Journal article
Accuracy and Completeness of Large Language Models About Antibody-Drug Conjugates and Associated Ocular Adverse Effects
Cornea, v 44(7), pp 851-855
01 Jul 2025
PMID: 39110155
Abstract
Purpose:The purpose of this study was to assess the accuracy and completeness of 3 large language models (LLMs) to generate information about antibody-drug conjugate (ADC)-associated ocular toxicities.Methods:There were 22 questions about ADCs, tisotumab vedotin, and mirvetuximab soravtansine that were developed and input into ChatGPT 4.0, Bard, and LLaMa. Answers were rated by 4 ocular toxicity experts using standardized 6-point Likert scales on accuracy and completeness. ANOVA tests were conducted for comparison between the 3 subgroups, followed by pairwise t-tests. Interrater variability was assessed with Fleiss kappa tests.Results:The mean accuracy score was 4.62 (SD 0.89) for ChatGPT, 4.77 (SD 0.90) for Bard, and 4.41 (SD 1.09) for LLaMA. Both ChatGPT (P = 0.03) and Bard (P = 0.003) scored significantly better for accuracy when compared with LLaMA. The mean completeness score was 4.43 (SD 0.91) for ChatGPT, 4.57 (SD 0.93) for Bard, and 4.42 (SD 0.99) for LLaMA. There were no significant differences in completeness scores between groups. Fleiss kappa assessment for interrater variability was good (0.74) for accuracy and fair (0.31) for completeness.Conclusions:All 3 LLMs had relatively high accuracy and completeness ratings, showing LLMs are able to provide sufficient answers for niche topics of ophthalmology. Our results indicate that ChatGPT and Bard may be slightly better at providing more accurate answers than LLaMA. As further research and treatment plans are developed for ADC-associated ocular toxicities, these LLMs should be reassessed to see if they provide complete and accurate answers that remain in line with current medical knowledge.
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Details
- Title
- Accuracy and Completeness of Large Language Models About Antibody-Drug Conjugates and Associated Ocular Adverse Effects
- Creators
- Rayna Marshall - Drexel UniversityHannah Xu - University of California San DiegoLauren A. Dalvin - Mayo Clinic in ArizonaKapil Mishra - University of California, IrvineCamellia Edalat - Drexel UniversityNila Kirupaharan - Drexel UniversityJasmine H. Francis - Memorial Sloan Kettering Cancer CenterMeghan Berkenstock (Corresponding Author) - Johns Hopkins University
- Publication Details
- Cornea, v 44(7), pp 851-855
- Publisher
- Lippincott Williams & Wilkins
- Number of pages
- 5
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- College of Medicine
- Web of Science ID
- WOS:001501591300001
- Scopus ID
- 2-s2.0-85201053492
- Other Identifier
- 991022197319204721