Survey and perspective on verification, validation, and uncertainty quantification of digital twins for precision medicine
Kaan Sel, Andrea Hawkins‐Daarud, Anirban Chaudhuri, Deen Osman, Ahmad Bahai, David Paydarfar, Karen Willcox, Caroline Chung, Roozbeh Jafari
Abstract
Digital twins in precision medicine provide tailored health recommendations by simulating patient-specific trajectories and interventions. We examine the critical role of Verification, Validation, and Uncertainty Quantification (VVUQ) for digital twins in ensuring safety and efficacy, with examples in cardiology and oncology. We highlight challenges and opportunities for developing personalized trial methodologies, validation metrics, and standardizing VVUQ processes. VVUQ frameworks are essential for integrating digital twins into clinical practice.
Topics & Concepts
Precision medicinePerspective (graphical)Psychological interventionComputer scienceData scienceMedical physicsModel validationDigital healthMedicineHealth careArtificial intelligencePathologyNursingEconomicsEconomic growthArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical ImagingBiomedical and Engineering Education