Assessing the Potential of AI-Driven Drug Repurposing in Ophthalmology: An Analysis of ChatGPT's Therapeutic Recommendations
Mahmoudzadeh, R.; Zaichik, M.; Selvan, K.; Islam, T.; Salabati, M.; Leffler, C. T.
Show abstract
PurposeThis study aimed to evaluate the novelty and potential value of therapeutic suggestions made by an artificial intelligence large language model for treating various ophthalmic diseases. MethodsChatGPT-3.5 was used to suggest novel ophthalmic indications for available medications. The generation of therapeutic suggestions was performed by inputting standardized queries about treatments for common ophthalmic conditions and then categorizing the responses by drug type. Data tables were organized by ophthalmic condition, with consistent quality checks to ensure accuracy. Literature searches were conducted to determine the FDA-approval status of each therapy, and whether the suggested application was novel in the context of the condition. Therapies were categorized according to current use and level of evidence for use. ResultsChatGPT proposed 180 medications and treatment options for 36 eye conditions. Of the 180 medications, 143 (79.4%) were FDA-approved for general medical use and 32 out of 180 (17.7%) were specifically approved for the recommended ophthalmological conditions. The majority of suggested treatments were for corneal and anterior segment disease (82/180 or 46%), with other categories being retina (23%), glaucoma (8.9%), pediatrics and strabismus (12%), neuro-ophthalmology (0.55%), and uveitis (10%). The proposed treatments were then evaluated by the degree to which the literature supported additional investigation. The majority, 86/180 (48%), were already being used in the clinic, while 27/180 (15%) represented a novel ophthalmic use that appeared to be a reasonable hypothesis to test, and 20/180 (11%) were novel, but appeared unlikely to succeed, based on their mechanism of action. The level of novelty for each treatment was also evaluated, with categories spanning from pre-existing testing in animal models to repurposed for novel ophthalmic use. ConclusionThese findings suggest that ChatGPT is capable of formulating novel treatment options for a range of ophthalmic diseases. Of the suggestions, 27/180 (15%) appeared novel, and reasonable suggestions, based on their mechanism of action. ChatGPT can potentially suggest novel ophthalmic applications for existing medications, which could be evaluated with further laboratory and clinical research.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Unveiling the Clinical Incapabilities: A Benchmarking Study of GPT-4V(ision) for Ophthalmic Multimodal Image Analysis 96%
- Evaluation of the Nallasamy Formula: A Stacking Ensemble Machine Learning Method for Refraction Prediction in Cataract Surgery 95%
- Autonomous Screening for Laser Photocoagulation in Fundus Images Using Deep Learning 94%
Similar papers in this journal
- Identification of Risk Factors for Glaucoma Progression in Free-Text Clinical Notes using a Local Small Language Model 93%
- Gradient Boosting Decision Tree Algorithm for the Prediction of Postoperative Intraocular Lens Position in Cataract Surgery 93%
- The learning curve of murine subretinal injection among clinically trained ophthalmic surgeons 93%
Similar papers in this journal
- An Open-Source Dataset Of Anti-Vegf Therapy In Diabetic Macular Oedema Patients Over Four Years & Their Visual Outcomes 93%
- Automated vision screening of children using a mobile graphic device 93%
- Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm 92%
Similar papers in this journal
- Detecting papilloedema as a marker of raised intracranial pressure using artificial intelligence: a systematic review 95%
- Ethical review of clinical research with generative AI: Evaluating ChatGPT’s accuracy and reproducibility 91%
- Artificial Intelligence's Contribution to Biomedical Literature Search: Revolutionizing or Complicating? 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.