Artificial Intelligence as a Return on Investment Multiplier in Health Care

AbstractIn heterogeneous clinical populations, the clinical and economic value of therapies depends on how they are allocated. Predictive artificial intelligence (AI) models can improve the precision of treatment allocation beyond what earlier clinical decision support tools could achieve by integrating data across clinical, biomarker, imaging, and administrative domains simultaneously. This capacity can increase the realized value of existing and emerging therapies relative to the status quo by improving two types of treatment decisions: risk-based targeting, which concentrates treatment among patients at higher baseline risk, and response-based targeting, which identifies patients with greater expected treatment benefit. Both mechanisms reduce effective numbers needed to treat (NNT) and improve return on investment (ROI), reducing low-value care and strengthening incentives to develop therapies for well-defined patient subgroups. We illustrate this argument using remote physiological monitoring and Alzheimer's disease - two settings where current deployment heuristics produce high effective NNT and limited ROI, and where improved predictive targeting could materially improve the economic conclusion. Realizing this potential depends on data governance, incentives, and regulatory pathways that support the development, continuous refinement, and clinical integration of predictive models. Framing predictive AI as an ROI multiplier on the therapies it guides has broader consequences for how those therapies are developed, regulated, priced, and covered, and for how predictive AI models themselves should be valued and evaluated.