Author

L. Adams

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Open access Sep 2026

Improving Reliability and Explainability of Medical Question Answering Through Atomic Fact-Checking in Retrieval-Augmented Large Language Models: Creation and Validation Study

Abstract Background Large language models (LLMs) exhibit extensive medical knowledge but are prone to hallucinations and show low fact-level explainability, limiting clinical adoption and regulatory compliance. Existing approaches, such as retrieval-augmented generation, partially address these issues by grounding answ...

J. Vladika, A. Domres, Mai Q. Nguyen et al. · Journal of Medical Internet... · 0 citations

Preprint Jul 2026

RadPRISM: Schema-stratified radiology-report supervision for concept-disentangled image representations and visual grounding

Vision-language pretraining learns rich medical image representations from radiology reports, but previous model variants commonly operate within a single shared embedding space, so concept-level structure and interpretability must be recovered post hoc, limiting model transparency and, hence, clinical utility. We intr...

Fabian Drexel, Marlene Fritzsche, Era Stambollxhiu et al. · 0 citations

#generative ai Editorial Sep 2026

Human-AI Collaboration in Radiology: The Blind Spots.

A narrative synthesis of the human-AI interaction and radiology AI literature highlighted three underrecognized determinants of successful human-AI collaboration in radiology, and concrete research directions are proposed to bridge the gap between algorithmic capabilities and clinical utility.

Su Hwan Kim, L. Adams, B. Wiestler et al. · Radiology: Artificial Intell... · 0 citations