Advocates for establishing preventive pathology as a structured discipline, enhancing disease prevention in communities.
Pathology has been foundational to preventive medicine, providing the biological scaffolding for cervical cancer screening, colorectal and esophageal surveillance programs, pre-malignant lesion risk stratification, and cancer prevention guidelines across multiple organ systems. Yet despite these contributions, pathology has lacked the organizational coherence to participate as a systematic partner in prevention strategy. Biological understanding generated within individual diagnostic domains have too often remained siloed, disconnected from the insights that cross-domain synthesis could generate. The artificial intelligence and machine learning revolution now transforming pathology presents a critical inflection point. For the first time, analytical tools capable of extracting biological information from tissue at population scale are being designed and deployed but largely around diagnostic endpoints, without organized pathology input into prevention-oriented validation frameworks. The annotated datasets and regulatory standards being established now will shape prevention workflows for years. This paper argues for the formal construction of preventive pathology as an organized discipline built on three pillars: a research agenda oriented toward prevention endpoints and disease biology rather than diagnostic classification alone; multidisciplinary dialogue that is structural rather than incidental, bringing pathological evidence to the tables where prevention guidelines are written and AI tools are validated; and education that produces a workforce fluent across pathological biology and prevention science. The opportunity is not simply to predict more accurately what we already measure, but to ask genuinely new questions about disease biology that no single diagnostic category has yet framed. Realizing this opportunity requires intention and a deliberate decision by pathologists, preventive medicine clinicians, health systems, and AI developers to organize around a shared preventive mandate before the technological moment narrows.
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Monika Vyas (2026) studied this question.
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