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cs.AI2026

World Feedback for Clinical Agents: Diagnosing RL in FHIR Environments

Ananya Mantravadi, Harshit Rajgarhia, Prasanna Desikan +1

Clinical protocol-execution tasks -- checking a lab value, applying a threshold, placing a correctly structured FHIR order -- are natural candidates for RL from world feedback: onc…

cs.AI2026

LLM Doesn't Know What It Doesn't Know: Detecting Epistemic Blind Spots via Cross-Model Attribution Divergence on Clinical Tabular Data

Akshat Dasula, Prasanna Desikan, Jaideep Srivastava

Large language models (LLMs) are increasingly applied to structured clinical data, yet whether they can recognize the limits of their own knowledge on such tasks remains unexplored…

cs.AI2026

CareTransition-Audit: A Benchmark to Audit Discharge Summaries for Efficient Care Transitions

Akshat Dasula, Prasanna Desikan, Jaideep Srivastava +2

Incomplete or inconsistent discharge documentation drives care fragmentation and avoidable readmissions. Despite its critical role in patient safety, auditing discharge summaries r…

cs.AI2026

Measuring What Matters: Benchmarking Generative, Multimodal, and Agentic AI in Healthcare

Prasanna Desikan, Harshit Rajgarhia, Shivali Dalmia +1

AI models are increasingly deployed in live clinical environments where they must perform reliably across complex, high-stakes workflows that standard training and validation datas…

cs.AI2026

Systematic Evaluation of Large Language Models for Post-Discharge Clinical Action Extraction

Shivali Dalmia, Ananya Mantravadi, Prasanna Desikan

The work in this paper evaluates zero-shot and few-shot large language models (LLMs) for safety-critical clinical action extraction using the CLIP discharge-note dataset, with part…