aceAI without reach: The last mile problem in preventive public health
Objectives: Examine the "last mile problem" in artificial intelligence (AI) applications for preventive public health. Study design: Conceptual short communication introducing a five-stage pathway to examine where AI-enabled prevention breaks down. Methods: This draws on emerging literature in AI, health services research, and implementation science to distinguish between predictive capacity and real-world impact. A five-stage pathway model of prediction, translation, delivery, uptake, and sustainment is introdu…
Public health AI focuses mostly on prediction, with little attention to delivery, engagement and sustained behavior change, so prevention benefits may not materialize and could widen inequities for disadvantaged groups.
The work is a conceptual communication built on emerging literature, so it offers a framework and argument rather than measured evidence of outcomes from AI prevention tools.
Evidence
- Peer-reviewedPublic Health2026-10-18
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Truvace Impact Record TRV-2026-1877, v1: “AI without reach: The last mile problem in preventive public health.” Truvace, 2026-10-19. /record/TRV-2026-1877 (accessed at citation time). sha256 fab7e5cd213a8040…
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