AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO025280305
Submit to EJMO
Cite this article
4
Download
59
Views
Journal Browser
Volume | Year
Issue
Search
Forthcoming Issue
Current Issue
View All
News and Announcements
View All
REVIEW ARTICLE

Exploring artificial intelligence-driven photodynamic therapy to advance cancer treatment

Malefo Tshepiso Mofokeng1 ,  Rahul Chandran1 ,  Heidi Abrahamse1*
Show Less
1 Laser Research Centre, Faculty of Health Sciences, University of Johannesburg, Johannesburg, Gauteng, South Africa
EJMO, 025280305 https://doi.org/10.36922/EJMO025280305
Received: 11 July 2025 | Revised: 20 October 2025 | Accepted: 3 November 2025 | Published online: 22 December 2025
© 2025 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
Download PDF
XML
Cite
Abstract

Due to its multifaceted nature, cancer remains a formidable disease that continues to cause substantial mortality globally. Conventional treatments, such as surgery, chemotherapy, and radiation therapy, often fail to adequately control this aggressive disease, thereby reducing the quality of life of affected individuals. Photodynamic therapy (PDT) has emerged as a promising treatment modality that utilizes lasers to destroy cancer cells. While PDT has shown promise as a safer and more targeted treatment with fewer side effects compared to some conventional therapies, it faces certain limitations, such as limited light penetration for deep tumors and the need for tissue oxygenation in hypoxic regions. The emergence and potential use of artificial intelligence (AI)-driven technologies in PDT may offer favorable outcomes for cancer treatment by addressing some of the limitations of conventional PDT. AI uses machine learning (ML) and deep learning (DL) algorithms to continuously learn, adapt to changes, recognize abnormalities in a dataset, and make accurate predictions. In this review, we propose integrating AI tools, such as ML and DL, with PDT to combat cancer and address some of the limitations of conventional PDT. The combination of AI and PDT could improve the precision and effectiveness of PDT for cancer treatment by monitoring the effects of PDT during treatment, advancing imaging techniques for better diagnosis of specific tumor types, enabling monitoring of light in real time, and overcoming light-delivery limitations associated with deep, internal tumors, with the potential to improve overall clinical outcomes in cancer patients.

Keywords
Cancer
Photodynamic therapy
Artificial intelligence
Machine learning
Deep learning
Funding
This research is supported by the South African Research Chairs Initiative of the Department of Science and Technology/National Research Foundation of South Africa (SARChI/NRF-DST; grant number 98337), and the University of Johannesburg Global Excellence and Stature, Fourth Industrial Revolution (GES 4.0) Doctoral Scholarship.
Conflict of interest
The authors declare no conflicts of interest.
References
  1. Correia JH, Rodrigues JA, Pimenta S, Dong T, Yang Z. Photodynamic therapy review: Principles, photosensitizers, applications, and future directions. Pharmaceutics. 2021;13(9):1332. doi: 10.3390/pharmaceutics13091332

 

  1. Hamblin MR, Huang Y. Imaging in Photodynamic Therapy. United States: CRC Press; 2017.

 

  1. National Cancer Institute. Photodynamic Therapy to Treat Cancer. NCI; 2011. Available from: https://www.cancer.gov/ about-cancer/treatment/types/photodynamic-therapy [Last accessed on 2024 Jun 01].

 

  1. Tan A, Jeyaraj R, De Lacey SF. Nanotechnology in neurosurgical oncology. In: Mathur AB, editor. Nanotechnology in Cancer. Micro and Nano Technologies. Ch. 7. New York: William Andrew Publishing; 2017. p. 139-170. doi: 10.1016/B978-0-323-39080-4.00007-0

 

  1. Jia J, Wu X, Long G, et al. Revolutionizing cancer treatment: Nanotechnology-enabled photodynamic therapy and immunotherapy with advanced photosensitizers. Front Immunol. 2023;14:1219785. doi: 10.3389/fimmu.2023.1219785

 

  1. Huang P, Wang D, Su Y, et al. Combination of small molecule prodrug and nanodrug delivery: Amphiphilic drug-drug conjugate for cancer therapy. J Am Chem Soc. 2014;136(33):11748-11756. doi: 10.1021/ja505212y

 

  1. Bao R, Wang Y, Lai J, et al. Enhancing Anti-PD-1/PD-L1 immune checkpoint inhibitory cancer therapy by CD276- targeted photodynamic ablation of tumor cells and tumor vasculature. Mol Pharm. 2019;16(1):339-348. doi: 10.1021/acs.molpharmaceut.8b00997

 

  1. Wong TH, Morton CA, Collier N, et al. British association of dermatologists and british photodermatology group guidelines for topical photodynamic therapy 2018. Br J Dermatol. 2019;180(4):730-739. doi: 10.1111/bjd.17309

 

  1. Lange N, Szlasa W, Saczko J, Chwiłkowska A. Potential of cyanine derived dyes in photodynamic therapy. Pharmaceutics. 2021;13(6):818. doi: 10.3390/pharmaceutics13060818

 

  1. Calixto GMF, Bernegossi J, De Freitas LM, Fontana CR, Chorilli M. Nanotechnology-based drug delivery systems for photodynamic therapy of cancer: A review. Mol Basel Switz. 2016;21(3):342. doi: 10.3390/molecules21030342

 

  1. Gorin F, Harley W, Schnier J, Lyeth B, Jue T. Perinecrotic glioma proliferation and metabolic profile within an intracerebral tumor xenograft. Acta Neuropathol. 2004;107(3):235-244. doi: 10.1007/s00401-003-0803-1

 

  1. Huis in ‘t Veld RV, Heuts J, Ma S, Cruz LJ, Ossendorp FA, Jager MJ. Current challenges and opportunities of photodynamic therapy against cancer. Pharmaceutics. 2023;15(2):330. doi: 10.3390/pharmaceutics15020330

 

  1. Sun Q, Bi H, Wang Z, et al. O2-generating metal-organic framework-based hydrophobic photosensitizer delivery system for enhanced photodynamic therapy. ACS Appl Mater Interfaces. 2019;11(40):36347-36358. doi: 10.1021/acsami.9b11607

 

  1. Hu X, Zhu C, Sun F, et al. Insights into the organic semiconducting photosensitizers for hypoxia-tolerant type I photodynamic therapy. Nano TransMed. 2022;1(2):e9130010. doi: 10.26599/NTM.2022.9130010

 

  1. Dutta D, Wang J, Li X, Zhou Q, Ge Z. Covalent organic framework nanocarriers of singlet oxygen for oxygen-independent concurrent photothermal/ photodynamic therapy to ablate hypoxic tumors. Small. 2022;18(37):2202369. doi: 10.1002/smll.202202369

 

  1. Zhang L, Wang S, Zhou Y, Wang C, Zhang XZ, Deng H. Covalent organic frameworks as favorable constructs for photodynamic therapy. Angew Chem Int Ed. 2019;58(40):14213-14218. doi: 10.1002/anie.201909020

 

  1. An Y, Xu D, Wen X, Chen C, Liu G, Lu Z. Internal light sources-mediated photodynamic therapy nanoplatforms: Hope for the resolution of the traditional penetration problem. Adv Healthc Mater. 2024;13(1):2301326. doi: 10.1002/adhm.202301326

 

  1. Gunaydin G, Gedik ME, Ayan S. Photodynamic therapy-current limitations and novel approaches. Front Chem. 2021;9:691697. doi: 10.3389/fchem.2021.691697

 

  1. ASLMS. Photodynamic Therapy. Available from: https:// www.aslms.org/for/the-public/treatments-using-lasers-and-energy-based-devices/photodynamic-therapy [Last accessed on 2024 Oct 01].

 

  1. Dumoulin F, Durmuş M, Ahsen V, Nyokong T. Synthetic pathways to water-soluble phthalocyanines and close analogs. Coord Chem Rev. 2010;254(23):2792-2847. doi: 10.1016/j.ccr.2010.05.002

 

  1. Li Y, Wang J, Zhang X, et al. Highly water-soluble and tumor-targeted photosensitizers for photodynamic therapy. Org Biomol Chem. 2015;13(28):7681-7694. doi: 10.1039/C5OB01035G

 

  1. Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73-81. doi: 10.1080/13645706.2019.1575882

 

  1. Xu Y, Liu X, Cao X, et al. Artificial intelligence: A powerful paradigm for scientific research. Innovation. 2021;2(4):100179. doi: 10.1016/j.xinn.2021.100179

 

  1. Dlamini Z, Francies FZ, Hull R, Marima R. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020;18:2300-2311. doi: 10.1016/j.csbj.2020.08.019

 

  1. Bajwa J, Munir U, Nori A, Williams B. Artificial intelligence in healthcare: Transforming the practice of medicine. Future Healthc J. 2021;8(2):e188-e194. doi: 10.7861/fhj.2021-0095

 

  1. Reardon S. Rise of robot radiologists. Nature. 2019;576(7787):S54-S58. doi: 10.1038/d41586-019-03847-z

 

  1. Dixon D, Sattar H, Moros N, et al. Unveiling the influence of AI predictive analytics on patient outcomes: A comprehensive narrative review. Cureus. 2024;16(5):e59954 doi: 10.7759/cureus.59954

 

  1. Myszczynska MA, Ojamies PN, Lacoste AMB, et al. Applications of machine learning to diagnosis and treatment of neurodegenerative diseases. Nat Rev Neurol. 2020;16(8):440-456. doi: 10.1038/s41582-020-0377-8

 

  1. Huang C, Clayton EA, Matyunina LV, et al. Machine learning predicts individual cancer patient responses to therapeutic drugs with high accuracy. Sci Rep. 2018;8(1):16444. doi: 10.1038/s41598-018-34753-5

 

  1. Sheu YH, Magdamo C, Miller M, Das S, Blacker D, Smoller JW. AI-assisted prediction of differential response to antidepressant classes using electronic health records. NPJ Digit Med. 2023;6:73. doi: 10.1038/s41746-023-00817-8

 

  1. Sauer CM, Chen LC, Hyland SL, Girbes A, Elbers P, Celi LA. Leveraging electronic health records for data science: Common pitfalls and how to avoid them. Lancet Digit Health. 2022;4(12):e893-e898. doi: 10.1016/S2589-7500(22)00154-6

 

  1. Karalis VD. The integration of artificial intelligence into clinical practice. Appl Biosci. 2024;3(1):14-44. doi: 10.3390/applbiosci3010002

 

  1. Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer. 2024;130(12):2101-2107. doi: 10.1002/cncr.35307

 

  1. Alowais SA, Alghamdi SS, Alsuhebany N, et al. Revolutionizing healthcare: The role of artificial intelligence in clinical practice. BMC Med Educ. 2023;23:689. doi: 10.1186/s12909-023-04698-z

 

  1. Kim WS, Khot MI, Woo HM, et al. AI-enabled, implantable, multichannel wireless telemetry for photodynamic therapy. Nat Commun. 2022;13(1):2178. doi: 10.1038/s41467-022-29878-1

 

  1. Upadhyay RK. Drug delivery systems, CNS protection, and the blood brain barrier. BioMed Res Int. 2014;2014:869269. doi: 10.1155/2014/869269

 

  1. Kumar R, Sharma A, Alexiou A, Bilgrami AL, Kamal MA, Ashraf GM. DeePred-BBB: A blood brain barrier permeability prediction model with improved accuracy. Front Neurosci. 2022;16:858126. doi: 10.3389/fnins.2022.858126

 

  1. Huang ETC, Yang JS, Liao KYK, et al. Predicting blood- brain barrier permeability of molecules with a large language model and machine learning. Sci Rep. 2024;14(1):15844. doi: 10.1038/s41598-024-66897-y

 

  1. Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: A generalist algorithm for cellular segmentation. Nat Methods. 2021;18(1):100-106. doi: 10.1038/s41592-020-01018-x

 

  1. Sakaguchi T, Kinoshita H, Ikebuchi Y, et al. Next-generation laser-based photodynamic endoscopic diagnosis using 5-aminolevulinic acid for early gastric adenocarcinoma and gastric adenoma. Ann Gastroenterol. 2020;33(3):257-264. doi: 10.20524/aog.2020.0479

 

  1. Yamashita T, Kurumi H, Fujii M, et al. Objective methods of 5-aminolevulinic acid-based endoscopic photodynamic diagnosis using artificial intelligence for identification of gastric tumors. J Clin Med. 2022;11(11):3030. doi: 10.3390/jcm11113030

 

  1. Sylvester PW. Optimization of the tetrazolium dye (MTT) colorimetric assay for cellular growth and viability. In: Satyanarayanajois SD, editor. Drug Design and Discovery: Methods and Protocols. United States: Humana Press; 2011. p. 157-168. doi: 10.1007/978-1-61779-012-6_9

 

  1. Lv S, Wang X, Wang G, Yang W, Cheng K. Efficient evaluation of photodynamic therapy on tumor based on deep learning. Photodiagnosis Photodyn Ther. 2023;43:103658. doi: 10.1016/j.pdpdt.2023.103658

 

  1. Karimian G, Petelos E, Evers SMAA. The ethical issues of the application of artificial intelligence in healthcare: A systematic scoping review. AI Ethics. 2022;2(4):539-551. doi: 10.1007/s43681-021-00131-7

 

  1. Yurdem B, Kuzlu M, Gullu MK, Catak FO, Tabassum M. Federated learning: Overview, strategies, applications, tools and future directions. Heliyon. 2024;10(19):e38137. doi: 10.1016/j.heliyon.2024.e38137

 

  1. Sanchez-Serrano P, Rios R, Agudo I. A decision framework for privacy-preserving synthetic data generation. Comput Electr Eng. 2025;126:110468. doi: 10.1016/j.compeleceng.2025.110468

 

  1. Luxton DD. Artificial Intelligence in Behavioral and Mental Health Care. United States: Academic Press; 2015.

 

  1. Bi WL, Hosny A, Schabath MB, et al. Artificial intelligence in cancer imaging: Clinical challenges and applications. CA Cancer J Clin. 2019;69(2):127-157. doi: 10.3322/caac.21552

 

  1. Kasula BY. Advancements in AI-Driven Healthcare: A Comprehensive Review of Diagnostics, Treatment, and Patient Care Integration. Kentucky: University of the Cumberland; 2024.

 

  1. Hong TS, Tomé WA, Harari PM. Heterogeneity in head and neck IMRT target design and clinical practice. Radiother Oncol. 2012;103(1):92-98. doi: 10.1016/j.radonc.2012.02.010

 

  1. Li XA, Tai A, Arthur DW, et al. Variability of target and normal structure delineation for breast cancer radiotherapy: An RTOG multi-institutional and multiobserver study. Int J Radiat Oncol Biol Phys. 2009;73(3):944-951. doi: 10.1016/j.ijrobp.2008.10.034

 

  1. Warfield SK, Zou KH, Wells WM. Validation of image segmentation by estimating rater bias and variance. Philos Transact A Math Phys Eng Sci. 2008;366(1874):2361-2375. doi: 10.1098/rsta.2008.0040

 

  1. Castellino RA. Computer aided detection (CAD): An overview. Cancer Imaging. 2005;5(1):17-19. doi: 10.1102/1470-7330.2005.0018

 

  1. Liang M, Tang W, Xu DM, et al. Low-dose CT screening for lung cancer: Computer-aided detection of missed lung cancers. Radiology. 2016;281(1):279-288. doi: 10.1148/radiol.2016150063

 

  1. Cheng HD, Cai X, Chen X, Hu L, Lou X. Computer-aided detection and classification of microcalcifications in mammograms: A survey. Pattern Recognit. 2003;36(12): 2967-2991. doi: 10.1016/S0031-3203(03)00192-4

 

  1. Mirsadraee S, Oswal D, Alizadeh Y, Caulo A, Van Beek EJ Jr. The 7th lung cancer TNM classification and staging system: Review of the changes and implications. World J Radiol. 2012;4(4):128-134. doi: 10.4329/wjr.v4.i4.128

 

  1. Song SE, Seo BK, Cho KR, et al. Computer-aided detection (CAD) system for breast MRI in assessment of local tumor extent, nodal status, and multifocality of invasive breast cancers: Preliminary study. Cancer Imaging. 2015;15(1):1. doi: 10.1186/s40644-015-0036-2

 

  1. He L, Dong J, Yang Y, et al. Accelerating the discovery of type Ⅱ photosensitizer: Experimentally validated machine learning models for predicting the singlet oxygen quantum yield of photosensitive molecule. J Mol Struct. 2025;1321:139850. doi: 10.1016/j.molstruc.2024.139850

 

  1. Sarbadhikary P, George BP, Abrahamse H. Recent advances in photosensitizers as multifunctional theranostic agents for imaging-guided photodynamic therapy of cancer. Theranostics. 2021;11(18):9054-9088. doi: 10.7150/thno.62479

 

  1. Woodhams JH, MacRobert AJ, Bown SG. The role of oxygen monitoring during photodynamic therapy and its potential for treatment dosimetry. Photochem Photobiol Sci. 2007;6(12):1246-1256. doi: 10.1039/b709644e

 

  1. Moan J, Sommer S. Oxygen dependence of the photosensitizing effect of hematoporphyrin derivative in NHIK 3025 cells. Cancer Res. 1985;45(4):1608-1610.

 

  1. Bilodeau S. Artificial intelligence and medical oxygen. Biomed J Sci Tech Res. 2023;51(2):42413. doi: 10.26717/BJSTR.2023.51.008062

 

  1. Desautels K. AI-Assisted Oxygenation Device Shows Promise in Quebec Hospitals. Montreal; 2024. Available from: https:// montreal.ctvnews.ca/ai-assisted-oxygenation-device-shows-promise-in-quebec-hospitals-1.7104607 [Last accessed on 2024 Dec 09].

 

  1. Mak KK, Pichika MR. Artificial intelligence in drug development: Present status and future prospects. Drug Discov Today. 2019;24(3):773-780. doi: 10.1016/j.drudis.2018.11.014
Next article in this issue
Share
Back to top
Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
We use cookies on our website to ensure you get the best experience.
Read more about our cookies here
Accept