PACT: Learning Diverse Diagnostic Strategies via Privileged Synthesis and Branch Consensus

Artificial intelligence (AI)-based diagnosis has substantial potential to improve the accessibility, efficiency and quality of clinical care. Although existing AI diagnostic methods have demonstrated strong medical reasoning capabilities, most follow a single, fixed reasoning paradigm. In real-world clinical practice, however, physicians may adopt different strategies when questioning patients, gathering clinical evidence and formulating diagnoses. Here we introduce PACT (Periodic Anchor Consensus Training), a model-merging framework that separately learns paradigm-specific LoRA Branches and periodically merges them into a shared Anchor through sign consensus, enabling multiple diagnostic paradigms to be integrated while reducing cross-paradigm interference. Because high-quality clinical dialogues reflecting distinct diagnostic strategies are difficult to collect at scale, we further develop DPS (Doctor Patient Supervisor), which distils dialogues from large language models by simulating four diagnostic strategies; DPS uses complete electronic medical records (EMRs) for quality control while restricting the simulated doctor to patient-visible information, thereby preventing hidden clinical answers from leaking into the dialogues. We collect 10,000 de-identified internal-medicine EMRs to construct a dynamic, multi-turn diagnostic benchmark and show that PACT achieves state-of-the-art performance against strong baselines.