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Background/Objectives: There is growing evidence to support ambient artificial intelligence (AI) scribes in healthcare to improve medical documentation by generating timely and comprehensive notes. Using the Plan–Do–Study–Act (PDSA) methodology, this study evaluated the utility and potential time savings of an ambient AI scribe, Scribeberry, (V2), in a palliative medicine outpatient setting, comparing it to the standard practice of dictation. Methods: This prospective quality improvement study was conducted at an academic medical center by two palliative medicine resident physicians. Residents documented patient visits using a freely available ambient AI scribe software program, Scribeberry, as well as using standard dictation software. Primary outcome measures included the editing time for the AI scribe and the dictating and editing times for a dictated manuscript, as well as subjective assessments of the accuracy, organization, and overall usefulness of the AI-generated clinical letters. Results: A heterogenous response was seen with the implementation of an AI scribe. One resident saw a statistically significant reduction (p < 0.025) in the time spent on clinical documentation, while a second resident saw essentially no improvement. The resident who experienced time savings with the ambient AI scribe also demonstrated a significant improvement in the graded organization and usefulness of the AI outputs over time, while the other resident did not demonstrate significant improvements in any of the metrics assessed over the course of this project. Conclusions: This pilot study describes the use of an ambient AI scribe software program, Scribeberry, in the community palliative medicine context. Our results showed a mixed response with respect to time savings and improvements in the organization, accuracy, and overall clinical usefulness of the AI-generated notes over time. Given the small sample size and short study duration, this study is insufficiently powered to draw conclusions with respect to AI scribe benefits in real-world contexts. Full article

As artificial intelligence advances toward unprecedented capabilities, society faces a choice between two trajectories. One continues scaling transformer-based architectures, such as state-of-the-art large language models (LLMs) like GPT-4, Claude, and Gemini, aiming for broad generalization and emergent capabilities. This approach has produced powerful tools but remains largely statistical, with unclear potential to achieve hypothetical “superintelligence”—a term used here as a conceptual reference to systems that might outperform humans across most cognitive domains, though no consensus on its definition or framework currently exists. The alternative explored here is the Mindful Machines paradigm—AI systems that could, in future, integrate intelligence with semantic grounding, embedded ethical constraints, and goal-directed self-regulation. This paper outlines the Mindful Machine architecture, grounded in Mark Burgin’s General Theory of Information (GTI), and proposes a post-Turing model of cognition that directly encodes memory, meaning, and teleological goals into the computational substrate. Two implementations are cited as proofs of concept. Full article

Artificial intelligence (AI) is emerging as a valuable complementary tool in small-molecule drug discovery, augmenting traditional methodologies rather than replacing them. This review examines the evolution of AI from early rule-based systems to advanced deep learning, generative models, diffusion models, and autonomous agentic AI systems, highlighting their applications in target identification, hit discovery, lead optimization, and safety prediction. We present both successes and failures to provide a balanced perspective. Notable achievements include baricitinib (BenevolentAI/Eli Lilly, an existing drug repurposed through AI-assisted analysis for COVID-19 and rheumatoid arthritis), halicin (MIT, preclinical antibiotic), DSP-1181 (Exscientia, discontinued after Phase I), and ISM001-055/rentosertib (Insilico Medicine, positive Phase IIa results). However, several AI-assisted compounds have also faced challenges in clinical development. DSP-1181 was discontinued after Phase I, despite a favorable safety profile, highlighting that the acceleration of discovery timelines by AI does not guarantee clinical success. Despite progress, challenges such as data quality, model interpretability, regulatory hurdles, and ethical concerns persist. We provide practical insights for integrating AI into drug discovery workflows, emphasizing hybrid human-AI approaches and the emergence of agentic AI systems that can autonomously navigate discovery pipelines. A critical evaluation of current limitations and future opportunities reveals that while AI offers significant potential as a complementary technology, realistic expectations and careful implementation are crucial for delivering innovative therapeutics. Full article

The absence of parental presence has a direct impact on the emotional stability and social routines of children, especially during extended periods of separation from their family environment, as in the case of daycare centers, hospitals, or when they remain alone at home. At the same time, the technology currently available to provide emotional support in these contexts remains limited. In response to the growing need for emotional support and companionship in child care, this project proposes the development of a multi-platform software architecture based on artificial intelligence (AI), designed to be integrated into humanoid robots that assist children between the ages of 6 and 14. The system enables daily verbal and non-verbal interactions intended to foster a sense of presence and personalized connection through conversations, games, and empathetic gestures. Built on the Robot Operating System (ROS), the software incorporates modular components for voice command processing, real-time facial expression generation, and joint movement control. These modules allow the robot to hold natural conversations, display dynamic facial expressions on its LCD (Liquid Crystal Display) screen, and synchronize gestures with spoken responses. Additionally, a graphical interface enhances the coherence between dialogue and movement, thereby improving the quality of human–robot interaction. Initial evaluations conducted in controlled environments assessed the system’s fluency, responsiveness, and expressive behavior. Subsequently, it was implemented in a pediatric hospital in Guayaquil, Ecuador, where it accompanied children during their recovery. It was observed that this type of artificial intelligence-based software, can significantly enhance the experience of children, opening promising opportunities for its application in clinical, educational, recreational, and other child-centered settings. Full article

Organs-on-Chips (OoC) technology has begun to be considered a pragmatic tool for drug evaluation, offering researchers an opportunity to move beyond the less physiologically relevant animal models. OoCs are microfluidic structures that imitate the functionalities of individual human organs, serving as mimicry tools for drug response and reproducibility studies. On the one hand, companies producing OoCs find managing and analyzing the large amounts of data generated challenging. This is where artificial intelligence (AI) can be deployed to address such problems. This paper will present the state-of-the-art of current OoC technology and AI, discussing the benefits and threats of combining these approaches. AI can be applied to optimize the process of OoC fabrication and operation, as well as for the big data analysis of OoC devices. By combining these technologies, scientists gain a powerful tool for drug development that is more efficient and accurate. However, processing the vast datasets generated by OoC systems often requires specialized AI expertise and computational resources. Despite the numerous possible benefits of amalgamating OoC technology with AI, several challenges and limitations need to be addressed. The large datasets generated by OoC systems can be difficult to process and analyze, which is a task that may require specialized AI expertise. Additionally, limitations of OoC systems include issues with reproducibility, as the devices are sensitive to perturbations in experimental conditions. Furthermore, the development and implementation of AI algorithms require significant computational resources and expertise, which may not be readily available to all research institutions. To overcome these challenges, interdisciplinary collaboration between biologists, engineers, data scientists, and AI experts is essential. Continued advancements in both OoC technology and AI will likely lead to more robust and versatile platforms for biomedical research and drug development, ultimately contributing to the advancement of personalized medicine and the reduction of reliance on animal testing. Full article

The rapid expansion of online learning platforms has necessitated advanced systems to address scalability, personalization, and assessment challenges. This paper presents a comprehensive review of artificial intelligence (AI)-based decision support systems (DSSs) designed for online learning and assessment, synthesizing advancements from 2020 to 2025. By integrating machine learning, natural language processing, knowledge-based systems, and deep learning, AI-DSSs enhance educational outcomes through predictive analytics, automated grading, and personalized learning paths. This study examines system architecture, data requirements, model selection, and user-centric design, emphasizing their roles in achieving scalability and inclusivity. Through case studies of a MOOC platform using NLP and an adaptive learning system employing reinforcement learning, this paper highlights significant improvements in grading efficiency (up to 70%) and student performance (12–20% grade increases). Performance metrics, including accuracy, response time, and user satisfaction, are analyzed alongside evaluation frameworks combining quantitative and qualitative approaches. Technical challenges, such as model interpretability and bias, ethical concerns like data privacy, and implementation barriers, including cost and adoption resistance, are critically assessed, with proposed mitigation strategies. Future directions explore generative AI, multimodal integration, and cross-cultural studies to enhance global accessibility. This review offers a robust framework for researchers and practitioners, providing actionable insights for designing equitable, efficient, and scalable AI-DSSs to transform online education. Full article

Artificial intelligence (AI) plays a pivotal role in Industry 4.0, with predictive maintenance (PdM) emerging as a core application for improving operational efficiency by reducing unplanned downtime and extending asset life. Despite these advancements, the black-box nature of AI models remains a significant barrier to adoption, as industry stakeholders require systems that are both transparent and trustworthy. This study presents a systematic literature review examining how human-in-the-loop explainable AI (HITL-XAI) approaches can enhance the effectiveness and adoption of AI systems in PdM contexts. This review followed the PRISMA methodology, employing predefined search strings across Scopus, ProQuest, and EBSCO databases. Sixty-three peer-reviewed journal articles, published between 2019 and early 2025, were included in the final analysis. The selected studies span various domains, including industrial manufacturing, energy, and transportation, with findings synthesized through both descriptive and thematic analyses. A key gap identified is the limited empirical exploration of generative AI (GenAI) in improving the usability, interpretability, and trustworthiness of HITL-XAI systems in PdM applications. This review outlines actionable insights for integrating explainability and GenAI into existing rule-based PdM systems to support more adaptive and reliable maintenance strategies. Ultimately, the findings underscore the importance of designing HITL-XAI systems that not only demonstrate high model performance but are also effectively aligned with operational workflows and the cognitive needs of maintenance personnel. Full article

Traditional Case-Based Reasoning (CBR) methods face significant methodological challenges, including limited information resources in case databases, methodologically inadequate similarity calculation approaches, and a lack of standardized case revision mechanisms. These limitations lead to suboptimal case matching and insufficient solution adaptation, highlighting critical gaps in the development of CBR methodologies. This paper proposes a novel CBR framework enhanced by generative AI, aiming to improve and innovate existing methods in three key stages of traditional CBR, thereby enhancing the accuracy of retrieval and the scientific nature of corrections. First, we develop an ontology model for comprehensive case representation, systematically capturing scenario characteristics, risk typologies, and strategy frameworks through structured knowledge representation. Second, we introduce an advanced similarity calculation method grounded in triangle theory, incorporating three computational dimensions: attribute similarity measurement, requirement similarity assessment, and capability similarity evaluation. This multi-dimensional approach provides more accurate and robust similarity quantification compared to existing methods. Third, we design a generative AI-based case revision mechanism that systematically adjusts solution strategies based on case differences, considering interdependence relationships and mutual influence patterns among risk factors to generate optimized solutions. The methodological framework addresses fundamental limitations in existing CBR approaches through systematic improvements in case representation, similarity computation, and solution adaptation processes. Experimental validation using actual case data demonstrates the effectiveness and scientific validity of the proposed methodological framework, with applications in risk assessment and emergency response scenarios. The results show significant improvements in case-matching accuracy and solution quality compared to traditional CBR approaches. This method provides a robust methodological foundation for CBR-based decision-making systems and offers practical value for risk management applications. Full article

Autonomous ship control faces significant challenges due to the diversity of ship types, the complexity of task scenarios, and the uncertainty of dynamic marine environments. These factors limit the effectiveness of traditional control approaches that rely on explicit dynamics modeling and handcrafted control laws. With the rapid advancement of computing and artificial intelligence, imitation learning offers a promising alternative by directly learning expert behaviors from data. This paper proposes a Generative Adversarial Imitation Learning (GAIL) method for heading and path-following control of autonomous surface ships. It employs an adversarial learning structure, in which a generator learns control policies that reproduce expert behavior while a discriminator distinguishes between expert and learned trajectories. In this way, the control strategies can be learned from expert demonstrations without requiring explicit reward design. The proposed method is validated through simulations on a model-scale tug. Compared with a behavioral cloning (BC) baseline controller, the GAIL-based controller achieves superior performance in terms of path-following accuracy, heading stability, and control smoothness, confirming its effectiveness and potential for real-world deployment. Full article

Miao embroidery holds significant cultural, economic, and aesthetic value. However, its transmission faces numerous challenges: a high learning threshold, a lack of interest among younger generations, and low production efficiency. These factors have created obstacles to its sustainable development. In the age of artificial intelligence (AI), generative AI is expected to improve the efficiency of pattern innovation and the adaptability of the embroidery industry. Therefore, this study proposes a Miao embroidery pattern generation and application method based on Stable Diffusion and low-rank adaptation (LoRA) fine-tuning. The process includes image preprocessing, data labeling, model training, pattern generation, and embroidery production. Combining objective indicators with subjective expert review, supplemented by feedback from local artisans, we systematically evaluated five representative Miao embroidery styles, focusing on generation quality and their social and business impact. The results demonstrate that the proposed model outperforms the original diffusion model in terms of pattern quality and style consistency, with optimal results obtained under a LoRA scale of 0.8–1.2 and diffusion steps of 20–40. Generated patterns were parameterized and successfully implemented in digital embroidery. This method uses AI technology to lower the skill threshold for embroidery training. Combined with digital embroidery machines, it reduces production costs, significantly improving productivity and increasing the income of embroiderers. This promotes broader participation in embroidery practice and supports the sustainable inheritance of Miao embroidery. It also provides a replicable technical path for the intelligent generation and sustainable design of intangible cultural heritage (ICH). Full article

This paper presents a modification of an image encryption algorithm combining chaos and the Fibonacci matrix by integrating artificial intelligence via a Generative Pre-Trained Transformer (GPT). The goal is to improve the robustness of the algorithm by dynamically adapting the parameters of the chaotic system and generating cryptographic keys based on image characteristics. The proposed methodology includes two main innovations: the implementation of GPT for automated generation of the initial parameters of the chaotic system, which allows for greater variability and security in encryption, and the use of GPT for dynamic determination of the Fibonacci Q-matrix, which provides additional complexity and increased resistance to attacks. The method is realized in the MATLAB (R2023a) environment through integration with OpenAI API and the MATLAB–Python interface for requesting GPT models. The efficiency and reliability of the modified algorithm are compared with those of standard chaotic encryption algorithms, and its robustness to various cryptographic attacks is also studied. Full article

Genitourinary (GU) cancers, including prostate, bladder, and renal cancers, represent a significant burden on global health. Conventional treatments, while effective in certain contexts, face limitations due to tumor heterogeneity, therapeutic resistance, and relapse. Recent advances in cancer immunotherapy, particularly in the development of personalized mRNA and DNA-based vaccines, have opened new avenues for precise and durable antitumor responses. These vaccines are being developed to leverage neoantigen identification and next-generation sequencing technologies, with the goal of tailoring immunotherapeutic interventions to individual tumor profiles. mRNA vaccines offer rapid, non-integrative, and scalable, with encouraging results reported in infectious diseases and early-phase cancer trials. DNA vaccines, known for their stability and ease of modification, show promise in generating robust cytotoxic T-cell responses. This review discusses the current landscape, preclinical findings, and ongoing clinical trials of mRNA and DNA-based vaccines in GU cancers, highlighting delivery technologies, combination strategies with immune checkpoint inhibitors, and future challenges, including tumor immune evasion and regulatory hurdles. Integrating immunogenomics and artificial intelligence into vaccine design is poised to further enhance precision in cancer vaccine development. As GU malignancies remain a leading cause of cancer-related morbidity and mortality, mRNA and DNA vaccine strategies represent a promising and rapidly evolving area of investigation in oncology. Full article

This survey offers a comprehensive overview of advancements in Artificial Intelligence in Graphic Design (AIGD), with a focus on the integration of AI techniques to enhance design interpretation and creative processes. The field is categorized into two primary directions: perception tasks, which involve understanding and analyzing design elements, and generation tasks, which focus on creating new design elements and layouts. The methodology emphasizes the exploration of various subtasks including the perception and generation of visual elements, aesthetic and semantic understanding, and layout analysis and generation. The survey also highlights the role of large language models and multimodal approaches in bridging the gap between localized visual features and global design intent. Despite significant progress, challenges persist in understanding human intent, ensuring interpretability, and maintaining control over multilayered compositions. This survey aims to serve as a guide for researchers, detailing the current state of AIGD and outlining potential future directions. Full article

Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods: In this multi-reader, multi-case study, 14 clinicians of varying experience reviewed 374 retrospectively selected LUS scans (cine clips from the PLAPS point, obtained using three different probes) from 359 patients across six centers in the U.S. and Canada. In phase one, readers scored the likelihood (0–100) of pleural effusion and consolidation/atelectasis without AI. After a 4-week washout, they re-evaluated all scans with AI-generated bounding boxes. Performance metrics included area under the curve (AUC), sensitivity, specificity, and Fleiss’ Kappa. Subgroup analyses examined effects by reader experience. Results: For pleural effusion, AUC improved from 0.917 to 0.960, sensitivity from 77.3% to 89.1%, and specificity from 91.7% to 92.9%. Fleiss’ Kappa increased from 0.612 to 0.774. For consolidation/atelectasis, AUC rose from 0.870 to 0.941, sensitivity from 70.7% to 89.2%, and specificity from 85.8% to 89.5%. Kappa improved from 0.427 to 0.756. Conclusions: AI assistance enhanced clinician detection of pleural effusion and consolidation/atelectasis in LUS scans, particularly benefiting less experienced users. Full article

Chronic kidney disease (CKD) affects over 850 million individuals worldwide, yet conventional risk stratification approaches fail to capture complex disease progression patterns. Current machine learning approaches suffer from inefficient parameter optimization and limited clinical interpretability. We developed an integrated framework combining advanced Bayesian optimization with explainable artificial intelligence for enhanced CKD risk assessment. Our approach employs XGBoost ensemble learning with intelligent parameter optimization through Optuna (a Bayesian optimization framework) and comprehensive interpretability analysis using SHAP (SHapley Additive exPlanations) to explain model predictions. To address algorithmic “black-box” limitations and enhance clinical trustworthiness, we implemented four-tier risk stratification using stratified cross-validation and balanced evaluation metrics that ensure equitable performance across all patient risk categories, preventing bias toward common cases while maintaining sensitivity for high-risk patients. The optimized model achieved exceptional performance with 92.4% accuracy, 91.9% F1-score, and 97.7% ROC-AUC, significantly outperforming 16 baseline algorithms by 7.9–18.9%. Bayesian optimization reduced computational time by 74% compared to traditional grid search while maintaining robust generalization. Model interpretability analysis identified CKD stage, albumin-creatinine ratio, and estimated glomerular filtration rate as primary predictors, fully aligning with established clinical guidelines. This framework delivers superior predictive accuracy while providing transparent, clinically-meaningful explanations for CKD risk stratification, addressing critical challenges in medical AI deployment: computational efficiency, algorithmic transparency, and equitable performance across diverse patient populations. Full article