Empowering Photodynamic Therapy With Artificial Intelligence: Current Trends and Future Directions

Avijit Paul¹Marvin Xavierselvan¹David Aebisher²Tomasz Kubrak²Dorota Bartusik-Aebisher²Srivalleesha Mallidi^1*

• ¹Tufts University, Medford, United States
• ²Uniwersytet Rzeszowski Collegium Medicum, Rzeszow, Poland

The evolution of photodynamic therapy (PDT), from ancient photomedicine practices to modern clinical applications, reflects its remarkable versatility in oncology and beyond. PDT relies on the interaction between photosensitizers, light, and tissue oxygen to generate reactive oxygen species that selectively destroy diseased cells. While the therapy has proven effective across various cancers and nonmalignant conditions, tailoring treatment to individual patients remains challenging due to patient-specific variations in tissue optical properties, photosensitizer pharmacokinetics, and tumor heterogeneity. The rapid advancement of artificial intelligence (AI), including machine learning and deep learning, offers transformative opportunities to address these challenges through data-driven optimization and personalization. In this review, we examine how AI is being integrated across the PDT pipeline. We analyze AI-driven approaches for photosensitizer development, including quantitative structure-activity relationship modeling, graph neural networks for property prediction, and generative models for de novo molecular design. We examine machine learning applications in nanoparticle-based drug delivery systems, encompassing synthesis optimization, nano-bio interaction prediction, and stimuli-responsive release modeling. The review further explores AI integration in treatment planning through real-time tissue optical property estimation, and in clinical decision-making through treatment response monitoring and outcome prediction using multimodal imaging data. We critically assess current limitations, including small dataset challenges, model interpretability concerns, and the gap between preclinical research and clinical translation. Finally, we outline future directions, including federated learning, explainable AI, and regulatory considerations. This review aims to bridge the AI and PDT communities, providing a roadmap for improved patient outcomes.