Large language models in clinical nutrition practice: opportunities, risks, and governance

Evidence, use cases, and implementation safeguards of large language models in primary care

Recent developments in large language models (LLMs) have created new opportunities to support primary care, where much of clinical work is text-mediated. This narrative review synthesizes evidence on LLM applications relevant to primary care workflows and summarizes implementation safeguards. Across studies, the most consistently supported near-term value is workflow augmentation, particularly documentation and inbox management (e.g., drafting portal replies and summarizing information for clinician review) and communication support, where benefits are reported primarily as process endpoints (time, acceptability, perceived communication quality) rather than hard patient outcomes. Evidence for improvements in clinician diagnostic reasoning, treatment planning, and downstream patient outcomes is more limited and context-dependent, and many evaluations remain simulated or conducted in adjacent settings, limiting generalizability to routine primary care. Accordingly, potential roles in population health and cost reduction should be treated as hypothesis-generating and evaluated prospectively. Challenges related to privacy, security, transparency, and model reliability shape organizational governance requirements and evolving regulatory expectations for the clinical use of generative AI in primary care. We emphasize a pragmatic adoption approach: prioritize high-volume, lower-risk clerical and communication workflows; maintain clinician verification and accountability; and apply governance and equity safeguards (e.g. privacy, security, transparency, auditability, monitoring for drift and error) before scale-up. Christof et al. provide a narrative review that synthesizes evidence on LLM applications relevant to primary care workflows and summarizes implementation safeguards. They highlight the remaining need to demonstrate improved patient outcome of clinical improvement in many studies and outline a pragmatic adoption approach in practice.

Large language models in sports cardiology: Challenges, governance, and human-in-the-loop integration.

BACKGROUND Large language models (LLMs) are increasingly integrated into clinical artificial intelligence (AI) workflows, with emerging applications in medical documentation, information synthesis, guideline retrieval, and patient communication. Recent advances in foundation models, including multimodal systems, have further expanded these capabilities. However, current evidence derives predominantly from examination-style benchmarks, retrospective vignettes, and hospital-based populations, limiting its direct applicability to athlete-centered cardiovascular care. Sports cardiology represents a uniquely challenging environment for AI because of low disease prevalence, physiological-pathological overlap, longitudinal uncertainty, and eligibility-driven decision-making. MAIN BODY This narrative review critically examines the conceptual, technical, regulatory, and governance challenges associated with LLM deployment in sports cardiology. We argue that current evaluation paradigms inadequately capture the uncertainty-intensive nature of athlete cardiovascular assessment and propose the text-proxy paradigm, a governance-oriented framework in which LLMs operate as semantic integration layers that process clinician-verified and structured outputs generated by validated upstream systems rather than directly interpreting physiological signals. Within this architecture, LLMs may support documentation, longitudinal synthesis, guideline retrieval, contextual reasoning, and athlete communication under continuous human oversight. We further discuss uncertainty propagation across layered AI systems, workflow-oriented validation, regulatory implications, and a staged framework for responsible clinical implementation. CONCLUSION Future progress will depend less on increasingly capable models than on trustworthy clinical ecosystems that prioritize governance, calibration, transparency, auditability, and clinician oversight. Rather than serving as an early adopter of autonomous AI, sports cardiology may provide a benchmark for the responsible integration of foundation models into high-stakes clinical medicine.

Large Language Models in Medicine: Opportunities, Limitations, and Future Directions – A Scoping Review

A scoping review of 24 PubMed-indexed studies published between 2023 and 2026 was conducted to assess current applications, benefits, limitations, and future directions of LLMs in healthcare.

Large Language Models in Healthcare: Opportunities and Ethical Challenges

A thorough review of the developments in LLM technologies, their uses in clinical and administrative settings, as well as their ethical considerations are reviewed to suggest a conceptual structure for responsible implementation that will ensure both technological innovation and patient safety, as well as regulatory compliance and ethical health care practices.

Large Language Models in Dentistry: A Narrative Review of Reliability, Clinical Utility, and Safety Considerations

AI-Powered Prescription Error Detection Using Large Language Models (LLMs): A Systematic Review and Future Perspectives

It is concluded that LLM-based decision-support tools hold substantial promise as complementary — rather than autonomous — decision-support systems capable of transforming medication safety and pharmacy practice.