Investigating the utility of large language models for image-based rare disease phenotyping

• 발행기관: 대한의학유전학회
• 저자명: Jihoon G. Yoon
• 간행물 정보: 『대한의학유전학회지』제22권 제1호, 7~15쪽, 전체 9쪽
• 주제분류: 의약학 > 기타의약학
• 파일형태: PDF
• 발행일자: 2025.06.30

국문 초록

Purpose: Artificial intelligence has been applied across various fields of medicine, with large language models (LLMs) demonstrating potential to assist in clinical decision-making for rare diseases. This study assessed the performance of LLMs in aiding the phenotyping process and guiding the identification of correct genetic conditions. Materials and Methods: Clinical images from 10 Korean individuals with genetically confirmed rare diseases were collected through a literature review. Using identical prompts, the top 10 Human Phenotype Ontology (HPO) terms and suspected genetic conditions were queried across three LLMs: generative pre-trained transformers (GPTs) models GPT-4o and GPT o1, and Claude 3.5 Sonnet. Concordance between 5 manually curated key HPO terms and the top 10 predicted terms were assessed, and the accuracy of genetic diagnoses among the LLMs was analyzed. Results: Clinical images, ranging from 3 to 9 per case, were used as input for 10 Korean rare disease cases. The average number of key HPO terms correctly matched among the top 10 predictions was 3.0 (1.99-4.01) for GPT-4o, 2.8 (2.06-3.54) for GPT o1, and 1.7 (0.80-2.60) for Claude 3.5 (mean, [95% confidence interval]). GPT models provided more specific HPO terms than Claude in these cases. The accuracy of genetic diagnosis within the top 10 predictions was 2/10 for GPT-4o, 3/10 for GPT o1, and 0/10 for Claude 3.5, with frequent hallucination events observed. Conclusion: LLMs demonstrate potential as a supportive tool for image-based rare disease phenotyping, while the frequent hallucinations highlight the need for further investigation and caution in clinical application.

목차

Introduction

Materials and Methods

Results

Discussion

Acknowledgements

Funding

References

키워드

Artificial intelligence Large language models Rare diseases Phenotype