Jadwiga Inglot, Dorota Bartusik-Aebisher, Katarzyna Bania, Klaudia Dynarowicz, David Aebisher
Photodynamic therapy (PDT) is a minimally invasive therapeutic modality that combines a photosensitizer, light of an appropriate wavelength, and molecular oxygen to generate cytotoxic reactive oxygen species for selective tissue destruction. Over recent decades, PDT has evolved from early porphyrin-based systems to advanced third-generation photosensitizers incorporating nanotechnology, targeting ligands, and activatable designs, significantly improving tumor selectivity, pharmacokinetics, and therapeutic efficacy. This article offers an in-depth look at the fundamental principles of PDT, including the roles of photosensitizers, light delivery systems, and oxygen dynamics, as well as the resulting biological effects such as direct tumor cell death, vascular shutdown, and immune activation. Clinical applications across oncology, dermatology, ophthalmology, and antimicrobial therapy are discussed, highlighting both established and emerging indications. Furthermore, the review critically examines recent advances in machine learning (ML) and deep learning (DL) applied to PDT, including treatment planning, dosimetry optimization, photosensitizer and nanoparticle design, real-time treatment monitoring, and outcome prediction. By integrating physics-based modeling, multimodal imaging, and artificial intelligence-driven approaches, PDT is transitioning toward adaptive, personalized photomedicine. This work outlines current challenges, future research directions, and the translational potential of AI-enabled PDT systems, emphasizing their role in improving precision, reproducibility, and clinical outcomes.