Guiding artificial intelligence in public health and medicine with epidemiology: A lifecycle framework for mitigating...

Artificial Intelligence (AI) holds immense promise for public health, yet its potential is undermined by alignment failures where systems act contrary to human values, often exacerbating health disparities. This paper challenges the narrow view that algorithmic bias is solely a data problem, arguing instead that misalignment arises at every stage of the AI development lifecycle. We introduce a comprehensive seven-stage framework, spanning problem definition, team assembly, study design, data acquisition, model training, validation, and post-deployment implementation, viewed through an epidemiological lens. This approach systematically integrates core principles such as population representativeness, rigorous study design, bias characterization, and causal reasoning to identify and mitigate alignment risks. For each stage, we define specific alignment failures, from flawed problem formulation to post-market performance degradation, and propose actionable, evidence-based solutions. By embedding epidemiological rigor throughout the entire AI lifecycle, this framework provides a structured, proactive pathway for researchers, developers, and policymakers to create trustworthy, safe, and fair AI systems. This systemic approach is critical to harnessing AI's transformative benefits for population health while preventing the perpetuation of inequity and harm.