# MedClarify: An information-seeking AI agent for medical diagnosis with case-specific follow-up questions

arXiv: https://arxiv.org/abs/2602.17308 · HTML: https://mlsanity.com/paper/2602.17308/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 5 of its 81 references that are in the map.
- First posted on arXiv 19 Feb 2026.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 5 of 5)

- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [Uncertainty of Thoughts: Uncertainty-Aware Planning Enhances Information Seeking in Large Language Models](https://mlsanity.com/paper/2402.03271/) (2024)
- [Bayesian Preference Elicitation with Language Models](https://mlsanity.com/paper/2403.05534/) (2024)
- [Trained on Tokens, Calibrated on Concepts: The Emergence of Semantic Calibration in LLMs](https://mlsanity.com/paper/2511.04869/) (2025)

## Built on by (most cited first, 4 of 4)

- [MoBayes: A Modular Bayesian Framework for Separating Reasoning from Language in Conversational Clinical Decision Support](https://mlsanity.com/paper/2604.20022/) (2026)
- [Beyond Information Seeking: Severity-Aware Question Supervision for Proactive Medical Dialogue](https://mlsanity.com/paper/2608.24521/) (2026)
- [HPOQuest: A Rare-Disease Diagnostic Agent Using Active Phenotype Acquisition](https://mlsanity.com/paper/2609.18431/) (2026)
- [From Hindsight to Foresight: Dissecting Clinical Evidence Acquisition in Large Language Models](https://mlsanity.com/paper/2610.18605/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used for diagnostic tasks in medicine. In clinical practice, the correct diagnosis can rarely be immediately inferred from the initial patient presentation alone. Rather, reaching a diagnosis often involves systematic history taking, during which clinicians reason over multiple potential conditions through iterative questioning to resolve uncertainty. This process requires considering differential diagnoses and actively excluding emergencies that demand immediate intervention. Yet, the ability of medical LLMs to generate informative follow-up questions and thus reason over differential diagnoses remains underexplored. Here, we introduce MedClarify, an AI agent for information-seeking that can generate follow-up questions for iterative reasoning to support diagnostic decision-making. Specifically, MedClarify computes a list of candidate diagnoses analogous to a differential diagnosis, and then proactively generates follow-up questions aimed at reducing diagnostic uncertainty. By selecting the question with the highest expected information gain, MedClarify enables targeted, uncertainty-aware reasoning to improve diagnostic performance. In our experiments, we first demonstrate the limitations of current LLMs in medical reasoning, which often yield multiple, similarly likely diagnoses, especially when patient cases are incomplete or relevant information for diagnosis is missing. We then show that our information-theoretic reasoning approach can generate effective follow-up questioning and thereby reduces diagnostic errors by ~27 percentage points (p.p.) compared to a standard single-shot LLM baseline. Altogether, MedClarify offers a path to improve medical LLMs through agentic information-seeking and to thus promote effective dialogues with medical LLMs that reflect the iterative and uncertain nature of real-world clinical reasoning.