Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models

These findings support the development of more interpretable and clinically applicable medical AI systems, and offer a scalable foundation for knowledge-intensive reasoning tasks in other domains.

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Publication Details

Journal
Journal of Craniofacial Surgery
Published
2026-01-13
DOI
https://doi.org/10.1097/scs.0000000000012355
Primary Topic
Topic Modeling
Type
article
Field-Weighted Citation Impact
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article

Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models

Jingwei Wu, Lei Ji, Heqing Lian
Journal of Craniofacial Surgery
Topic Modeling
article

Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models

Jingwei Wu, Lei Ji, Heqing Lian
article en

Abstract

These findings support the development of more interpretable and clinically applicable medical AI systems, and offer a scalable foundation for knowledge-intensive reasoning tasks in other domains.

Journal of Craniofacial Surgery
Beijing University of Chinese Medicine (CN), Cognitive Neuroimaging Lab (FR)
Peace, Justice and strong institutions
Openalex Percentile: Top 89%
Topic Modeling
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Active Knowledge Retrieval to Reduce Hallucinations and Enhance Factual Accuracy in Large Medical Reasoning Models — Jingwei Wu, Lei Ji, et al. · Journal of Craniofacial Surgery (2026) | TGRS Research Map | TGRS