ReviewFrontiers in oncology2026
Empowering photodynamic therapy with artificial intelligence: current trends and future directions.
Review in Frontiers in oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Next-generation photodynamic cancer therapy: purpurin-based nanocarriers as emerging photosensitizers.Naunyn-Schmiedeberg's archives of pharmacology · 2026Review
- Artificial Intelligence and Natural Photosensitizer-Based Nanopharmaceuticals in Photodynamic Therapy: Advanced Modeling, Data-Driven Optimization, and Translational Perspectives.Pharmaceutics · 2026Review
- Artificial intelligence-assisted photodynamic diagnosis and photodynamic therapy against cancer.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
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 non-malignant 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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.