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TRUVACE RECORD VERSION
record: TRV-2026-0526
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-24T00:26:10.911944Z
status: published
lens: trace
sector: health
headline: Large Language Models in Medicine: Applications, Challenges, and Future Directions
dek: In recent years, large language models (LLMs) represented by GPT-4 have developed rapidly and performed well in various natural language processing tasks, showing great potential and transformative impact. The medical field, due to its vast data information as well as complex diagnostic and treatment processes, is undoubtedly one of the most promising areas for the application of LLMs. At present, LLMs has been gradually implemented in clinical practice, medical research, and medical education. However, in pract…
gain_title: Large language models represented by GPT-4 have been gradually implemented in clinical practice, medical research, and medical education with transformative potential for healthcare delivery.
problem_title: Medical LLMs still face numerous challenges in practical applications, including hallucination, limited interpretability, and ethical concerns that hinder widespread use.
trace_subject: use of large language models in medicine and global healthcare
gain_reading: Large language models represented by GPT-4 have been gradually implemented in clinical practice, medical research, and medical education with transformative potential for healthcare delivery.
gain_evidence: LLMs has been gradually implemented in clinical practice, medical research, and medical education | showing great potential and transformative impact
problem_reading: Medical LLMs still face numerous challenges in practical applications, including hallucination, limited interpretability, and ethical concerns that hinder widespread use.
problem_evidence: medical LLMs still face numerous challenges, including the phenomenon of hallucination, interpretability, and ethical concerns
quick_read: This review describes the rapid development of large language models such as GPT-4 and their growing use in medicine. By May 2025, the authors state that LLMs have been gradually implemented in clinical practice, medical research, and medical education, while still facing challenges of hallucination, interpretability, and ethics.

The topic matters because medicine involves vast data and complex diagnostic and treatment processes where LLMs could transform care, research, and training. What remains uncertain is how to ensure safety and effectiveness at scale, as the authors note that standardized evaluation, multimodal capabilities, and cross-disciplinary collaboration still require in-depth exploration before widespread application.
limitation: The review notes that widespread application is not yet realized and that further work is needed on evaluation, multimodality, and collaboration.
tag: Automated dual reading
key_points: LLMs represented by GPT-4 have developed rapidly and performed well in various natural language processing tasks | Medical field identified as promising area due to vast data and complex diagnostic and treatment processes | LLMs have been gradually implemented in clinical practice, medical research, and medical education | Medical LLMs face challenges including hallucination, interpretability, and ethical concerns | Future work needed on standardized evaluation frameworks, multimodal LLMs, and multidisciplinary collaboration
rundown: The source is a peer-reviewed review published May 31, 2025, focusing on GPT-4 class models and their performance in natural language processing tasks applied to medicine.

It characterizes current status as gradual implementation across three domains: clinical practice, medical research, and medical education, while listing hallucination, interpretability, and ethical concerns as active challenges.

It concludes that future directions should include standardized evaluation frameworks, multimodal LLMs, and multidisciplinary collaboration to promote development and transformation in global healthcare.
sources:
- peer_reviewed | International Journal of Medical Sciences | https://doi.org/10.7150/ijms.111780 | 2025-05-31
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