This review explores the transformative impact of Large Language Models on medical question-answering robots and addresses critical challenges faced by LLM-driven systems, such as limited interpretability, restricted applicability across medical domains, and serious concerns about patient privacy.
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper presents a comprehensive survey of various benchmark datasets used in medical LLM tasks. These datasets span multiple modalities including t...
L. K. Yan, Qian Niu, Ming Li et al.· Medicine Advances· 33 citations· ⚡1
A comprehensive framework for transforming LLMs into trustworthy medical specialists is presented, and the evolution toward agentic AI systems that orchestrate them for autonomous, collaborative clinical decision-making is explored.
Kiduk Kim, Jeong Min Song, Dong Yeong Kim et al.· Cell Reports Medicine· 0 citations
Although promising, LLM-based systems are not yet reliable enough for autonomous medical diagnosis, and multiple recommendations for future research are contained to ensure a high level of safety, transparency, and clinical applicability for LLMs and other AI/ML-related technologies and devices.
M. U. K. Gunawardhna, Pirunthavi Wijikumar, D. Weerasinghe· Sri Lankan Journal of Applie...· 1 citation
Smart Medical Microbiology showed strong domain adaptability in medical microbiology knowledge organization, semantic generation, and retrieval-augmented reasoning, which supports its potential use in educational support, infectious disease knowledge assistance, and retrieval-enhanced medical question answering.
Yong-Qian Gong, Ruiqiang Ma, Xicheng Wang et al.· Applied Informatics· 0 citations
This work presents MedProb, a lightweight probing framework that predicts multiple-choice Med-VQA answers from frozen VLM representations without free-text generation, and shows the probe can be extended to open-ended generation via a rejection-sampling scoring procedure.
Large Language Models (LLMs) have emerged as a transformative paradigm in Natural Language Processing (NLP), significantly advancing the capabilities of machines in understanding, generating, and reasoning with human language. Built upon transformer-based architectures and trained on massive datasets, LLMs have demonst...
Rampy, Anjana Kumari, Ranjit Singh· International journal of re...· 0 citations
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