Limitations of broadly trained LLMs in interpreting orthopedic Walch glenoid classifications.

ABSTRACT: Artificial intelligence (AI) integration in medical practice has grown substantially, with physician use nearly doubling from 38% in 2023 to 68% in 2024. Recent advances in large language models (LLMs) include multimodal inputs, showing potential for medical image interpretation and clinical software integrations. This study evaluated the accuracy of two popular LLMs, Claude 3.5 Sonnet and DeepSeek R1, in interpreting glenoid diagrams using Walch glenoid classification in preoperative shoulder reconstruction applications. Test images included seven black-white Walch glenoid diagrams from Radiopedia. LLMs were accessed via Perplexity.ai without specialized medical training. LLMs were tested across multiple conversation threads with prompt instructions of varying length, ranging from 22 to 8

SUBMITTER: ElSayed A 

PROVIDER: S-EPMC12424433 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature