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…
Medical large language models have potential to streamline healthcare delivery and improve patient outcomes by assisting clinicians, educators, and patients in daily practice.
Trustworthiness remains constrained by unresolved needs for safety, evaluation, and regulation.
Evidence
- Peer-reviewedIEEE Transactions on Knowledge and Data Engineering2026-01-01
How should this claim be treated?
Truvace Impact Record TRV-2026-0398, v1: “A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions.” Truvace, 2026-07-20. /record/TRV-2026-0398 (accessed at citation time). sha256 3430496987af677e…
Calibration history
Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.
Certified into the record
How to verify without trusting this page
Fetch the canonical text of any version from /api/record/TRV-2026-0398 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.