arXiv:2605.22872v1 Announce Type: new 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
The rapid advancement of AI in medicine, particularly vision-language models, necessitates new approaches to bridge the gap between static AI knowledge and dynamic human diagnostic expertise.
This development indicates progress towards more capable and adaptable AI systems in critical fields like healthcare, potentially enhancing diagnostic accuracy and efficiency.
AI models for medical diagnosis are evolving beyond mere knowledge retrieval to incorporate cumulative experience and differential diagnostic reasoning, mirroring human clinical development.
- · AI healthcare providers
- · Patients
- · Medical AI researchers
- · Healthcare technology companies
- · Medical AI models reliant solely on static knowledge
Improved diagnostic capabilities of AI systems in medical settings.
Increased trust and adoption of AI assistants by medical professionals for complex cases.
Re-evaluation of medical education and training to integrate human-AI collaborative diagnostic processes.
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Read at arXiv cs.LG