Toward Clinically Explainable AI for Medical Diagnosis: A Foundation Model with Human-Compatible Reasoning via Reinforcement Learning

Explainability is essential for clinical adoption: The paper identifies the "black-box nature" of AI as a critical barrier preventing doctors from using AI diagnostic tools in practice. DeepMedix-R1 addresses this by showing its step-by-step reasoning alongside diagnoses, making it possible for clinicians to verify why the AI reached a particular conclusion.

AI reasoning can be trained to match clinical standards: Using reinforcement learning with "grounded rewards," the researchers aligned the model's reasoning process with what clinicians consider medically plausible. Clinical experts formally reviewed the outputs and strongly preferred DeepMedix-R1's explanations over a competing advanced model (Qwen2.5-VL-7B), suggesting that AI reasoning quality can be systematically improved to meet professional standards.

Transparency doesn't require sacrificing accuracy: DeepMedix-R1 achieved "substantial" improvements over other foundation models in both diagnostic accuracy and report generation tasks, demonstrating that explainability and performance are not competing goals—the model can do both well simultaneously.

Expert validation is crucial for responsible AI deployment: Rather than relying solely on automated metrics, the researchers conducted formal clinical expert review to assess interpretability and utility. This human-in-the-loop validation approach provides stronger evidence that the AI system is genuinely useful in real medical contexts.