Background/Objectives: Heart transplantation is a life-saving treatment for advanced cardiac disease, but post-transplant adaptation may involve psychosocial, identity-related, and existential change. Some recipients interpret these changes as corresponding to donor characteristics, although the nature and evidence base of these reports remain unclear. This review mapped post-transplant changes reported by adult heart transplant recipients, perceived donor–recipient correspondences, and the explanatory frameworks proposed for these phenomena. Methods: A scoping review was conducted following the Arksey and O’Malley framework, Joanna Briggs Institute guidance, and PRISMA-ScR reporting standards. PubMed, Web of Science, Scopus, CINAHL, and PsychINFO were searched in August 2026. Quantitative, qualitative, mixed-methods, conceptual, opinion, and review articles were eligible. Findings were synthesized descriptively. Results: Sixteen sources published between 1992 and 2025 were included. Empirical studies reported changes in preferences, emotional and personality-related experiences, social and physical functioning, sexuality, identity and embodiment, sensory and memory-like experiences, and spirituality. Some changes were attributed by recipients to donor characteristics, whereas correspondences supported by independently obtained donor information were reported in a limited subset of studies. Proposed explanations ranged from psychological, pharmacological, physiological, and sociocultural processes to cellular, molecular, neurocardiac, energetic, and transpersonal hypotheses. Donor-transfer hypotheses were developed in the secondary literature and were not empirically established. Methodological quality varied substantially across sources. Conclusions: Post-transplant changes and perceived donor–recipient correspondences are meaningful phenomena, but current evidence does not support the transfer of donor memory, personality, or identity through the transplanted heart. Future prospective, longitudinal, and methodologically rigorous studies should distinguish reported experience, donor attribution, and proposed explanations.

Background: The integration of large language models (LLMs) into high-risk systems such as health care is accelerating. Rigorous evaluations aligned with emerging legislation are imperative prior to their incorporation into university educational platforms and clinical practice settings. Objective: The study aimed at implementing the first Regulation (European Union [EU]) 2024/1689–aligned methodological framework for a systematic, comprehensive, and dynamically adaptable language model evaluation, supporting decision-making in specialized health care management. Methods: We analyzed 15 LLMs and 2 small language models. A 7-domain, EU AI Act–aligned methodological framework was used. Feasibility was tested with a dataset of 32 multiparametric-engineered clinical prompts to elicit evaluation in 27 items, with Delphi expert responses as ground truth (available in the repository [32 Clinical Engineered Prompts and Delphi Panel's Responses]). Double-blind interdisciplinary evaluation on a 7-point Likert scale achieved high interrater reliability per model (Krippendorff α=.759 on average). A comprehensive analysis identified specific strengths and vulnerabilities. Safety was analyzed as alignment with both evidence-based nursing and novel structured assessments, including ethical resilience testing via progressive “jailbreaking.” Further novel structured assessments included reference classification, automated consistency, and NANDA-I (North American Nursing Diagnosis Association–International) terminology. Results: A stringent “Safety-Gatekeeper” domain immediately classified 11 of 17 language models as unsuitable due to critical failures in evidence-based alignment or ethical resilience. GPT-o1, GPT-4o, Gemini 2.0 Pro Experimental, and 3 Anthropic models surpassed minimum thresholds, permitting evaluation progression. Only Anthropic Sonnet variants achieved uniform “recommended” categorization. For instance, Claude 3.7 Sonnet (extended thinking) produced 75.9% of accurate, focused references, and achieved high average scores both in clinical safety and data security (mean 6.73, SD 0.23 and mean 6.83, SD 0.41, respectively). DeepSeek-R1, Perplexity Sonar, Mistral Large 2, and Qwen2.5-14B-Instruct failed to resist even explicit harmful prompts; Claude 3 Opus resisted both explicit harmful prompts and all jailbreak attempts, while demonstrating null sycophancy. Notably, Qwen2.5-14B-Instruct, operating locally, outperformed 4 of the 15 LLMs in multistep problems in nurse staffing optimization. NANDA-I diagnostic translation capability improved significantly with taxonomy-embedded contexts, with Gemini demonstrating adequate performance ( F ₁ -score=0.59, Mean Absolute Priority Distance=4.0). Conclusions: Regulatory-aligned LLM integration in pilot university hospitals can enhance health care education and decision-making across standardized taxonomy, evidence-based personalized clinical care algorithms, computational tasks, and nondiscrimination policies, under structured interdisciplinary expert oversight. The methodology demonstrates adaptability across various clinical settings. Future advancements should prioritize multimodal capabilities and locally functioning models, addressing resource disparities in line with Sustainable Development Goal 10, alongside operational resilience, and enhanced data protection.

In contemporary practice, artificial intelligence (AI) has evolved into a practical tool for clinical decision-making and diagnostic accuracy in healthcare. In wound management, AI-based tools enable the analysis of tissue types, precise size measurement, and automated boundary delineation. This narrative review aims to synthesize current evidence on the role of AI in nursing practice for wound management within palliative care settings. It evaluates technical applications, ethical implications, and nursing readiness to provide strategic directions for clinical practice and education. A comprehensive search was conducted in PubMed and ScienceDirect for literature published up to 12 February 2026, focusing on AI, machine learning, wound care, and nursing. The database search identified 1,228 records. After screening and eligibility assessment, approximately 20 studies relevant to the aims of this narrative review were thematically synthesized. Thematically synthesized findings suggest that AI technologies, particularly computer vision and predictive analytics, may enhance wound assessment accuracy and documentation. Mobile platforms support remote monitoring and telemedicine, which is crucial for rural and home-based palliative settings. While AI offers tools for prognostication and decision-making, nurses express a need for better training to address concerns regarding algorithmic bias, privacy, and the preservation of the relational aspect of care.AI serves as a powerful partner that supports nursing expertise rather than replacing it. In palliative care, its value lies in providing objective assessments and enabling proactive responses to patient needs while maintaining a focus on comfort and dignity. Integrating AI fundamentals into nursing curricula and professional development is essential. Furthermore, nurse participation in the design and governance of AI ensures they align with professional values and clinical workflows.