ArticleOphthalmology and therapy2024
Exploring the Potential of Code-Free Custom GPTs in Ophthalmology: An Early Analysis of GPT Store and User-Creator Guidance.
Article in Ophthalmology and therapy, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating Large Language Models in Ophthalmology: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Large Language Models in Ophthalmology: A Bibliographic Analysis.Turkish journal of ophthalmology · 2026Article
- Prompt injection of OpenAI custom GPTs leaks informatics secrets.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2026Article
- A custom ChatGPT can accurately answer questions from an international expert osteotomy consensus statement.European journal of orthopaedic surgery & traumatology : orthopedie traumatologie · 2025Article
- Generative AI's healthcare professional role creep: a cross-sectional evaluation of publicly accessible, customised health-related GPTs.Frontiers in public health · 2025Observational
- Evaluating large language model clinical reasoning in glaucoma using retrieval-augmented generation.Advances in ophthalmology practice and researchArticle
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2 authors.
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Abstract
introductionOpenAI recently introduced the ability to create custom generative pre-trained transformers (cGPTs) using text-based instruction and/or external documents using retrieval-augmented generation (RAG) architecture without coding knowledge. This study aimed to analyze the features of ophthalmology-related cGPTs and explore their potential utilities.
methodsData collection took place on January 20 and 21, 2024, and custom GPTs were found by entering ophthalmology keywords into the "Explore GPTS" section of the website. General and specific features of cGPTs were recorded, such as knowledge other than GPT-4 training data. The instruction and description sections were analyzed for compatibility using the Likert scale. We analyzed two custom GPTs with the highest Likert score in detail. We attempted to create a convincingly presented yet potentially harmful cGPT to test safety features.
resultsWe analyzed 22 ophthalmic cGPTs, of which 55% were for general use and the most common subspecialty was glaucoma (18%). Over half (55%) contained knowledge other than GPT-4 training data. The representation of the instructions through the description was between "Moderately representative" and "Very representative" with a median Likert score of 3.5 (IQR 3.0-4.0). The instruction word count was significantly associated with Likert scores (P = 0.03). Tested cGPTs demonstrated potential for specific conversational tone, information, retrieval and combining knowledge from an uploaded source. With these safety settings, creating a malicious GPT was possible.
conclusionsThis is the first study to our knowledge to examine the GPT store for a medical field. Our findings suggest that these cGPTs can be immediately implemented in practice and may offer more targeted and effective solutions compared to the standard GPT-4. However, further research is necessary to evaluate their capabilities and limitations comprehensively. The safety features currently appear to be rather limited. It may be helpful for the user to review the instruction section before using a cGPT.
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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.