies.	The research highlights the need for standardized, validated scales for evaluating rehabilitation or assistive robots, improving the ability to assess their effectiveness and user satisfaction. The identification of PYTHEIA offers a potential solution for bridging this gap.

Table 4 presents a selection of studies on SASRs, categorizing them into clusters based on their design and intended application. Each study is grouped into one of the following clusters: emotional and social support, therapeutic robots for special needs, and cognitive support. The table also provides a justification for the classification of each study, offering insight into the focus and objectives of the robot interventions discussed.

Table 4. Overview of key studies on social and assistive robots: cluster and justification.

Reference	Cluster	Justification of Cluster
[11]	Emotional and Social Support	The study focuses on SASRs designed to offer companionship and emotional relief to elderly individuals, addressing issues like loneliness and depression.
[16]	Emotional and Social Support	This study explores robots that engage patients emotionally, providing companionship and supporting cognitive functions, which help alleviate emotional distress in dementia patients.
[18]	Emotional and Social Support	The review examines the impact of non-facilitated interactions by SASRs on dementia patients, focusing on emotional support through unmediated social engagement.
[13]	Emotional and Social Support	This study focuses on creating robots capable of empathetic interactions, helping establish emotional bonds between robots and users, which enhances social and emotional support.
[17]	Emotional and Social Support	This study explores how SASRs can build trust through facial and emotional expressions, strengthening the emotional bonds between AI and human users.
[22]	Emotional and Social Support	This study consolidates evidence on the emotional benefits of SASRs for elderly individuals, emphasizing their role in enhancing emotional well-being and social interaction.
[21]	Emotional and Social Support	This study focuses on the use of robots to provide emotional support to older adults suffering from depression, aiming to improve their quality of life through social interaction.
[20]	Therapeutic Robots for Special Needs	This study discusses robots that support children’s emotional well-being in healthcare settings, offering comfort and reducing anxiety during medical treatments.
[15]	Therapeutic Robots for Special Needs	The study investigates robots designed to alleviate anxiety and provide emotional comfort to children undergoing medical procedures, which serves as therapeutic support.
[12]	Therapeutic Robots for Special Needs	This study focuses on robots aimed at supporting sleep hygiene for adults, which addresses a therapeutic need, although with limited effectiveness compared to traditional methods.
[14]	Cognitive Support	The study evaluates robots used in autism therapy, focusing on structured and repetitive tasks designed to support social engagement and cognitive development in children with ASD.
[19]	Cognitive Support	This study investigates how robots can stimulate cognitive functions in elderly individuals with early-stage dementia, aiming to enhance memory and task completion capabilities.
[23]	Emotional and Social Support	The study explores the use of SASRs in adult psychiatry, highlighting their potential to reduce symptoms, improve functioning, and gain insights into mental health conditions like schizophrenia and autism spectrum disorder.
[24]	Emotional and Social Support	This study focuses on the use of AI SAR to enhance social interaction and cognitive function in older adults, emphasizing the promise of socially assistive robots as caregivers to improve older adults’ quality of life.
[25]	Therapeutic Robots for Special Needs	This study reviews non-pharmacological interventions for managing pain in people with dementia, including the use of assistive robots as a form of therapeutic support.
[26]	Emotional and Social Support	This study examines the effect of socially assistive robots on the mental state of older adults with dementia, specifically looking at the reduction in agitation and depression, supporting their role in emotional well-being.
[27]	Therapeutic Ro-bots for Special Needs	This study identifies and classifies outcome measures used to assess rehabilitation or assistive robots, focusing on the importance of standardized assessment tools to improve rehabilitation outcomes for users.

Based on the design and application of these robots, the studies can be grouped into three broad categories: emotional and social support, cognitive support, and therapeutic robots for special needs. However, While the dominant clusters for each study have been identified, it is important to note that some studies also touch upon adjacent clusters, indicating a broader scope of impact. For example, Yen et al. [11], primarily focused on emotional and social support, also contribute to cognitive support, as emotional relief in elderly individuals can enhance cognitive engagement. Similarly, Park and Whang [13] mainly address emotional and social support, but their exploration of empathy in human–robot interactions also touches on aspects of cognitive support, as fostering emotional bonds can improve cognitive performance. Salimi et al. [14] focus on cognitive support for children with autism, yet its inclusion of social engagement through repetitive tasks also aligns with the emotional and social support cluster. These examples demonstrate how the impact of social and assistive robotics can span multiple domains, addressing a wide range of therapeutic and cognitive needs.

3.3. Emerging Opportunities and Limitations/Barriers

SASRs have the potential to improve emotional well-being and support various healthcare applications, such as reducing loneliness and depression in older adults, aiding sleep disorders, and assisting in therapy for autism and dementia. However, there are still barriers and limitations to overcome in design optimization and clinical integration. Further research is needed to validate their effectiveness and establish best practices. Table 5 below highlights the opportunities and limitations encountered with SASRs in healthcare.

Table 5. Emerging opportunities and limitations/barriers.

Study	Opportunities	Limitations/Barriers
[11]	- SASRs show great potential in reducing both depression and loneliness in older adults, which is critical for improving mental health in long-term care settings. Their use in group or individual activities could provide personalized care and emotional support.	- Further research is needed to determine which type of activities (group-based vs. individual-based) work best. The interaction dynamics and the robot’s role in group settings require more exploration.
[12]	- SASRs may offer a complementary or even alternative option to traditional sleep treatments, making them a valuable tool for managing sleep disorders. They could help individuals fall asleep or maintain a steady sleep pattern.	- While some robots showed positive effects on sleep quality, some results were inconsistent. There is a need for more targeted robot designs specifically optimized for sleep improvement, along with better clinical trials.
[13]	- Empathic robots, capable of understanding and responding to human emotions, could improve human–robot interactions, especially in therapeutic and healthcare contexts. These robots could offer emotional support and foster better relationships with patients or users.	- Designing robots that can express empathy across various settings and interactions remains difficult. The challenge lies in creating robots that can adapt to the nuances of human emotions in real-time interactions.
[14]	- Robots can play a role in enhancing therapy for individuals with autism by encouraging engagement and offering tailored interaction. They can serve as an interactive tool to stimulate cognitive and social development in autism therapy.	- Robots are not yet ready for full therapeutic use in autism treatment. Many robots are currently better suited for entertainment and basic interaction, lacking the complexity required for advanced therapeutic applications.
[15]	- Robots have been shown to effectively reduce anxiety and distress in children, particularly in medical environments like hospitals. These robots could be used as tools for emotional support during treatments or hospital stays, providing distraction and comfort.	- While there are positive findings, more robust and conclusive research is needed to solidify the role of robots in reducing anxiety and distress. There is a need for greater evidence supporting long-term impacts and the scalability of these interventions.
[16]	- SASRs hold promise for engaging individuals with dementia, promoting emotional well-being, and potentially improving cognitive function. Their use could lead to more personalized care and better interaction for those with memory impairments.	- Outcomes across studies are highly variable due to differences in participant characteristics, robot designs, and the specific interventions used. The lack of standardized measures and diverse patient populations complicates interpreting the results.
[17]	- Facial anthropomorphic trustworthiness can significantly enhance the design of SASRs by ensuring that facial expressions and features elicit trust and positive emotional responses. A robot’s face can influence how people interact with and accept the robot in various contexts.	- Despite the promising findings, more research is needed to identify which specific facial features and emotional expressions contribute most to establishing trust. There is still a lack of standardized design principles based on human facial perception.
[18]	- Non-facilitated activities like SASRs, plush toys, and family presence could improve the emotional and psychological well-being of people with dementia. These activities may help alleviate agitation, promote engagement, and improve sleep quality in patients.	- There is a lack of strong evidence supporting the physiological and psychological benefits of non-facilitated activities. The evidence is still inconclusive, and further research is necessary to understand how these interventions truly affect dementia patients.
[19]	- SASRs offer the opportunity to improve psychosocial health and provide more accessible healthcare solutions. These robots could act as supportive tools in managing health and promoting well-being, especially for those with chronic conditions or disabilities.	- The research base is still in its infancy, with limited randomized controlled trials (RCTs). The methodological quality of current studies is mixed, and more comprehensive trials are required to validate the efficacy of SASR interventions in diverse healthcare settings.
[20]	- SASRs can help mitigate stress and emotional distress for children in hospital settings, providing a calming presence and an engaging distraction during treatment. This could improve overall hospital experiences for children and support their emotional needs during stressful situations.	- Although results are promising, there is still limited data on long-term impacts. Further research is necessary to evaluate how robots can be effectively integrated into hospital routines and ensure they align with healthcare staff workflows.
[21]	- SASRs have shown potential in alleviating depression in older adults, improving their emotional well-being and providing personalized care. These interventions could enhance social interaction, reducing isolation and loneliness in long-term care environments.	- The evidence is not robust enough to make definitive clinical recommendations. The inconsistency in study findings and the limited number of high-quality trials mean that more rigorous research is needed to understand the full clinical impact.
[22]	- SASRs can improve well-being by addressing key issues like agitation, loneliness, and stress, and offering emotional support to older adults. Their use could also reduce dependency on medication and improve overall life quality for older individuals.	- Many studies suffer from high risks of bias, including issues with randomization and blinding. The need for better methodological designs and larger sample sizes is evident to draw firmer conclusions about the effectiveness of SASRs.
[23]	- SASRs can alleviate staffing shortages; enhance mental health treatment, particularly in psychiatric settings; and offer support for conditions like dementia and autism. They could improve patient engagement and care outcomes.	- The application of SASRs in psychiatry remains restricted, with limited research and low evidence. The sample sizes of studies are small, making it difficult to generalize findings.
[24]	- AI-driven Socially Assistive Robots (SARs) show promise in improving cognitive functions in older adults, providing an effective non-pharmacological alternative to caregiving, and reducing caregiver burden.	- Ethical concerns, digital literacy issues among older adults, and technological limitations complicate the widespread use of AI SARs. More research is needed to confirm long-term effectiveness and address implementation barriers.
[25]	- Non-pharmacological interventions like robots can offer a safer, drug-free approach to managing pain in people living with dementia, potentially improving their quality of life and reducing medication reliance.	- The evidence is mixed, with most studies showing limited sample sizes, and there is a lack of long-term validation. Further research is needed with larger, more diverse samples to confirm the effectiveness of these interventions.
[26]	- Pet-type robots have been shown to reduce agitation and depression in dementia patients, contributing to improved social interaction, emotional well-being, and overall care quality in long-term care settings.	- While pet robots have demonstrated positive effects on agitation and depression, the impact on quality of life was not significant. The length of exposure and frequency of sessions require further exploration.
[27]	- Assistive robots could play a critical role in rehabilitation, offering tailored, interactive devices for patients. They help engage patients in their recovery process, promoting independence and mobility.	- A lack of standardized assessment tools hinders the ability to compare results across studies. Most research uses non-validated instruments, making it challenging to draw consistent conclusions and establish best practices.

4. Discussion

The discussion is organized into five comprehensive sections: Section 1 reports synoptic diagrams to aligh the results with the discusison. Section 2 presents the key evidence derived from the overview of the systematic reviews, with a particular emphasis on detailing the added value they provide to the field. Section 3 focuses on the emerging recommendations that arise from the analysis, offering insights into the best practices and potential strategies for further development. Section 4 shifts the focus to recent primary studies, analyzing their findings and perspectives in light of the emerging recommendations to assess their alignment and relevance. Lastly, Section 5 provides a critical evaluation of the review, outlining its limitations and discussing areas for improvement in future research efforts.

4.1. Synoptic Diagrams

Figure 3 and Figure 4 present two synoptic diagrams that outline the rationale behind the design of the narrative review. These diagrams provide a structured visual representation of how the study was developed, showing the logical sequence of its different phases and how they interconnect.

Figure 3. Synoptic diagram of the study structure: from bibliometric trends to SARS applications in the health domain.

Figure 4. Logical progression from the narrative review findings to the cutting-edge research.

4.1.1. First Diagram (Figure 3): Linking Objectives to Analysis

The first diagram (Figure 3) illustrates how the study was structured based on its general objective and three specific objectives. The logical progression follows a top-down approach:

• Block 1: This block represents the bibliometric trends reported in Figure 1 and Figure 2 (Section 3.1). These trends were analyzed to provide an overview of the scientific production also in relation to robotics in the health domain in general.
• Block 2: This block corresponds to the identification of thematic areas, as presented in Table 2 and Table 3 (Section 3.2). This categorization allowed for the organization of the reviewed studies according to key themes, focus, and contribution, facilitating a structured analysis.
• Block 3: Building upon the thematic categorization, this block highlights the comparative side-by-side analysis of the studies. The classification was refined, with categorization as reported in Table 4 (Section 3.2), enabling a deeper understanding of the different ways SARSs are applied in the health domain.
• Block 4: This block synthesizes the opportunities and barriers/limitations identified in the reviewed studies, as reported in Table 5 (Section 3.3). These findings highlight both the potential benefits of SARSs’ applications in the health domain and the barriers/limitations.

This diagram provides a step-by-step visualization of the study’s methodological process, from bibliometric analysis to thematic categorization, comparative analysis, and the identification of emerging opportunities and challenges.

4.1.2. Second Diagram (Figure 4): Connecting Discussion to Findings

The second diagram (Figure 4) is logically connected to the first and illustrates how the study transitions from the findings of the literature review to the discussion. The sequential organization follows a structured approach:

• Block 5 identifies the emerging recommendations from the overviewed studies as reported in Table 6 (Section 4.3).
  Table 6. Emerging general recommendations.

  Source	Recommendation	Description	Importance
  Yen et al. [11], Park and Whang [13]	Empathy-driven Design	SASRs (SRs) should not only be functional but also designed to understand and engage with patients emotionally. This means incorporating advanced sensors, algorithms, and AI that can interpret human emotions through facial expressions, voice tone, body language, and even physiological cues. This empathetic capability is essential for creating meaningful interactions, especially for patients with cognitive impairments, dementia, or mental health concerns. By offering emotionally intelligent responses, SASRs can help mitigate feelings of loneliness, provide comfort, and strengthen the emotional bond between robot and patient, improving overall well-being.	Empathy in SASRs promotes emotional well-being by providing personalized and comforting interactions that address patients’ emotional needs. SASRs designed with empathy foster trust and a sense of companionship, making them effective for patients who require emotional or social support, particularly the elderly and those with special needs.
  Song and Luximon [17], Robinson et al. [19]	User-Centered Design	SRs should be tailored to meet the diverse needs of various patient populations. This includes considering the cognitive, emotional, and physical abilities of users, such as children with autism, elderly individuals with dementia, and people undergoing medical treatments that affect their mental or physical states. The design should allow for easy customization, ensuring that SASRs are adaptable to different users’ needs, preferences, and specific medical conditions. Personalization can involve adjusting the robot’s behavior, language, and communication methods, ensuring the robot is effective and comfortable to interact with.	Tailoring SASRs to specific patient populations ensures that the robots can provide relevant support that addresses individual needs, thus enhancing their effectiveness in clinical settings. A user-centered design approach makes SASRs more accessible, reducing the likelihood of frustration or disengagement and improving their therapeutic outcomes.
  Giansanti and Gulino [28], Monoscalco et al. [29]	Cybersecurity and Data Privacy	SRs are increasingly used in clinical settings, meaning they handle sensitive patient data such as personal information, health records, and behavioral data. To ensure the trust of both patients and healthcare providers, SASRs must be equipped with robust cybersecurity features. This involves adopting encryption techniques, secure data storage, and secure communication protocols to protect patient data from unauthorized access or cyber-attacks. In addition to protecting patient privacy, SASRs should be designed to comply with healthcare regulations such as GDPR or HIPAA. Ensuring cybersecurity also involves providing regular updates and monitoring the robots for potential vulnerabilities to safeguard against emerging threats.	Cybersecurity is critical for building trust and ensuring the adoption of SASRs in healthcare. Without strong data protection measures, the use of SASRs could lead to privacy violations, data breaches, and loss of patient confidence. Effective cybersecurity is essential not only for protecting sensitive information but also for ensuring that SASRs remain functional and reliable in healthcare environments.
  Lastrucci et al. [30]	Algorethics (AI Ethics)	As SASRs are powered by AI, their development must be guided by ethical principles to ensure they act in ways that are beneficial to patients. These robots need to make decisions or interact with patients in ways that promote fairness, transparency, and accountability. Algorethics involves ensuring that algorithms powering SASRs are free from biases and are designed with mechanisms for accountability so that the robots’ actions are understandable and can be questioned if necessary. Developers must also consider the long-term societal impacts of deploying AI-powered robots in sensitive healthcare settings, where mistakes or unethical decisions could have serious consequences.	Algorethics is essential for ensuring that SASRs act in a fair, transparent, and accountable manner. Incorporating ethical principles into AI systems minimizes the risk of bias, ensures that robots are working for the benefit of all patients, and builds trust in the technology. By addressing ethical concerns, the development of SASRs can align more closely with societal values and patient rights, fostering better acceptance and use of these technologies.
  Various studies [11,12,13,14,15,16,17,18,19,20,21,22]	Interdisciplinary Collaboration	The development of SASRs requires input from a diverse range of disciplines, including healthcare, engineering, psychology, ethics, and sociology. By bringing together experts from these fields, the design process can more comprehensively address the challenges and opportunities presented by SASRs. Engineers may focus on functionality and design, while healthcare providers bring practical insights into how SASRs can be used effectively in patient care. Psychologists can guide the development of empathetic interaction models, and ethicists can ensure the technology adheres to ethical standards. This holistic, interdisciplinary approach ensures that SASRs are well rounded and suitable for real-world applications.	Collaboration across different disciplines is vital for the success of SASRs. It ensures that SASRs are not just technologically advanced but also socially, ethically, and practically viable. Such collaboration maximizes the potential of SASRs to serve patients effectively while addressing potential issues in the robot’s design, use, and impact on human health and well-being.
  Maccioni et al. [31], Monoscalco et al. [29]	Standardization and Training	For SASRs to be integrated successfully into clinical settings, it is necessary to establish clear guidelines, standardized processes, and training programs. Healthcare professionals, including nurses, physiotherapists, and rehabilitation specialists, should be trained not only in the technical operation of SASRs but also in how to manage interactions with patients effectively. These training programs should cover areas such as ensuring patient comfort during interactions with robots, understanding their capabilities and limitations, and addressing cybersecurity risks. By providing healthcare workers with the tools and knowledge to use SASRs appropriately, we can ensure smoother integration and a more positive impact on patient care.	Standardization and training are critical to the successful integration of SASRs into healthcare. With clear guidelines and thorough training, healthcare providers can use SASRs more effectively and safely. These measures improve the overall user experience, enhance patient outcomes, and help ensure that SASRs are used in a way that aligns with clinical goals and patient needs.

• Connected to Block 5, Block 6 links to a comparison with the findings of recent Cutting Edge Research moving in the direction of the recommendations as reported in Table 7 (Section 4.4).
  Table 7. Sketch of the key findings in the cutting-edge recent studies and relationships with the general recommendations.

  Source	Recommendation	Contribution to Recommendation
  Figliano et al. [32]	Empathy-driven Design and Interdisciplinary Collaboration	Highlights the importance of empathy and collaborative approaches to SASR design. The study emphasizes the need for robots that are not only emotionally responsive but also adaptable to the cognitive and social development of diverse user groups, enhancing their ability to engage with and support individuals in various contexts.
  Baxter [33]	User-Centered Design and Empathy-driven Design	Examines child-robot interactions, particularly in educational environments. The findings underline how SASRs must be designed to accommodate children’s developmental needs, advocating for emotional engagement and interactive designs that encourage learning and social skill development.
  Paluch et al. [34]	Interdisciplinary Collaboration and Algorethics (AI Ethics)	Focuses on innovative robotic systems, emphasizing the need for collaboration between engineers, healthcare providers, and ethicists. Ethical considerations such as fairness, bias, and transparency are critical in developing socially aware and responsible SASRs, ensuring that their actions align with moral standards.
  Sørensen et al. [35]	User-Centered Design and Empathy-driven Design	Investigates the acceptance of SASRs by individuals with physical disabilities. The study stresses the importance of personalized and empathetic design in creating robots that meet the specific needs of disabled individuals, ensuring that robots can effectively support their users’ daily activities.
  Winslow et al. [36]	Cybersecurity and Data Privacy	Discusses the integration of advanced medical devices with SASRs, highlighting the critical need for robust cybersecurity measures to protect sensitive patient data. SASRs in healthcare must ensure data privacy and security to build trust with users and healthcare providers.
  Salem and Sumi [37]	Algorethics (AI Ethics)	Addresses ethical challenges related to deception in AI interactions, particularly in educational contexts. It emphasizes the necessity for SASRs to operate transparently, ensuring fairness and ethical standards in their behavior to prevent potential harm and maintain trust in AI-driven systems.
  Tozadore and Romero [38]	User-Centered Design and Empathy-driven Design	Examines SASR design in education, stressing the importance of considering diverse perspectives from teachers, students, and researchers. The research highlights that effective SASRs must be designed with empathy to engage and support learning, considering the emotional and cognitive needs of both students and educators.
  Elgarf et al. [39]	Empathy-driven Design and User-Centered Design	Explores how SASRs can foster creativity, particularly in children. It underscores the significance of creating emotionally engaging robots that can interact with children in ways that encourage creativity, imagination, and learning, while also adapting to their individual emotional and developmental needs.
  García-Martínez et al. [40]	User-Centered Design and Empathy-driven Design	Focuses on the concept of joint attention in human–robot interaction, showing that empathetic robot design can improve user perceptions. By aligning the robot’s actions with the user’s attention and emotional cues, the SASR can foster a more engaging and supportive interaction.
  Mizuho et al. [41]	User-Centered Design	Investigates how customers interact with SASRs in commercial environments, highlighting the need for adaptable design. The study emphasizes that SASRs should be designed to cater to the diverse needs and preferences of customers, providing personalized experiences and adapting to various contexts and interactions.
  Ahlin and Mann [42]	Empathy-driven Design and Interdisciplinary Collaboration	Examines resistance to SASRs in healthcare, emphasizing that empathy and interdisciplinary collaboration are vital to overcoming challenges in SASR adoption. By addressing emotional concerns and working across fields, SASRs can be better integrated into healthcare settings, fostering trust and acceptance among healthcare providers and patients.
  Lee et al. [43]	User-Centered Design and Empathy-driven Design	Analyzes the experiences of older adults using companion robots, focusing on emotional and cultural factors that influence their perceptions. The study suggests that empathetic design is essential for developing SASRs that align with the emotional and social needs of aging populations, promoting long-term use and satisfaction.
  Zhou and Dong [44]	User-Centered Design, Empathy-driven Design	Investigates how older adults respond to SASRs with offspring-like voices, showing that emotional responses are key to how these robots are perceived. This research supports the idea that SASRs should be personalized, particularly in terms of emotional cues and voice characteristics, to foster positive interactions with older adults.
  Rosenberg et al. [45]	Interdisciplinary Collaboration, Cybersecurity, and Data Privacy	Discusses a multimodal lifestyle program for older adults, where SASRs are used to support brain health. It highlights the need for collaboration across disciplines and the importance of integrating strong cybersecurity measures to ensure the safe and effective use of SASRs in healthcare, particularly when dealing with vulnerable populations like older adults.
  Fournier et al. [46]	User-Centered Design and Empathy-driven Design	Evaluates the impact of SASRs on autistic children, emphasizing the necessity for robots that are responsive to their emotional and communication needs. This research emphasizes the importance of designing SASRs that are tailored to the unique sensory and communication profiles of children with autism, improving interaction quality.
  Shankar et al. [47]	User-Centered Design and Empathy-driven Design	Provides insights into child-robot interactions, emphasizing the importance of adapting SASRs to children’s learning and communication styles. The study advocates for robots that can respond emotionally and provide educational support based on the child’s individual needs and preferences.
  Haresamudram et al. [48]	Empathy-driven Design and User-Centered Design	Explores the impact of anthropomorphic features in robots on user perception, demonstrating that empathy-driven design can significantly improve user acceptance and trust. SASRs that exhibit human-like features, such as voice and body movement, can foster more natural and positive interactions, especially for those requiring emotional support.
  Han et al. [49]	Interdisciplinary Collaboration, Cybersecurity, and Data Privacy	Reviews the use of SASRs in healthcare, highlighting the importance of interdisciplinary collaboration and robust cybersecurity measures to ensure safe and effective care. Emphasizing collaboration across fields and ensuring data privacy are essential for successful SASR implementation in sensitive healthcare settings.
  Rosero et al. [50]	Algorethics (AI Ethics)	Explores human perceptions of SASR deception, advocating for the adoption of ethical standards and transparency in robot interactions. The study stresses that SASRs should operate with honesty and fairness, ensuring that deceptive behaviors are avoided in all forms of human–robot interaction to maintain trust.
  Yashinski [51]	Cybersecurity and Data Privacy, User-Centered Design	Discusses cognitive monitoring in home settings, emphasizing the importance of secure data handling and the personalization of SASRs for in-home use. It highlights that in-home SASRs must be designed to meet individual needs while ensuring that sensitive data are protected, addressing both security and user-centered requirements.
  Maroto-Gómez et al. [52]	Algorethics (AI Ethics), Empathy-driven Design	Investigates bio-inspired decision making in SASRs, stressing the importance of transparency in decision-making processes. The study advocates for designing SASRs with ethical frameworks that ensure fairness and empathetic responses in their interactions, thus enhancing user trust and experience.
  Tan et al. [53]	Empathy-driven Design	Investigates how the LOVOT SASR enhances emotional well-being in older adults by fostering empathy-driven interactions that reduce loneliness and promote companionship.
  Lym et al. [54]	User-Centered Design	Focuses on designing family companion robots for children, tailoring the robot’s functions to provide emotional and practical support in home settings, especially for children with autism.
  Kok et al. [55]	User-Centered Design	Introduces a novel SASR model designed to sustain long-term engagement in home care services for aging populations, emphasizing the importance of adapting to the evolving needs of elderly users.
  Komariyah et al. [56]	User-Centered Design	Highlights how occupational therapists in Indonesia view SASRs for children with autism, stressing the need for SASRs to be designed with both therapeutic and emotional support in mind.

4.2. Highlight from the Overview

Based on the overview of the systematic reviews, a key added value is the recognition of multiple therapeutic applications of SASRs across different patient populations and healthcare settings. SASRs have been primarily designed to provide emotional and social support, cognitive assistance, and therapeutic interventions for specific needs, which can vary significantly depending on the condition or age group they are intended for. For instance, robots designed for emotional and social support often aim to alleviate loneliness, such as in elderly care or dementia, by offering companionship and emotional comfort. These robots can enhance patients’ psychological well-being and contribute to reducing isolation, as seen in the studies by Yen et al. [11], Hirt et al. [16], and Chen et al. [21]. Likewise, robots used for cognitive support aim to assist individuals with dementia or autism, where they help stimulate cognitive functions like memory, problem-solving, or even task completion, as demonstrated in the work of Salimi et al. [14] and Robinson et al. [19].

Another added value stems from the flexibility and potential overlap between categories of SASRs. While robots primarily focused on emotional support often provide cognitive stimulation as well, this cross-functionality enhances their effectiveness. For instance, robots used in dementia care not only provide emotional comfort but can also engage patients in cognitive tasks, such as remembering names or following simple commands, thus helping to maintain cognitive function. This suggests that the design of SASRs should be flexible enough to address multiple therapeutic goals simultaneously, improving their utility in healthcare settings, as evidenced by the work of Moerman et al. [20] and Pu et al. [22].

A third added value arises from the application of SASRs in diverse healthcare contexts. The reviews illustrate how SASRs have been successfully deployed in pediatric, geriatric, and dementia care settings. For example, in children’s hospitals, robots have been used to alleviate stress and anxiety during medical procedures, promoting relaxation and distraction, as seen in the studies by Moerman et al. [20] and Littler et al. [15]. Similarly, in geriatric care, robots have been used to support emotional health by alleviating depression, anxiety, and loneliness in older adults, enhancing their overall quality of life, as shown in the works by Chen et al. [21] and Robinson et al. [19]. These applications highlight the adaptability of robots across different populations, underscoring the potential for robots to meet the unique needs of various patient groups.

Furthermore, the reviews point to the need for more rigorous long-term research. Although the studies reviewed show promising short-term benefits of SASRs, there is a clear need for more longitudinal studies to assess the long-term efficacy and sustainability of these interventions. This would help determine the lasting impact of SASRs on emotional well-being, cognitive support, and overall health outcomes, as noted by Pu et al. [22] and Robinson et al. [19].

Finally, the need for integration into healthcare systems is a significant consideration moving forward. As SASRs become more advanced, understanding how they can be seamlessly integrated into clinical workflows is crucial. This involves addressing practical issues such as how to incorporate robots into everyday care routines and ensuring that healthcare providers are adequately trained in their use. Additionally, ethical considerations around privacy, consent, and robot–human interaction will need to be carefully managed to ensure these technologies are used responsibly and effectively, as discussed by Song and Luximon [17] and Støre et al. [12].

Overall, the systematic reviews reveal several added values from the use of SASRs in healthcare. These robots offer emotional, cognitive, and therapeutic support across diverse patient populations. However, to maximize their potential, future research must address their long-term impacts, integration into healthcare practices, and ethical implications, ensuring their place as effective, ethical tools in improving patient care.

4.3. Emerging Recommendations

Emerging recommendations for SASRs, a specialized subset of care robots, are derived from systematic reviews [11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]. Designed to address patients’ emotional, cognitive, and social needs, particularly among the elderly and those with special needs, SASRs present distinct opportunities and challenges.

A key recommendation is an empathy-driven design, ensuring SASRs can recognize and respond to human emotions. Studies by Yen et al. [11] and Park and Whang [13] highlight the importance of emotional responsiveness in fostering meaningful interactions, reducing loneliness [11], and supporting cognitive functions in dementia patients [19].

User-centered design is equally critical. SASRs must be adaptable to diverse users, from children with autism [14] to elderly patients with early-stage dementia [19] and individuals undergoing medical treatments [15]. Personalization enhances their therapeutic impact by addressing cognitive, emotional, and physical variations.

Cybersecurity is another fundamental aspect. As Giansanti and Gulino [28] emphasize, SASRs handle sensitive patient data, necessitating stringent security measures to prevent breaches and ensure safe operation. Monoscalco et al. [29] further highlight the need for cybersecurity training among healthcare professionals to mitigate risks in clinical environments.

Interdisciplinary collaboration strengthens SASR development. Insights from healthcare, engineering, psychology, and ethics help create socially and ethically responsible robots [17,19]. Ethical considerations, particularly “algorethics” (fairness, transparency, and accountability in AI systems), as discussed by Lastrucci et al. [30], are vital for ensuring trust and bias-free interactions.

Finally, gradual integration into clinical practice, supported by standardized guidelines and rigorous testing, is essential. Maccioni et al. [31] propose consensus conferences as a means to establish best practices and protocols for SASR deployment.

Overall, SASR development requires a holistic approach combining empathy-driven design, user-centered customization, cybersecurity, interdisciplinary collaboration, and structured clinical integration. These principles will ensure that SASRs effectively support patients while addressing ethical, technical, and practical challenges. Table 6 summarizes these emerging recommendations. Table 5 reports the emerging recommendations.

4.4. Comparison of the Overview with the Contribution of the Cutting Edge Research

To compare the findings with recent studies published after or during the last systematic reviews, cutting-edge research was considered [32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56] to analyze their contributions to the emerging recommendations for SASRs and the directions they are taking. These studies provide valuable insights that inform the design, development, and deployment of SASRs, particularly in healthcare, education, and social contexts. Table 7 presents key studies and their contributions to specific recommendations that emerged in the narrative review of systematic review, offering a clearer understanding of how these studies address essential aspects of SASR design and usage.

4.5. Limitations

This narrative review of systematic reviews employs a structured methodology designed for the narrative reviews. However, several limitations must be acknowledged, which in turn provide valuable insights into future research directions.

One significant limitation is the exclusion of conference proceedings, preprints, and gray literature. While this approach ensures that only rigorously peer-reviewed studies are included, it may omit emerging trends, innovative pilot studies, and cutting-edge experimental work that has not yet undergone formal publication. Future research should consider integrating these sources to provide a more dynamic and up-to-date understanding of SASR developments, particularly in rapidly evolving fields such as AI-driven human–robot interaction and adaptive learning algorithms.

Additionally, by focusing on the internationally published literature in English, this review enhances broad applicability and comparability across different healthcare settings. However, this language restriction may inadvertently exclude region-specific insights, localized best practices, and culturally adapted interventions that could significantly influence the effectiveness of SASRs. Future studies should prioritize multilingual and cross-cultural analyses, incorporating research from non-English sources to gain a more comprehensive view of SASR adoption and customization in diverse healthcare environments.

Another limitation lies in the reliance on systematic reviews as the primary unit of analysis. While this approach ensures a synthesis of high-level evidence, it may overlook novel perspectives, individual case studies, and experimental research that provide deeper, context-specific insights. Future research should complement systematic reviews with primary studies, especially qualitative and mixed-methods research, to capture patient experiences, caregiver perspectives, and real-world implementation challenges.

While the reviewed studies employ diverse methodologies, sample populations, and evaluation metrics, this variability reflects the multidimensional nature of SASRs and their applications across different contexts. Rather than being a limitation, this diversity underscores the need for more standardized frameworks and benchmarking tools that can facilitate clearer comparisons and assessments of SASR effectiveness, usability, and ethical considerations across diverse clinical settings. Developing common evaluation criteria could enhance the robustness of future research and support the integration of SASRs in various healthcare environments.

Additionally, while this review primarily focuses on the healthcare applications of SASRs, their broader societal and psychological implications remain an important avenue for further exploration. Future research could deepen the understanding of SASR integration in caregiving settings by examining their long-term influence on caregiver dynamics, human–machine trust relationships, and ethical considerations, particularly as robotic autonomy continues to advance.

To address these gaps, future research should carry out the following:

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Expand Data Sources: Incorporate conference proceedings, preprints, and gray literature to capture emerging trends and novel SASR applications.

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Enhance Cross-Cultural Insights: Conduct comparative studies across different linguistic and cultural contexts to understand the region-specific adaptations of SASRs.

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Integrate Primary Research: Combine systematic reviews with qualitative and mixed-methods studies to capture user experiences and implementation barriers.

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Develop Standardized Evaluation Frameworks: Establish universal benchmarking criteria for assessing SASR effectiveness, usability, and ethical compliance.

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Explore Long-Term Social and Psychological Effects: Investigate the evolving role of SASRs in human–machine relationships, trust formation, and ethical concerns in caregiving contexts.

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Promote Interdisciplinary Collaboration: Encourage joint research initiatives among engineers, healthcare professionals, ethicists, and policymakers to ensure holistic SASR development.

By addressing these limitations and pursuing these research directions, the field can advance toward the responsible and effective deployment of SASRs in healthcare and beyond.

5. Conclusions

The systematic reviews analyzed provide a comprehensive understanding of the role and impact of socially assistive service robots (SASRs) across various healthcare applications. These studies consistently highlight SASRs’ potential to enhance mental well-being, alleviate symptoms of anxiety and depression, and support cognitive and emotional recovery in vulnerable populations, including older adults, individuals with dementia, and children undergoing medical procedures. Findings from multiple systematic reviews underscore the importance of user-centered and empathy-driven design, demonstrating that robots capable of recognizing and responding to human emotions foster greater engagement, trust, and therapeutic efficacy.

A key insight emerging from the evidence is that SASRs must move beyond basic functionality to provide meaningful and personalized interactions. Studies on robotic interventions for autism, dementia care, and psychiatric conditions emphasize that the effectiveness of SASRs is linked to their ability to adapt to users’ specific needs and behavioral cues. For instance, empathy-driven robot design has been shown to improve human–robot trust, which is a fundamental factor in their successful adoption, particularly in sensitive environments such as elderly care and rehabilitation.

Despite their potential, the generalizability of findings is limited by methodological heterogeneity, variations in intervention protocols, and differences in study designs. The lack of standardized outcome measures complicates direct comparisons and limits the ability to derive clear clinical guidelines. This points to an urgent need for standardized frameworks and benchmarking tools to evaluate the effectiveness, usability, and ethical implications of SASRs in healthcare.

Ethical and practical considerations remain central to the successful integration of SASRs. Several reviews highlight concerns regarding algorithmic bias, transparency, and user privacy, emphasizing that trust in SASRs is contingent upon ensuring ethical AI governance and robust data protection mechanisms. Furthermore, cybersecurity risks associated with SASRs, particularly in clinical environments, must be addressed to safeguard patient information and prevent unauthorized access.

Future research should focus on refining SASR technologies through interdisciplinary collaboration, involving expertise from healthcare, engineering, psychology, ethics, and policy making. Large-scale randomized controlled trials (RCTs) are necessary to validate the long-term benefits of SASRs and provide stronger evidence for their clinical implementation. Additionally, studies should explore the broader societal impact of SASRs, including their influence on caregiver-patient dynamics, human–machine trust relationships, and ethical dilemmas related to increasing robot autonomy.

Overall, while SASRs present transformative opportunities for enhancing healthcare delivery and patient outcomes, their widespread adoption will require addressing key challenges related to standardization, ethical governance, and long-term efficacy. By bridging technological innovation with ethical and clinical considerations, SASRs can play a pivotal role in shaping the future of assistive and rehabilitative care, offering scalable, personalized, and emotionally intelligent solutions that support diverse patient needs.