Identification of kidney-related medications using AI from self-captured pill images
Open accessAbstract
INTRODUCTION: ChatGPT, a state-of-the-art large language model, has shown potential in analyzing images and providing accurate information. This study aimed to explore ChatGPT-4 as a tool for identifying commonly prescribed nephrology medications across different versions and testing dates. METHODS: 25 nephrology medications were obtained from an institutional pharmacy. High-quality images of each medication were captured using an iPhone 13 Pro Max and uploaded to ChatGPT-4 with the query, 'What is this medication?' The accuracy of ChatGPT-4's responses was assessed for medication name, dosage, and imprint. The process was repeated after 2 weeks to evaluate consistency across different versions, including GPT-4, GPT-4 Legacy, and GPT-4.Ø. RESULTS: ChatGPT-4 correctly identified 22 out of 25 (88%) medications across all versions. However, it misidentified Hydrochlorothiazide, Nifedipine, and Spironolactone due to misreading imprints. For instance, Nifedipine ER 90 mg was mistaken for Metformin Hydrochloride ER 500 mg because 'NF 06' was misread as 'NF 05'. Hydrochlorothiazide 50 mg was confused with the 25 mg version due to imprint errors, and Spironolactone 25 mg was misidentified as Naproxen Sodium or Diclofenac Sodium. Despite these errors, ChatGPT-4 showed 100% consistency when retested, correcting misidentifications after receiving feedback on the correct imprints. CONCLUSION: ChatGPT-4 shows strong potential in identifying nephrology medications from self-captured images, though challenges with difficult-to-read imprints remain. Providing feedback improved accuracy, suggesting ChatGPT-4 could be a valuable tool in digital health for medication identification. Future research should enhance the model's ability to distinguish similar imprints and explore broader integration into digital health platforms.
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ChatGPT-3.5 performed adequately in providing medication information but had poor performance in recognizing adverse drug reactions and on high-complexity tasks.
“In other studies, ChatGPT-3.5 was reported to have performed adequately in providing medication information, 32,55 but had poor performance in J o u r n a l P r e -p r o o f recognizing adverse drug reactions and similar poor performance on high-complexity tasks.55,91,92”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [55]
The Methods and Results assess ChatGPT-4’s identification of medications from pill images, not ChatGPT-3.5’s provision of medication information. A search for adverse drug reactions and high-complexity tasks found no passage assessing either; the Discussion’s comparison concerns ChatGPT-3 and ChatGPT-4 instead. (LLM, conf=0.75)
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Ong, J. C. L., Jin, L., Elangovan, K., Lim, G. Y. S., Lim, D. Y. Z., Sng, G. G. R., Ke, Y. H., Tung, J. Y. M., Zhong, R. J., Koh, C. M. Y., et al. (2025) · Cell Reports Medicine · doi:10.1016/j.xcrm.2025.102323 · Cited by 58 · see citing works
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- Authors
- Sheikh, M. S., Dreesman, B., Barreto, E. F., Thongprayoon, C., Miao, J., Suppadungsuk, S., Mao, M. A., Qureshi, F., Pham, J. H., Craici, I. M., Kashani, K. B., & Cheungpasitporn, W.
- Affiliations
- Mayo Clinic (US)
- Corresponding author
- Cheungpasitporn, Wisit
- ORCID
- Venue
- Renal Failure
- Publisher
- Informa UK Limited
- Published
- 2024-09-11
- Volume
- 46
- Issue
- 2
- Pages
- 2402075-2402075
- ISSN
- 0886-022X (ISSN-L)
- 1525-6049
- PMC
- PMC11391868
- OpenAlex
- W4402460406
- Cited by
- 5
- References
- 32
- Citations by year
- 2026: 3 · 2025: 2
- FWCI
- 0.47
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Renal Failure (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Pharmacology › Pharmacy and Medical Practices
- Keywords
- ChatGPT-4, drug identification, large language models, hydrochlorothiazide, nifedipine, spironolactone, model consistency, digital health
- MeSH terms
- Smartphone, Artificial Intelligence, Humans
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