Optical digital twins for disease prevention, diagnosis, therapy, and intervention

Digital twins are transitioning from conceptual models to operational frameworks that link measurement, prediction, and intervention in biomedicine. However, most biomedical digital twin efforts remain fragmented, with limited integration across biological scales, sensing modalities, and clinical decision points. Biophotonics provides a uniquely suited measurement foundation for biomedical digital twins by enabling quantitative, physics-grounded, and longitudinal noninvasive measurements spanning molecular, cellular, tissue, organ, and whole-body scales. These capabilities position photonics as a foundational measurement layer for next-generation biomedical digital twins.

To synthesize current advances and future opportunities in optical digital twins and to establish a unifying framework for how photonic sensing can support digital twin architectures for disease diagnosis, therapy guidance, prevention, continuous monitoring, and interventional healthcare.

This white paper summarizes perspectives presented at the annual meeting of the international society for optics and photonics (SPIE Photonics West), in the session "Digital Twins as New Approach Methodologies (NAMs) in Biophotonics." We review five complementary implementations of the digital twin paradigm: (i) virtual tissue staining for histopathology, which combines label-free optical imaging with machine learning-based inference to generate clinically interpretable representations with uncertainty quantification and validation; (ii) cell-level metabolic digital twins that use autofluorescence and redox imaging to predict patient-specific therapeutic responses in tumor organoids and immune cells under controlled perturbations; (iii) therapeutic digital twin frameworks for radiation therapy, in which Cherenkov imaging and radiation chemistry sensing verify treatment delivery and enable biophysical model correction and personalization; (iv) personalized optical digital twins for continuous monitoring that integrate longitudinal photonic sensing with physiological and contextual data to support early detection, prevention, and adaptive care; and (v) personalized digital twins for interventional healthcare.

Across these diverse applications, a common digital twin architecture emerges. Optical measurements define patient state, inference models translate measurements into predictions, therapeutic interventions perturb the system, verification measurements constrain and validate execution, and longitudinal sensing continuously updates the twin over time. The reviewed examples demonstrate that optical measurements can serve as a scalable and biologically relevant data layer linking prediction and intervention across multiple levels of biological organization.

Optical digital twins are no longer merely a conceptual aspiration but are emerging as a practical, measurement-driven infrastructure for precision medicine. The primary challenge is no longer feasibility, but rather the integration, interoperability, validation, and uncertainty quantification of digital twin systems capable of operating safely and at scale. Advances in photonic sensing, computational modeling, and clinical translation position optical digital twins to support real-time, patient-specific clinical decision-making across diagnosis, treatment, monitoring, and prevention.