AI Advances and Applications Virtual Seminar Series
The Big Short with AI in Biomedical Sciences: When Actions Don’t Follow Predictions by Dr. Mattia Prosperi
Associate Professor, Preeminence Faculty, Dept. of Epidemiology, Data Intelligence Systems Lab (DISL)
Artificial intelligence (AI) is increasingly becoming key to precision medicine –from identifying disease risks and taking preventive measures, to making diagnoses and personalizing treatment for individuals. Precision medicine, however, is not only about predicting risks and outcomes, but also about weighing interventions. Interventional clinical predictive models need to calculate the so-called counterfactuals, i.e. alternative scenarios (changing variables that would not be naturally modified), requiring the correct specification of cause and effect. In biomedical research, observational studies are commonly affected by bias, such as confounding, selection, and indication. Without robust assumptions, often requiring a priori domain knowledge, derivation of causal effects can be flawed. Data-driven models might have very high accuracy, even in multiple test sets, yet be non-causal and produce the wrong predictions when used to evaluate counterfactuals. Therefore, the premise that data-driven prediction models lead to trustable decisions/interventions for precision medicine is questionable. When pursuing intervention modelling, the bio-health informatics community needs to employ causal AI approaches.