Clinical Validation of AI-Based Medical Devices for Immune-Related Applications
DOI:
https://doi.org/10.61424/0eb35638Keywords:
Artificial intelligence; AI-based medical devices; Clinical validation; Immunological; Immune-mediated disorders; Cancer immunotherapy; Autoimmune diseases; Precision medicine; Clinical decision support; Machine learningAbstract
Artificial intelligence (AI)-enabled medical devices, particularly those based on image analysis and biomarkers, are transforming immune-related healthcare by enabling deeper insights into disease pathophysiology, increased diagnostic precision, patients’ stratification, personalized therapy selection, and improved monitoring of disease and treatment responses. The advent of the capability for AI-enabled medical devices to combine, analyze, and interpret multidimensional data, such as genomic, proteomic, radiomic, immunologic, and electronic health records data, has been key to the adoption of precision medicine approaches in cancer immunotherapy, autoimmune disease, and cellular immunotherapies. To fully realize the benefits of AI-enabled medical devices for immune-related healthcare, clinical validation of reliable and reproducible diagnostic and therapeutic performance must prove their clinical meaningfulness and generalizability to different clinical and demographic scenarios. In this review, we explore clinical validation concepts and systems, including analytical validation, clinical validation, external validation, prospective evaluation, and performance monitoring, as well as criteria used to evaluate diagnostic performance, clinical utility, model robustness, and interpretability. We further review the applications of clinically validated AI-enabled medical devices in cancer immunotherapy, autoimmune disease, chimeric antigen receptor T-cell therapy, and immune biomarker-guided precision medicine. In addition, the key challenges for their clinical translation, including data heterogeneity, generalizability, algorithmic bias, lack of explainability, workflow integration, and ethics, are critically evaluated.
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Copyright (c) 2026 Chrysoula I. Liakou, Marios Papadakis, Markos Plytas (Author)
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