The accuracy–utility gap: Why AI accuracy does not guarantee clinical benefit in digital pathology
- 1Servicio de Anatomía Patológica, Hospital Regional Dr. Franco Ravera Zunino, Av. Lib-ertador Bernardo O'Higgins 3065, Rancagua, Región del Libertador General Bernardo O'Higgins, Chile
- 2Centro de Pacientes Críticos, Clínica Las Condes, Santiago, Chile; Escuela de Medicina, Facultad de Medicina, Universidad Finis Terrae, Santiago, Chile; Corresponding author at: Centro de Pacientes Críticos, Clínica Las Condes, Santiago, Chile.
Abstract
Artificial intelligence (AI) in digital pathology has repeatedly achieved expert-level diagnostic accuracy under controlled validation conditions. However, its clinical translation—reflected in regulatory approval, routine adoption, changes in diagnostic decisions, or demonstrated patient benefit—remains inconsistent. This viewpoint argues that this discrepancy arises because diagnostic accuracy is a property of a model under specified conditions, whereas clinical utility emerges from the entire sociotechnical pathway in which the model is deployed. Building on recent work on standardization, system selection, institutional validation, and trial governance, we integrate these complementary layers into a continuous tissue-to-outcome pathway comprising six stages: tissue, image, model, workflow, decision, and outcome, with each stage explicitly linked to the claim it can support. Variability introduced at any stage may remain undetected and propagate downstream, potentially affecting clinical decisions and patient care. Accordingly, evidence generated at one stage cannot substitute for that required to support claims at subsequent stages. We therefore propose a stage-linked evidence map that identifies the principal risk and evidence required at each stage, while making explicit which stronger downstream claims remain unsupported. This framework enables reviewers and institutions considering adoption to identify where a study has addressed the accuracy–utility gap and where uncertainty remains. Evaluating AI across the complete pathway reframes the evidentiary requirements for clinical translation: high diagnostic accuracy is necessary, but insufficient, to establish meaningful clinical utility.
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BibTeX
@article{battiato2026accuracyutility,
title = {{The accuracy–utility gap: Why AI accuracy does not guarantee clinical benefit in digital pathology}},
author = {Romina Battiato and Felipe Montoya Silva and Mylene Cabrera Morales and Deyana Salazar Romero and Martín H. Benites},
journal = {Journal of Pathology Informatics},
year = {2026},
month = nov,
doi = {10.1016/j.jpi.2026.100751},
url = {https://doi.org/10.1016/j.jpi.2026.100751},
note = {CC BY},
}
RIS
TY - JOUR
TI - The accuracy–utility gap: Why AI accuracy does not guarantee clinical benefit in digital pathology
AU - Romina Battiato
AU - Felipe Montoya Silva
AU - Mylene Cabrera Morales
AU - Deyana Salazar Romero
AU - Martín H. Benites
PY - 2026
DA - 2026/11/05
JO - Journal of Pathology Informatics
DO - 10.1016/j.jpi.2026.100751
UR - https://doi.org/10.1016/j.jpi.2026.100751
AB - Artificial intelligence (AI) in digital pathology has repeatedly achieved expert-level diagnostic accuracy under controlled validation conditions. However, its clinical translation—reflected in regulatory approval, routine adoption, changes in diagnostic decisions, or demonstrated patient benefit—remains inconsistent. This viewpoint argues that this discrepancy arises because diagnostic accuracy is a property of a model under specified conditions, whereas clinical utility emerges from the entire sociotechnical pathway in which the model is deployed. Building on recent work on standardization, system selection, institutional validation, and trial governance, we integrate these complementary layers into a continuous tissue-to-outcome pathway comprising six stages: tissue, image, model, workflow, decision, and outcome, with each stage explicitly linked to the claim it can support. Variability introduced at any stage may remain undetected and propagate downstream, potentially affecting clinical decisions and patient care. Accordingly, evidence generated at one stage cannot substitute for that required to support claims at subsequent stages. We therefore propose a stage-linked evidence map that identifies the principal risk and evidence required at each stage, while making explicit which stronger downstream claims remain unsupported. This framework enables reviewers and institutions considering adoption to identify where a study has addressed the accuracy–utility gap and where uncertainty remains. Evaluating AI across the complete pathway reframes the evidentiary requirements for clinical translation: high diagnostic accuracy is necessary, but insufficient, to establish meaningful clinical utility.
SN - 2153-3539
SN - 2229-5089
ER -