Search Results (2,039)

Background/Objectives: Maternal, perinatal, and neonatal mortality remain major global health challenges, causing approximately 2.5 million neonatal deaths annually, particularly in low- and middle-income countries (LMICs). Although Artificial Intelligence (AI), including Machine Learning (ML) and Deep Learning (DL), is increasingly used to support healthcare decision-making, a comprehensive synthesis of its application to prevent prenatal, preterm birth, and neonatal deaths is lacking. This study systematically reviews the current state of research in this field. Methods: A Structured Literature Review (SLR) was conducted following a combined methodological framework integrating Massaro’s protocol and PRISMA 2020 guidelines. Searches were performed in Scopus, IEEE Xplore, and Google Scholar using domain-specific keywords. From 459 identified publications, 46 peer-reviewed studies published between 2018 and 2024 were selected through a four-step filtering and quality assessment process. Bibliometric and thematic analyses were performed. Results: ML techniques accounted for 71.7% of the selected studies, whereas DL approaches represented 28.3%. Neonatal death prediction was the most frequently investigated outcome (34.7% of publications). Most studies originated from Europe (39.1%) and North America (30.4%), while research from Latin America and Sub-Saharan Africa was scarce despite the high mortality burden in these regions. Key barriers included non-standardized clinical records, limited interoperability of health information systems, and the underrepresentation of LMIC populations in training datasets. Conclusions: AI shows significant potential for reducing maternal and neonatal mortality through predictive analytics. However, important geographical and methodological gaps remain. Future research should prioritize inclusive datasets and predictive frameworks adapted to resource-constrained healthcare settings. Full article

Background/Objectives: Empowerment strategies promoting healthy behaviors are important for preventing chronic diseases and cancer, while individual health responsibility (IHR) plays a significant role. Although widely discussed in the literature, the concept of IHR does not have clearly defined domains or a validated instrument for assessment. Its overlap with locus of control theory, self-motivation, and personality type creates further difficulties. Meanwhile, perception of one’s own responsibility for health is an intuitive, simple item to answer, reflecting a person’s belief related to this topic. Moreover, for ethical reasons, this concept cannot be separated from personal beliefs and should not be adjusted according to strict scales and externally imposed definitions. Therefore, this study aimed to: (1) explore whether there is an association between self-perception of IHR and preventive/risk-taking behaviors; and (2) examine socio-demographic factors associated with the IHR score and three self-perceived characteristics, namely self-perceived locus of control, health literacy, and health status. Methods: A cross-sectional study with 968 healthcare workers was conducted in a large, multidisciplinary hospital covering several behaviors (smoking, alcohol consumption, diet, and participation in a screening program for hepatitis C). IHR was evaluated on a scale from 1 to 10, where 1 meant “to very little extent” and 10 meant “to a great extent”. Results: The average IHR score was 8.80 (SD = 1.88). In the univariate analysis, the IHR score was significantly related to gender, age, education, marital status, and all three personal characteristics. In the multivariate analysis, the socio-demographic associations lost statistical significance. IHR maintained its association with the healthy diet score in the multivariable model (B = 0.113, p = 0.036). In the unadjusted analyses, the distribution of IHR differed between participants who declared alcohol consumption and those who declared abstinence (χ² = 17.157, p = 0.002). However, this association was not retained in the multivariable model. Self-perceived internal locus of control was negatively associated with screening acceptance, but not with IHR. The OR of 0.927 refers to a one-point increase on the 1–10 scale. Across the full observed scale range, this corresponds to an OR of 0.51 (95% CI: 0.29–0.89), indicating lower odds of screening acceptance among participants with higher perceived internal control. Because this estimate assumes linearity across the entire scale, it should be interpreted cautiously. Conclusions: The gap between perceived responsibility and actual behavior suggests that IHR messaging alone is unlikely to be sufficient. Future research should consider social desirability and external locus of control when analyzing IHR and health behaviors. Full article

Background: Multimorbidity is a growing global health challenge in aging populations, yet its progression across physical, psychological, and cognitive domains and its contribution to health inequalities remain unclear. We compared longitudinal patterns of multimorbidity in China and the United States (US) and examined the socioeconomic and healthcare-related factors associated with these patterns and their functional consequences. Methods: This longitudinal cohort study included adults aged ≥45 years from China (2011–2020) and the US (2012–2020), matched 1:1 by baseline age and sex. Multimorbidity was classified into eight domains spanning physical, psychological, and cognitive conditions and their combinations. Longitudinal changes across five survey waves were assessed. Multinomial logistic mixed models examined socioeconomic and healthcare-related correlations, and Cox models estimated associations with subsequent limitations in activities of daily living (ADL) limitations and instrumental activities of daily living (IADL) limitations. Results: A total of 7064 participants from China (mean age 60.15 ± 7.93 years; 45.4% male) and 7064 matched participants from the US were included. Multimorbidity patterns were more complex in the US at baseline, but progression toward cross-domain multimorbidity occurred in both countries and was more pronounced in China. Psychological conditions occupied a central position in the development of more complex multimorbidity patterns. Higher educational and household wealth were consistently associated with lower odds of cognitive-related and cross-domain multimorbidity in both countries, whereas associations with healthcare-related factors varied across settings. Cross-domain multimorbidity was more strongly associated with functional limitations than within-domain multimorbidity. In particular, physical–psychological–cognitive multimorbidity was associated with substantially higher risks of ADL limitations (hazard ratio (HR)  =  5.70, 95% confidence interval (CI)  =  4.87–6.67 in China; HR  =  7.72, 95% CI  =  5.90–10.11 in the US) and IADL limitations (HR  =  4.14, 95% CI  =  3.64–4.71; HR  =  5.84, 95% CI  =  4.50–7.59, respectively). Conclusions: Multimorbidity increasingly spans physical, psychological, and cognitive domains in both China and the US. Psychological conditions appeared to bridge physical and cognitive conditions in more complex multimorbidity patterns. Integrated care models incorporating psychological health and strategies addressing socioeconomic inequalities may help reduce the burden of multimorbidity and related functional decline. Full article

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines. Full article

Background: Pharmacogenetic (PGx) testing could identify actionable drug–gene interactions, reducing the risks of inappropriate prescribing of certain medications in some patients. An area of growing public health concern is rising global rates of depression and antidepressant use over the last two decades. Prior research has elucidated perspectives of healthcare providers who prescribe antidepressants regarding the clinical utility of genetic information, including PGx testing, but there is a gap in understanding how individual perspectives and systemic contextual factors may combine to influence PGx testing adoption. Objective: The objective of this study was to elucidate combinations of individual and contextual conditions associated with willingness to adopt PGx testing for Cytochrome P450 Subfamily IID, Polypeptide 6 (CYP2D6) and Subfamily IIC, Polypeptide 19 (CYP2C19) among antidepressant prescribers. Methods: We conducted a cross-sectional, mixed-methods study using structured questionnaires and semi-structured interviews with healthcare providers who prescribe antidepressants within their scope of practice across four healthcare systems in the United States. We collected data on implementation science concepts from the Theoretical Domains Framework, the Consolidated Framework for Implementation Research (CFIR), and the Implementation Outcomes Framework. Coincidence analysis (CNA), a case-based, Boolean logic-based method that identifies minimally sufficient combinations of conditions that lead to a particular outcome, was used to identify combinations of conditions for PGx test adoption among antidepressant prescribers. Interviews were also conducted with 10 patients who received pharmacogenetic testing within these healthcare systems to contextualize findings with patient perspectives. Results: Prescribers adopted PGx testing when they believed it would be beneficial to patients and were not deterred by cost-related concerns; the combination of these conditions led to PGx adoption in the most highly supported CNA model. Patient perspectives were also consistent with the selected model, with data suggesting they may have greater willingness to tolerate costs when they perceived or experienced benefits from testing. Conclusions: Insights from this study may be used by health system administrators and public health policymakers to inform future PGx implementation strategies that enhance uptake and awareness of existing evidence for clinical benefits of PGx testing and mitigate cost-related barriers to adoption. Full article

Electricity is fundamental for all modern infrastructures, while disruptions in transmission networks could result in severe failures in healthcare, transportation, communication, industry, and all related to public safety. However, inspection of overhead lines and their components is costly and labor-intensive. The convergence of Unmanned Aerial Vehicles (UAVs), advanced imaging sensors, and deep-learning-based Computer Vision has reshaped this domain. To this end, this work presents a systematic review of Computer Vision applications in electric power transmission line inspection. From an initial 1493 Scopus records, 148 studies published between 2018 and 2026 were retained through a transparent, multi-stage screening and quality-scoring process based on PRISMA guidelines. The reviewed literature was synthesized across four axes: (1) monitoring platforms and sensor technologies, (2) Computer Vision approaches per vision task, (3) datasets, metrics, and evaluation practices, and (4) synthesis of results and industrial adoption. The analysis of the literature confirms the dominance of You Only Look Once (YOLO) family models for real-time edge deployment and the rising adoption of Transformer architectures, while exposing persistent gaps in dataset availability, domain generalization, and field validation. The review concludes with the open challenges and concrete future directions toward fully autonomous, robust, and sustainable inspection systems. Full article

Background: Primary healthcare centers are an essential component of disaster response systems, particularly in the Makkah Region of Saudi Arabia, where recurrent mass gatherings and environmental hazards increase the need for workforce readiness. Although nurses play a central role in frontline emergency response, evidence on disaster preparedness among primary healthcare nurses in Saudi Arabia remains limited. Objective: To assess the level of disaster preparedness among primary healthcare nurses in Makkah and examine demographic and professional factors associated with preparedness. Methods: A descriptive cross-sectional correlational study was conducted among 194 nurses working in primary healthcare centers in the Makkah Region. A multi-stage sampling approach was used in which 23 of 115 primary healthcare centers were randomly selected, followed by recruitment of eligible nurses through an online survey using non-probability methods. Disaster preparedness was measured using an adapted 68-item Disaster Preparedness Evaluation Tool covering biological and chemical preparedness, disaster response, family preparedness, disaster recovery, awareness and knowledge, communication and coordination, psychological preparedness, and training and participation. Results: Overall disaster preparedness was moderate to high (M = 3.84, SD = 0.75). The highest mean scores were observed for family preparedness (M = 4.56, SD = 1.11), biological and chemical preparedness (M = 4.32, SD = 0.95), and disaster response (M = 4.31, SD = 0.99), whereas training and participation had the lowest mean score (M = 2.65, SD = 0.54). Preparedness differed significantly by educational attainment, specialization, and years of experience, while no significant gender difference was observed. Correlations among preparedness dimensions were strong (r = 0.685–0.872), indicating that the measured preparedness domains were closely interrelated. Conclusions: Primary healthcare nurses in Makkah demonstrated generally favorable disaster preparedness; however, important gaps remain in training and participation, communication and coordination, and psychological preparedness. Strengthening simulation-based education, interdisciplinary drills, communication systems, and continuing professional development may improve frontline emergency readiness in primary care settings exposed to recurrent large-scale hazards. Full article

Background/Objectives: While there is an existing body of quantitative data on the psychological and mental burden of suicide-bereaved parents, further qualitative research is needed to explore suicide-bereaved mothers’ living experiences in specific sociocultural contexts, especially in Southern Europe and the Eastern Mediterranean. Given the persistent stigma surrounding suicide in these societies, and the fact that previous research has often overlooked mothers’ perspectives in favor of broader samples of bereaved parents, we explored the psychosocial needs of Greek-speaking suicide-bereaved mothers in the Republic of Cyprus, and their experiences in accessing formal mental healthcare support. Methods: An inductive, secondary content analysis of qualitative data collected through personal semi-structured interviews with ten suicide-bereaved mothers was employed. Results: Participants’ psychosocial needs centered around a “persistent orientation towards protection,” encompassing three interconnected domains: (1) self-protection and the need for acceptance by shielding themselves from stigma, social judgment, and emotional disintegration, (2) ensuring a safe and protective environment for the surviving family by safeguarding the psychological well-being and cohesion of surviving family members, and (3) protecting the posthumous dignity and memory of the deceased child. Rather than seeking formal support, participants overwhelmingly avoided mental health services, citing a lack of empathy, cultural misunderstanding, and fear of further stigmatization. Mental health professionals were often perceived as inadequate or even harmful, undermining participants’ need for protective attitudes, self-reliance and self-respect during bereavement. These responses reflected how stigma and gendered social expectations surrounding suicide shaped participating bereaved mothers’ disengagement from the healthcare system, despite their intense psychological needs. Conclusions: These findings underscored how gendered social expectations, combined with the stigma surrounding suicide, created significant psychosocial barriers to mental health care for women navigating traumatic grief, particularly in sociocultural contexts where suicide remains highly stigmatized. Full article

Background and Objectives: Despite substantial advances in surgical techniques, anesthesia, and perioperative care, postoperative complications remain a major source of morbidity, mortality, prolonged hospitalization, and healthcare utilization following adult cardiac surgery (CS). Increasing evidence suggests that many of these complications are influenced by modifiable perioperative factors that can be addressed through multidisciplinary care. Materials and Methods: A narrative review was conducted to synthesize current evidence regarding perioperative practices associated with the prevention of postoperative complications in adult CS. A comprehensive literature search of PubMed/MEDLINE, Scopus, and Web of Science identified studies published between January 2015 and April 2026, supplemented by landmark studies and relevant clinical guidelines. Results: The identified evidence was organized into four major domains: infection prevention practices, physiological optimization strategies, protocol adherence and patient safety measures, and organizational and human factors. The strongest evidence supports timely antimicrobial prophylaxis, standardized infection prevention bundles, perioperative glycemic control, maintenance of normothermia, and patient blood management as key interventions associated with improved postoperative outcomes. Surgical safety checklists, standardized perioperative pathways, and adherence to evidence-based protocols further contributed to improved patient safety and consistency of care. Emerging evidence also highlighted the importance of communication, teamwork, safety culture, workload management, and healthcare professionals’ knowledge in facilitating successful implementation of perioperative interventions. Conclusions: Prevention of postoperative complications following CS requires a multidisciplinary, systems-based approach integrating evidence-based clinical interventions with standardized perioperative protocols and effective organizational practices that facilitate consistent implementation of evidence-based perioperative care. Future research should focus on prospective evaluation of integrated perioperative strategies, development of practical risk-stratification models, and further investigation of organizational determinants influencing implementation and postoperative outcomes. Full article

Climate-related hazards are occurring with increasing frequency, resulting in notable disruptions to healthcare systems. In Malaysia, healthcare facilities are particularly impacted by flooding and heatwaves, which can occur annually across some regions. Despite these recurrent challenges, there remains limited availability of a standardized tool to systematically assess healthcare facility (HCF) vulnerability to climate hazards. This study aimed to develop, validate and conduct a field testing of the Vulnerability Index Tool for Assessing Levels of Climate Resilience in Healthcare Facilities (VITAL-HCF) for facility-level assessment in Malaysia. The VITAL-HCF was developed through extensive literature review, experts consultations, and adaptation of the World Health Organization (WHO) healthcare facility vulnerability checklist. The tool underwent forward and backward translation to ensure linguistic and contextual equivalence, followed by content and face validation by subject-matter experts in climate and healthcare professionals. Subsequently, field testing was performed in three government healthcare facilities to assess the clarity, applicability, and feasibility of administration to ensure accurate responses representing facility-level capacity and vulnerability. The healthcare facility vulnerability index (HCFVI) for heatwaves and flooding was then calculated for each facility. Revised Scale-Level Content Validity Indices (S-CVI/Ave) varied across the exposure, sensitivity and adaptive capacity domains (0.93–1.00). Items with a content validity index < 0.83 were either removed or revised and reorganized to improve relevance. Face validation showed good clarity with S-FVI/Ave ≥ 0.87. The final tool comprised 12 exposure, 11 sensitivity, and 181 adaptive capacity indicators. Field testing showed that a facilitated, multidisciplinary group approach among key respondents was feasible and timely. Facility A, located in an urban setting, had high vulnerability for hot weather and heatwaves (HCFVI = 0.51), while facilities B and C recorded moderate vulnerability. Both Facilities A and B recorded moderate vulnerability for floods, while Facility C, a hospital in an urban setting, had low vulnerability (HCFVI = 0.23). The VITAL-HCF demonstrated satisfactory content validity, face validity, and feasibility for assessing climate-related vulnerability in HCFs. The tool incorporates key vulnerability components of exposure, sensitivity, and adaptive capacity, providing a structured approach for the systematic assessment of climate-related vulnerability in healthcare facilities to support targeted preparedness and resilience planning. Full article

Micro-expression recognition (MER) has a lot of applications in lie detection, education, healthcare, etc., as involuntary micro-expressions (MEs) may provide subtle facial cues associated with affective responses. With the development of deep learning, many studies have recently employed Vision Transformers (ViTs) to investigate MER, since ViTs show promising performance in various visual domains due to their excellent local–global modeling ability. However, such methods confront two fundamental challenges: First, fine-grained visual features are needed to capture the subtle facial movements of MEs, which ViTs relatively fall short on due to coarse patch resolution constrained by their quadratic complexity. Second, the data-intensive nature of ViTs impedes effective learning given the limited scale of ME data. To overcome the aforementioned limitations of using ViTs for MER, we propose the Prior-aware Sparse Transformer (PAST), a novel Transformer-based architecture integrating spatial and semantic prior knowledge synergistically into a sparse attention mechanism, enabling linear-complexity processing of large amounts of fine-grained features. Specifically, we first designed an extraction algorithm to generate a representative set of motion-intensive Principal Anchors, which are used to guide the model’s focus on biologically critical regions during sampling. Second, we introduced the Semantic Dictionary, which was trained with a carefully designed self-contrastive loss to embed task-invariant discriminative semantics of the anchors. Such global semantics further modulate patch sampling and attention weighting in the sparse attention procedure, achieving better training performance with limited ME data. Extensive evaluations on MEGC and CD6ME protocols demonstrate state-of-the-art performance, validating PAST’s efficacy for MER. Full article

Minimizing computational overhead in healthcare time-series classification remains a critical challenge, particularly for deep learning models operating on high-dimensional sequential data under resource and latency constraints. This challenge is further exacerbated by adversarial attacks, which introduce evolving threats and necessitate robust yet efficient mechanisms for maintaining reliable model performance—a requirement of paramount importance in healthcare, where wearable and smart devices have brought continuous monitoring outside clinical settings and model failures can directly compromise patient safety. In this paper, we propose ReLATE+, a unified framework for adversarially robust and computationally efficient time-series classification. ReLATE+ integrates three key capabilities: (i) detection and classification of adversarial inputs, (ii) dataset-level similarity analysis, and (iii) adaptive model selection. Upon receiving new data, the framework first determines whether the input is adversarial and identifies the attack type. It then leverages this information to retrieve a similar dataset from a repository and identify the corresponding high-performing models and trains only a small set of selected candidates instead of exhaustively retraining all models. This approach ensures strong performance while reducing the need for retraining, and it generalizes well across different domains with varying data distributions and feature spaces. Experiments show that ReLATE+ reduces computational overhead by an average of 77.68%, enhancing adversarial resilience and streamlining robust model selection, all without sacrificing performance, within 2.02% of Oracle. Full article

In the healthcare and clinical domain, artificial intelligence (AI) is evolving from earlier models that primarily predicted outcomes or generated content toward agentic AI systems that demonstrate the capability to make decisions and complete tasks autonomously. Previous research on AI has contributed significantly to disease identification, deep learning applications, large language models (LLMs), and generative AI. These systems primarily function as assistive tools, as they generate text or predictions without directly interacting with clinical infrastructures. Therefore, recent research trends are increasingly oriented toward agentic AI systems that extend beyond traditional predictive and generative model performance. This manuscript provides a detailed review of the current state of agentic AI, starting with the evolution of AI and the concept of a medical agent. A medical agent refers to an intelligent AI system designed to assist in clinical or administrative tasks by analyzing data, supporting decision making, and interacting with healthcare environments. Its underlying agentic AI architecture integrates planning, memory, reasoning, and environmental interaction to enable autonomous tool use, multi-agent collaboration, and continuous perception decision action loops across diverse healthcare applications and clinical workflows. The review further examines safety mechanisms, including human-in-the-loop oversight, self-verification strategies, and regulatory alignment frameworks, which are designed to ensure reliability, accountability, compliance, and safe deployment in regulated healthcare environments. Our findings indicate that a large number of AI agents have been introduced in various manuscripts for healthcare applications; however, fully autonomous systems remain challenging to achieve, as AI still faces several limitations related to reliability, interpretability, data dependency, and integration within complex clinical workflows. In response to these challenges, this survey shifts the focus from task-specific model performance to system-level autonomy and workflow orchestration, providing a structured foundation for understanding the design, deployment, governance, and limitations of agentic AI systems in modern healthcare ecosystems. Full article

Background/Objectives: Large multinational mass gatherings can create healthcare encounters in which severe mental health screening signals require rapid recognition, confidential assessment, language support, and referral. During Hajj, culturally and linguistically diverse pilgrims may first disclose severe psychological symptoms in general medical or field settings rather than through specialist psychiatric pathways. This study estimated the prevalence, overlap, and adjusted correlates of severe screen-positive mental health symptom signals among adult multinational Hajj pilgrims in 2025 and considered implications for multilingual healthcare encounters and referral planning. Methods: This cross-sectional field screening study analyzed de-identified coded data from 2171 adult pilgrims aged 18–95 years. Participants completed a structured, interview-administered multilingual mental health screening survey comprising 23 binary symptom items. The analysis focused on suicide-related symptoms, psychotic-like symptoms, and mood-instability/bipolar-spectrum symptoms. A severe symptom cluster was defined as positivity in at least one of these domains. Prevalence estimates with Wilson 95% confidence intervals, domain overlap, and multivariable logistic regression models were used. Results: Severe symptom cluster positivity was identified in 428 participants (19.7%; 95% CI 18.1–21.4). Mood-instability/bipolar-spectrum signals were most frequent (13.3%; 95% CI 11.9–14.8), followed by psychotic-like symptoms (8.0%; 95% CI 6.9–9.2) and suicide-related symptoms (5.6%; 95% CI 4.7–6.6). Among cluster-positive participants, 297 endorsed one severe domain, 108 endorsed two domains, and 23 endorsed all three. Current physical illness was independently associated with all severe outcomes, whereas prior psychiatric history was not independently associated with suicide-related symptoms. Conclusions: Severe screen-positive mental health signals were present in a clinically relevant minority of pilgrims. The findings support integrating confidential severe symptom screening, interpreter-supported assessment, psychological first aid, and clear referral pathways into general Hajj healthcare encounters, while emphasizing that screen-positive findings are not psychiatric diagnoses. Full article

Background: Although cervical cancer is a highly preventable malignancy, Romania continues to report critical mortality rates. This paradox is largely attributable to the suboptimal implementation of clinical guidelines and a passive, “opt-in” administrative framework. This cross-sectional study investigates the cognitive and behavioural determinants—specifically, human papillomavirus (HPV) knowledge, institutional trust in healthcare systems, and digital misinformation exposure—that modulate female attitudes toward HPV immunisation. Methods: An observational cross-sectional study was conducted between December 2025 and March 2026 across four obstetrics and gynaecology clinics. Data were collected via digital questionnaires administered to adult female patients aged 18 to 65 years. The assessment tool evaluated four primary domains: HPV knowledge, perceived vaccine safety, immunisation intention, and the degree of exposure to social media misinformation. Results: The cohort demonstrated a substantially low vaccination rate (28.6%). Statistical analysis elucidated significant positive correlations between HPV knowledge, trust in health authorities, and vaccination intention. Furthermore, reliance on official information sources strongly correlated with perceived vaccine safety. Conversely, exposure to negative online content exhibited a weak negative association with knowledge levels, yet it did not directly influence vaccination intention. Conclusions: Health literacy and institutional trust emerge as fundamental pillars for HPV vaccine acceptance. To overcome patient vaccine hesitancy, the national healthcare system must initiate a strategic paradigm shift, prioritising the neutralization of digital misinformation and transitioning from a passive model to a proactive, “opt-out” infrastructure. Full article