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MedExpMem: Adapting Experience Memory for Differential Diagnosis

Summary

MedExpMem is a novel experience memory framework that enables medical vision-language models to accumulate differential diagnosis expertise from diagnostic failures, achieving up to 7% accuracy improvement across 11 radiology subspecialties.

SourcearXiv Machine LearningAuthor: Qianhan Feng, Zhongzhen Huang, Yakun Zhu, Yannian Gu, Winnie Chiu Wing Chu, Xiaofan Zhang, Qi Dou
MedExpMem: Adapting Experience Memory for Differential Diagnosis
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[2605.22872] MedExpMem: Adapting Experience Memory for Differential Diagnosis

[Submitted on 20 May 2026]

Title:MedExpMem: Adapting Experience Memory for Differential Diagnosis

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Abstract:Experienced physicians develop diagnostic expertise through clinical practice, acquiring not only disease knowledge but also the ability to differentiate confusable conditions. Current medical vision-language models (VLMs) lack this capability -- their parameters encode static knowledge that does not evolve across diagnostic encounters. We propose MedExpMem, an experience memory framework enabling VLM-based diagnostic agents to accumulate differential diagnosis expertise. Unlike retrieval-augmented generation, which retrieves encyclopedic disease descriptions, MedExpMem memorizes discriminative experience derived from the agent's own diagnostic failures and organizes them as pairwise differential notes encoding key discriminators, actionable decision rules and reasoning error patterns. The framework adopts a two-phase construction process mirroring physician learning: initial practice exposes knowledge gaps, and reflective re-diagnosis refines understanding. When encountering new cases, the agent retrieves experience memory to guide differential reasoning. We evaluate MedExpMem on a radiology benchmark spanning 11 subspecialties. Results demonstrate consistent accuracy improvements, maximum 7.0%, across diverse models and scales. Analytical experiments validate experience quality and robustness, demonstrating MedExpMem as a competitive method addresses medical adaptation needs beyond the reach of parameteric learning.

Comments: MICCAI 2026 Early Accept. Submission Version

Subjects:

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)

Cite as: arXiv:2605.22872 [cs.LG]

(or arXiv:2605.22872v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2605.22872

arXiv-issued DOI via DataCite

Submission history

From: Qianhan Feng [view email] [v1] Wed, 20 May 2026 01:42:53 UTC (1,205 KB)

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Key points and analysis

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Key points

  • Current medical VLMs lack dynamic learning capabilities; MedExpMem mimics physician experience accumulation to improve differential diagnosis.
  • The framework learns from diagnostic failures, storing key discriminators, decision rules, and reasoning error patterns.
  • It adopts a two-phase construction: initial practice exposes knowledge gaps, and reflective re-diagnosis refines understanding.
  • Evaluated on 11 radiology subspecialties, it achieves consistent accuracy improvements up to 7.0% across diverse models.

Highlights and analysis are generated automatically and may contain errors. Check the original source.

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