At the 2026 Global Digital Economy Conference in Beijing, the conversation surrounding artificial intelligence in healthcare shifted from mere accuracy to the more rigorous standard of 'auditability.' Hu Haoyuan, head of smart algorithms at JD Health, highlighted a growing divergence between general-purpose large language models and specialized medical applications. While general AI focuses on scale and fluid conversation, the medical sector is entering a 'deep-water zone' where the cost of error is absolute, demanding a shift toward extreme controllability.
Central to this shift is a critique of the widely used 'Chain of Thought' (CoT) reasoning mechanism. Hu argues that CoT is essentially a post-hoc narrative: a model generates a conclusion first and then constructs a plausible-sounding explanation. In a clinical setting, this can be dangerously deceptive, as a model might provide a logically sound justification for a fundamentally incorrect diagnosis. To combat this, industry leaders are advocating for 'Action Flow Tracks'—reasoning expressed as executable, testable code.
Under this new paradigm, medical AI would not merely describe its thoughts; it would execute a sequence of verifiable tool calls, such as loading imaging sequences, selecting regions of interest, and performing specific measurements. Because code either runs correctly against a ground truth or fails with a specific error, it leaves no room for the 'hallucinations' or fabrications that plague current generative models. This 'executable reasoning' ensures that every step of a diagnosis is replayable, verifiable, and fully auditable by human practitioners.
However, technical rigor is only half the battle. Wang Shihe, chairman of Shanghai Yinghe Yimai, emphasized that the true value of medical AI lies in its integration into the existing clinical workflow, specifically in the 'order-scan-diagnose' chain. As imaging technologies like CT and MRI iterate rapidly, doctors often struggle to select the most appropriate test. Ensuring the 'correctness of the order' is now viewed as the primary hurdle for AI to clear if it is to gain widespread hospital adoption and financial viability.
Currently, the adoption of these tools in China is seeing a clear divide. Tier-1 'top-tier' hospitals are aggressively building internal technical teams and pursuing private, on-premise deployments to safeguard data. While the commercial model for these services is still being explored, the trend toward private infrastructure suggests that the future of Chinese medical AI will be localized, secure, and built upon transparent, code-based logic rather than the black-box narratives of the past.