aceA Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions
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…
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This peer-reviewed survey from January 2026 traces recent advances in Medical Large Language Models, from background and adaptation techniques for general-purpose models to medical-specialized applications and a review of existing systems across healthcare domains.
It matters because it links technical progress to daily medical practice while highlighting that trustworthiness depends on unresolved fairness, privacy, and robustness challenges; uncertainty remains about how evaluation and regulatory frameworks will be established to ensure safe real-world integration.
- Survey traces evolution from general-purpose LLMs to medical-specialized applications.
- Reviews foundational adaptation techniques for complicated medical tasks.
- Investigates wide-ranging applications across healthcare domains and existing Med-LLMs.
- Discusses challenges of fairness, accountability, privacy, and robustness for real-world deployment.
Medical large language models have potential to streamline healthcare delivery and improve patient outcomes by assisting clinicians, educators, and patients in daily practice.
The rundown
The survey outlines how general models are progressively adapted and refined for complicated medical tasks, then reviews applications across healthcare domains and existing Med-LLMs.
It frames responsible innovation around fairness, accountability, privacy, and robustness, calling for ethical considerations, rigorous evaluation, and regulatory frameworks to ensure safety and reliability.
Sources
- Peer-reviewedIEEE Transactions on Knowledge and Data Engineering2026-01-01
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