Timely Clinical Diagnosis through Active Test Selection

TL;DR

This paper introduces ACTMED, a framework combining Bayesian Experimental Design and large language models to enable adaptive, resource-aware clinical diagnosis that improves accuracy and interpretability while involving clinicians in decision-making.

Contribution

The paper presents ACTMED, a novel framework that uses LLMs and Bayesian design to emulate real-world diagnostic reasoning and optimize test selection in clinical settings.

Findings

Improves diagnostic accuracy over static methods

Reduces resource use in test selection

Enhances interpretability and clinician involvement

Abstract

There is growing interest in using machine learning (ML) to support clinical diagnosis, but most approaches rely on static, fully observed datasets and fail to reflect the sequential, resource-aware reasoning clinicians use in practice. Diagnosis remains complex and error prone, especially in high-pressure or resource-limited settings, underscoring the need for frameworks that help clinicians make timely and cost-effective decisions. We propose ACTMED (Adaptive Clinical Test selection via Model-based Experimental Design), a diagnostic framework that integrates Bayesian Experimental Design (BED) with large language models (LLMs) to better emulate real-world diagnostic reasoning. At each step, ACTMED selects the test expected to yield the greatest reduction in diagnostic uncertainty for a given patient. LLMs act as flexible simulators, generating plausible patient state distributions and supporting belief updates without requiring structured, task-specific training data. Clinicians can remain in the loop; reviewing test suggestions, interpreting intermediate outputs, and applying clinical judgment throughout. We evaluate ACTMED on real-world datasets and show it can optimize test selection to improve diagnostic accuracy, interpretability, and resource use. This represents a step toward transparent, adaptive, and clinician-aligned diagnostic systems that generalize across settings with reduced reliance on domain-specific data.