Lujan, Giovanni Mario

Background: The rapid adoption of digital pathology and artificial intelligence (AI) in oncology creates multiple opportunities for precision diagnostics but also creates an urgent need for evidence-based standards to ensure safe and effective implementation. Goal: This article, developed by the Digital Pathology Association, presents recommendations for digital pathology as well as the validation and clinical utility of AI-enabled digital pathology tools in clinical practice. This guidance addresses analytical and clinical validation, algorithm reliability, and criteria for establishing clinical utility attributed to test use. Key recommendations emphasize separate validation of scanning processes and AI algorithms, concordance studies to support interscanner generalizability, rigorous assessment of accuracy and reliability in real-world settings, and clear description of algorithmic use limitations. These recommendations further provide frameworks for when AI may replace or augment existing diagnostic approaches, such as biomarker scoring, cancer diagnosis, and prognostic risk assessment. Conclusions: By considering payer, regulatory, and clinical perspectives, the recommendations promote transparency, trust, and reproducibility in digital pathology while encouraging value-based care delivery. We support responsible innovation in computational pathology, ensuring that AI applications achieve not only technical performance goals but also deliver measurable clinical benefit to patients.

Context.— Machine learning applications in the pathology clinical domain are emerging rapidly. As decision support systems continue to mature, laboratories will increasingly need guidance to evaluate their performance in clinical practice. Currently there are no formal guidelines to assist pathology laboratories in verification and/or validation of such systems. These recommendations are being proposed for the evaluation of machine learning systems in the clinical practice of pathology. Objective.— To propose recommendations for performance evaluation of in vitro diagnostic tests on patient samples that incorporate machine learning as part of the preanalytical, analytical, or postanalytical phases of the laboratory workflow. Topics described include considerations for machine learning model evaluation including risk assessment, predeployment requirements, data sourcing and curation, verification and validation, change control management, human-computer interaction, practitioner training, and competency evaluation. Data Sources.— An expert panel performed a review of the literature, Clinical and Laboratory Standards Institute guidance, and laboratory and government regulatory frameworks. Conclusions.— Review of the literature and existing documents enabled the development of proposed recommendations. This white paper pertains to performance evaluation of machine learning systems intended to be implemented for clinical patient testing. Further studies with real-world clinical data are encouraged to support these proposed recommendations. Performance evaluation of machine learning models is critical to verification and/or validation of in vitro diagnostic tests using machine learning intended for clinical practice.