Current evidence indicates that LLMs have substantial potential to enhance healthcare delivery, research, and personalized medicine, but they should currently be regarded as supportive tools rather than autonomous clinical decision-makers.
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.
Antoni Klamka, Paulina Kawalec, Kamil Bronikowski et al.· Quality in Sport· 0 citations
This paper conducts a comprehensive analysis of evaluation methods, deployment processes, and governance strategies for LLMs in the healthcare field, focusing on three key issues: model version drift, multilingual external validation, and prompt injection security governance.
Song-Bin Guo, Sui-Xing Zhong, Yixian Ma et al.· International Journal of Sur...· 0 citations
The integration of Large Language Models (LLMs) into healthcare is poised to revolutionize various aspects of medical practice, including clinical decision‐making, patient care, and medical research. This review explores the applications of LLMs such as ChatGPT‐3, ChatGPT‐4, and BERT in healthcare, focusing on their potential to enhance disease diagnosis, treatment planning, and personalized care. The paper presents a comprehensive bibliometric analysis of the growing body of research, highlighting key trends, influential authors, institutions, and geographical contributions. Despite their promise, significant challenges remain, including model accuracy, data privacy, ethical concerns, and the need for domain‐specific fine‐tuning. This review examines the moral and technical challenges associated with deploying LLMs in healthcare, including biases, a lack of transparency, and issues related to model interpretability. The paper further emphasizes the importance of robust frameworks for ensuring ethical usage. It proposes future research directions to address these challenges, including the development of specialized healthcare models, enhanced transparency, and improved integration into clinical workflows. Ultimately, this review aims to inform healthcare professionals, researchers, and policymakers about the transformative potential of LLMs in healthcare while underscoring the critical issues that must be overcome for their widespread adoption.
This article is categorized under:
Application Areas > Health Care
Fundamental Concepts of Data and Knowledge > Big Data Mining
Technologies > Artificial Intelligence
Md Belal Bin Heyat, A. Rehman, H. M. Zeeshan et al.· WIREs Data Mining and Knowle...· 0 citations
The authors' analysis reveals that LLMs demonstrate promising capabilities in processing textual and visual data related to various liver diseases, including hepatocellular carcinoma, cirrhosis, and non-alcoholic fatty liver disease, but study heterogeneity and significant challenges remain regarding accuracy, reliability, and safety.
T. Suenghataiphorn, Narisara Tribuddharat, Pojsakorn Danpanichkul et al.· Hepatology Forum· 0 citations
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.
Noah Wright· International Journal of Mod...· 0 citations
A conceptual Clinical Co-pilot Framework is proposed to position GenAI as a collaborative partner that supports clinicians rather than replaces them, which provides a conceptual basis for future empirical validation and may help inform the responsible implementation of GenAI in healthcare.
Lina Cheng, Chia-Yu Hung, Te-Nien Chien· International Journal of Adv...· 0 citations