Capacity for large language model chatbots to aid in orthopedic management, research, and patient queries

Author:

Sosa Branden R.1,Cung Michelle1,Suhardi Vincentius J.2,Morse Kyle3,Thomson Andrew2,Yang He S.1,Iyer Sravisht3,Greenblatt Matthew B.12ORCID

Affiliation:

1. Department of Pathology and Laboratory Medicine Weill Cornell Medical College New York New York USA

2. Research Division and Department of Orthopedic Surgery Hospital for Special Surgery New York New York USA

3. Department of Spine Surgery Hospital for Special Surgery New York New York USA

Abstract

AbstractLarge language model (LLM) chatbots possess a remarkable capacity to synthesize complex information into concise, digestible summaries across a wide range of orthopedic subject matter. As LLM chatbots become widely available they will serve as a powerful, accessible resource that patients, clinicians, and researchers may reference to obtain information about orthopedic science and clinical management. Here, we examined the performance of three well‐known and easily accessible chatbots–ChatGPT, Bard, and Bing AI–in responding to inquiries relating to clinical management and orthopedic concepts. Although all three chatbots were found to be capable of generating relevant responses, ChatGPT outperformed Bard and BingAI in each category due to its ability to provide accurate and complete responses to orthopedic queries. Despite their promising applications in clinical management, shortcomings observed included incomplete responses, lack of context, and outdated information. Nonetheless, the ability for these LLM chatbots to address these inquires has largely yet to be evaluated and will be critical for understanding the risks and opportunities of LLM chatbots in orthopedics.

Publisher

Wiley

Subject

Orthopedics and Sports Medicine

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