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On May 8, 2026, the China National Intellectual Property Administration granted the invention patent Method, System and Application for Generating Compound Intervention Schemes Based on Pre-trained Models (CN117766054B). This patent corresponds to our technical exploration around SEMO: how to put compounds, protein-protein interaction networks, individual omics data, and intervention scheme generation into a computable framework. I prefer to understand it as a methodological shift from checking whether an indicator is below the average to examining whether there exists an intervenable gap within the individual network.

Over the past decade, AI in medicine has followed a clear migration path: Medical AI solves the problem of understanding medical facts, AI drug discovery solves the problem of finding intervention tools, and medical world models solve the problem of verifying intervention outcomes. The first two primarily change efficiency; the latter may change the medical paradigm itself. Medical AI is the past, AI drug discovery is the present, medical world models are the future.

What medical AI truly lacks may not be yet another larger model, but an ImageNet-like infrastructure — a foundational data and evaluation system that systematically records current biological states, intervention actions, and subsequent state changes. The next decade of medical AI does not lack large models. What is truly missing is a shared infrastructure for biological state transitions. Whoever defines state, action, and transition may define the underlying coordinate system of next-generation AI in medicine.

Medical AI cannot rely solely on the external guardrails of harness engineering. A true medical world model must enter the internal architecture of state-action-transition-feedback, making biological state changes representable, deducible, auditable, and correctable. Harness engineering controls the AI system from outside. Steerable world modeling structures biomedical reasoning from inside.

Prediction models answer what will happen; world models answer what will happen if I take this action. Medical AI must learn not just to predict, but to reason about the consequences of actions. The core structure of a world model consists of five elements: State, Action, Transition, Objective, and Feedback. The future competition in medical AI is not just about bigger models, more data, or better predictions, but about who can represent living systems as explainable, deducible, feedback-driven, and calibratable world models.

If a person stood before you and a world model stood beside them, would you trust it with your health? SEWO (Steerable Medicine World Model) argues that the ultimate mission of a world model is not to be a more accurate oracle, but to be a reliable steed — the doctor gives direction, and the model navigates uncertain terrain on its own. Prediction is the means; reliable state transition is the goal.

The same inflammation concept, measured via serum protein, shows weak correlation with brain health. Switch to a DNA methylation surrogate, and the correlation jumps 6.4x. The concept didn't change — the 'shutter speed' did. This is precisely the core insight behind EvoSika's methodology.

The mission of AI4S should not be using AI to accelerate existing science, but using AI to reinvent how science organizes knowledge. The content of a concept depends on the context of inquiry — we need an operating system for concepts.