AI in Biomedicine Shifts from Prediction to Intervention
A new paper on arXiv argues that artificial intelligence in biomedicine is undergoing a structural transition from predictive to interventional systems. Current AI models, which learn statistical associations from historical data, are fundamentally observational and cannot generalize to novel therapies or unobserved interventions. As biomedical decision-making increasingly requires reasoning about intervention, predictive architectures become structurally insufficient. The paper contends that systems learning from past data cannot represent how biological systems evolve under perturbation, limiting their reliability for decision-making involving novel interventions.
Key facts
- arXiv paper 2605.16293v1 discusses AI in biomedicine.
- Current AI systems are observational, learning from historical data.
- These systems cannot generalize to novel therapies or unobserved interventions.
- Biomedical decision-making increasingly requires reasoning about intervention.
- Predictive architectures are structurally insufficient for this new paradigm.
- Systems learning from past data cannot represent biological evolution under perturbation.
- The paper argues AI in biomedicine is undergoing a structural transition.
- The transition is from prediction to intervention.
Entities
Institutions
- arXiv