A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
- 1Ant Group
- 2Zhejiang University
Abstract
With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-purpose models to medical-specialized applications. Firstly, we delve into the foundational technology of Med-LLMs, indicating how general models can be progressively adapted and refined for the complicated medical tasks. Secondly, the wide-ranging applications of Med-LLMs are investigated across various healthcare domains, as well as an up-to-date review of existing Med-LLMs. The transformative impact of these models on daily medical practice is evident through their ability to assist clinicians, educators, and patients. Recognizing the importance of responsible innovation, we discuss the challenges associated with ensuring fairness, accountability, privacy, and robustness. Ethical considerations, rigorous evaluation methodologies, and the establishment of regulatory frameworks are crucial for building trustworthiness in the real-world system. We emphasize the need for ongoing scrutiny and development to maintain high standards of safety and reliability. Finally, we anticipate possible future trajectories for Med-LLMs, identifying key avenues for prudent expansion. By consolidating these insights, our review aims to provide professionals and researchers with a thorough understanding of the strengths and limitations of Med-LLMs, fostering a balanced and ethical approach to their integration into the healthcare ecosystem.
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BibTeX
@article{liu2026survey,
title = {{A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions}},
author = {Lei Liu and Xiaoyan Yang and Junchi Lei and Yue Liang Shen and Jian Wang and Peng Wei and Zhixuan Chu and Zhan Qin and Kui Ren},
journal = {IEEE Transactions on Knowledge and Data Engineering},
year = {2026},
month = jul,
doi = {10.1109/tkde.2026.3709941},
url = {https://doi.org/10.1109/tkde.2026.3709941},
note = {CC BY},
}
RIS
TY - JOUR
TI - A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
AU - Lei Liu
AU - Xiaoyan Yang
AU - Junchi Lei
AU - Yue Liang Shen
AU - Jian Wang
AU - Peng Wei
AU - Zhixuan Chu
AU - Zhan Qin
AU - Kui Ren
PY - 2026
DA - 2026/07/03
JO - IEEE Transactions on Knowledge and Data Engineering
DO - 10.1109/tkde.2026.3709941
UR - https://doi.org/10.1109/tkde.2026.3709941
AB - With the advent of Large Language Models (LLMs), medical artificial intelligence (AI) has experienced substantial technological progress and paradigm shifts, highlighting the potential of LLMs to streamline healthcare delivery and improve patient outcomes. Considering this rapid technical progress, in this survey, we trace the recent advances of Medical Large Language Models (Med-LLMs), including the background, key findings, and mainstream techniques, especially for the evolution from general-purpose models to medical-specialized applications. Firstly, we delve into the foundational technology of Med-LLMs, indicating how general models can be progressively adapted and refined for the complicated medical tasks. Secondly, the wide-ranging applications of Med-LLMs are investigated across various healthcare domains, as well as an up-to-date review of existing Med-LLMs. The transformative impact of these models on daily medical practice is evident through their ability to assist clinicians, educators, and patients. Recognizing the importance of responsible innovation, we discuss the challenges associated with ensuring fairness, accountability, privacy, and robustness. Ethical considerations, rigorous evaluation methodologies, and the establishment of regulatory frameworks are crucial for building trustworthiness in the real-world system. We emphasize the need for ongoing scrutiny and development to maintain high standards of safety and reliability. Finally, we anticipate possible future trajectories for Med-LLMs, identifying key avenues for prudent expansion. By consolidating these insights, our review aims to provide professionals and researchers with a thorough understanding of the strengths and limitations of Med-LLMs, fostering a balanced and ethical approach to their integration into the healthcare ecosystem.
SN - 1041-4347
SN - 1558-2191
SN - 2326-3865
ER -