ArticleAmerican journal of human genetics2024
Evaluating large language models on medical, lay-language, and self-reported descriptions of genetic conditions.
Article in American journal of human genetics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Disparate language and model effects on AI-based translation and recognition of genetic conditions.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- Large Language Model Hallucinations in Spine Surgery: A Comparative Analysis of Clinician vs Patient-Level Prompts.Neurosurgery practice · 2026Article
- Artificial intelligence in genomic medicine: dispelling three myths.NPJ genomic medicine · 2026Article
- Increasing Large Language Model Accuracy for Care-Seeking Advice Using Prompts Reflecting Human Reasoning Strategies in the Real World: Validation Study.JMIR biomedical engineering · 2026Article
- Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support tools.European journal of human genetics : EJHG · 2026Article
- Comparing five generative AI chatbots' answers to LLM-generated clinical questions with medical information scientists' evidence summaries.Journal of the Medical Library Association : JMLA · 2026Article
- Genetic analysis of neurodegenerative diseases.The Journal of clinical investigation · 2026Review
- Benchmarking five large language models in medical genetics: a bilingual comparative evaluation using published and novel expert-authored questions.Frontiers in medicine · 2026Article
- Consistent performance of large language models in rare disease diagnosis across ten languages and 4917 cases.EBioMedicine · 2025Article
- Perspectives on the Current and Future State of Artificial Intelligence in Medical Genetics.American journal of medical genetics. Part A · 2025Review
- Comparing Five Generative AI Chatbots' Answers to LLM-Generated Clinical Questions with Medical Information Scientists' Evidence Summaries.medRxiv : the preprint server for health sciences · 2025Article
- AI-driven simplification of surgical reports in gynecologic oncology: A potential tool for patient education.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases.NPJ digital medicine · 2025Article
- Evaluating Large Language Models for Preoperative Patient Education in Superior Capsular Reconstruction: Comparative Study of Claude, GPT, and Gemini.JMIR perioperative medicine · 2025Article
- Article
- Artificial intelligence in clinical genetics.European journal of human genetics : EJHG · 2025Review
- Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 Cases.medRxiv : the preprint server for health sciences · 2025Article
- Survey and improvement strategies for gene prioritization with large language models.Bioinformatics advances · 2025Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Large language models (LLMs) are generating interest in medical settings. For example, LLMs can respond coherently to medical queries by providing plausible differential diagnoses based on clinical notes. However, there are many questions to explore, such as evaluating differences between open- and closed-source LLMs as well as LLM performance on queries from both medical and non-medical users. In this study, we assessed multiple LLMs, including Llama-2-chat, Vicuna, Medllama2, Bard/Gemini, Claude, ChatGPT3.5, and ChatGPT-4, as well as non-LLM approaches (Google search and Phenomizer) regarding their ability to identify genetic conditions from textbook-like clinician questions and their corresponding layperson translations related to 63 genetic conditions. For open-source LLMs, larger models were more accurate than smaller LLMs: 7b, 13b, and larger than 33b parameter models obtained accuracy ranges from 21%-49%, 41%-51%, and 54%-68%, respectively. Closed-source LLMs outperformed open-source LLMs, with ChatGPT-4 performing best (89%-90%). Three of 11 LLMs and Google search had significant performance gaps between clinician and layperson prompts. We also evaluated how in-context prompting and keyword removal affected open-source LLM performance. Models were provided with 2 types of in-context prompts: list-type prompts, which improved LLM performance, and definition-type prompts, which did not. We further analyzed removal of rare terms from descriptions, which decreased accuracy for 5 of 7 evaluated LLMs. Finally, we observed much lower performance with real individuals' descriptions; LLMs answered these questions with a maximum 21% accuracy.
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