Reporting follow-up budgets for diagnostic and predictive AI

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ID: 319888
2026
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Abstract
OBJECTIVE: To argue that diagnostic and predictive AI should be evaluated by both classification performance and the downstream work their outputs create. DISCUSSION: Prior work has framed AI as an intervention and has quantified alert burden or decision utility under resource constraints. This perspective extends that logic beyond surveillance alerts by proposing follow-up budget reporting for diagnostic and predictive AI. Across lung nodule detection, sepsis alerting, and autonomous diabetic retinopathy screening, similar classification performance can imply very different demands on imaging, specialist access, nursing attention, and patient waiting. A follow-up budget statement should specify the comparator, scope and stopping rule, downstream action volume, resource types consumed, yield per clinically meaningful true-positive case, and the local pathway assumptions and stakeholders responsible for those estimates. CONCLUSION: Clinical AI evaluation should make follow-up capacity visible alongside discrimination, calibration, and decision-analytic measures.
Reference Key
openalex_W7167716751 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Henry Bair, Mak Djulbegovic
Journal Journal of the American Medical Informatics Association : JAMIA
Year 2026
DOI
10.1093/jamia/ocag118
URL
https://doi.org/10.1093/jamia/ocag118
Keywords Keywords not found

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