Open-source LLMs, when fine-tuned using labeled data, can effectively automate the ascertainment of key radiophenotypic variables using only the impression section of radiology reports, without the full report text, suggesting that these models may provide a scalable approach for phenotypic characterization of patients...
S. A. Naqvi, I. Riaz, Amir Saeidi et al.· JCO Clinical Cancer Informat...· 0 citations
CAST, a critique-aware training framework that converts sparse task outcomes into action-level supervision for critique learning and policy optimization, is presented, demonstrating that critique-aware training improves the robustness of LLM agents in realistic dynamic environments.
Amir Saeidi, Zeng Zhang, Rishi Singh et al.· 1 citation
COGTRL is proposed, a trajectory-level reinforcement learning framework that trains LLMs to emulate cognitively grounded reasoning by jointly optimizing cognitive traces and the scientific steps produced in an interleaved manner.
Shrinidhi Kumbhar, Santosh Mashetty, Divij Handa et al.· 1 citation
Evaluating and monitoring reasoning fidelity should be a prerequisite for safe deployment of LLMs in oncology decision support, as endpoint accuracy alone may mask clinically meaningful reasoning failures.
Matthew Kenaston, U. Ayub, Mihir Parmar et al.· npj Digital Medicine· 0 citations
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