Problem
Research questions and friction points this paper is trying to address.
Medical AI
patient state change
intervention
statistical models
mechanistic models
Life Operators: a self-evolving framework for multiscale life modelling
🤖 AI Summary
论文提出Life Operators框架，通过定义感知、演化和生成操作符来模拟患者状态变化，解决现有模型无法统一表示患者状态及跨尺度耦合的问题。
📝 Abstract
Medical AI is moving beyond recognition towards clinical dialogue and longitudinal prediction. Yet a central question remains: how would a patient's state change under intervention? Statistical models learn future observations, whereas mechanistic models describe selected processes. Neither provides a common framework for representing patient state, coupling scales or revising failed assumptions. We propose Life Operators: task-bounded mappings that define three scientific roles. Perception operators infer task-relevant biological states from multimodal observations, Evolution operators propagate these states under natural or intervention-conditioned dynamics, and Generation operators map them to measurable signals. Each role may be realised by equations, statistical models, neural networks or hybrids. Bridge operators connect components with different variables, scales and time steps. Selected operators and bridges form task-specific Operator Graphs containing the smallest set of states and mechanisms sufficient for a declared claim. This modular structure also makes scientific revision localisable. An AI co-scientist may propose changes to states, operators, bridges or graph structure, while independent evidence determines which variants are retained, restricted or retired. Over time, validated components could accumulate into broader multiscale models of the human body and provide a computational foundation for medical artificial superintelligence.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Life Operators
multiscale life modelling
task-bounded mappings
modular structure
AI co-scientist
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