arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision by applying Medical Sparse Autoencoders (MedSAEs) to the latent space of MedCLIP, a vision-language model trained on chest radiographs and reports. To quantify interpretability, we propose an evaluation framework that combines correlation metrics, entropy analyses, and automated neuron naming via the MedGemma foundation model. Experiments on the CheXpert dataset show that MedSAE neurons achieve h
The proliferation of complex AI models necessitates advanced interpretability techniques to ensure their safe and effective deployment, particularly in sensitive sectors like healthcare.
Improving the interpretability of medical AI models like MedCLIP is critical for building trust, enabling regulatory acceptance, and facilitating effective human-AI collaboration in diagnostics.
The introduction of MedSAE and its evaluation framework provides a standardized and robust method for dissecting and understanding the internal workings of vision-language models in medical imaging.
- · Medical AI developers
- · Healthcare providers
- · Regulatory bodies
- · Patients
- · Black-box AI models in healthcare
Increased adoption and trustworthiness of AI in medical diagnostics due to enhanced interpretability.
Faster development and deployment cycles for regulated AI applications as interpretability challenges are reduced.
The development of new AI architectures specifically designed for inherent interpretability from the ground up.
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Read at arXiv cs.AI