From Prediction to Intervention: The Evolution of AI in Biomedicine

TL;DR

AI in biomedicine is evolving from predictive models based on historical data to interventional models that simulate biological responses to novel therapies, enabling better decision-making.

Contribution

The paper introduces a conceptual framework distinguishing observational and interventional AI, emphasizing the need for models that explicitly represent biological dynamics and responses to interventions.

Findings

Predictive architectures are limited to observational data and cannot generalize to new interventions.

A new framework for interventional AI enables simulation of biological responses under perturbation.

Transitioning to interventional models shifts value creation from prediction to decision support.

Abstract

Artificial intelligence has advanced rapidly in biomedicine through large-scale multimodal data integration, enabling increasingly accurate prediction of clinical outcomes and patient stratification. These systems, however, remain fundamentally observational: they learn statistical associations from historical data and operate within previously observed biological and clinical states, limiting their ability to generalize to novel therapies or unobserved interventions. We argue that AI in biomedicine is undergoing a structural transition. As biomedical decision-making increasingly depends on reasoning about intervention rather than extrapolation from past observations, predictive architectures become structurally insufficient. Systems that learn from historical data cannot, by construction, represent how biological systems evolve under perturbation, and therefore cannot reliably support decision-making in the presence of novel interventions. We introduce a conceptual framework distinguishing observational and interventional intelligence and define disease-level models as systems that explicitly represent the state, dynamics, and intervention response of biological processes. These models enable a shift from inference to simulation -- reasoning about what will happen under intervention rather than what is likely based on the past. This transition also implies a shift in where value is created: from data processing and prediction toward systems that support and define decision-making under intervention. It follows directly from the structure of biomedical decision-making and defines the next stage of AI in medicine. Systems that cannot model intervention will be structurally excluded from decision-making.