ChatGPT can yield valuable responses in the context of orthopaedic trauma surgery
Artikel i vetenskaplig tidskrift, 2024

Purpose: To assess the possibility of using Generative Pretrained Transformer (ChatGPT) specifically in the context of orthopaedic trauma surgery by questions posed to ChatGPT and to evaluate responses (correctness, completeness and adaptiveness) by orthopaedic trauma surgeons. Methods: ChatGPT (GPT-4 of 12 May 2023) was asked to address 34 common orthopaedic trauma surgery-related questions and generate responses suited to three target groups: patient, nonorthopaedic medical doctor and expert orthopaedic surgeon. Three orthopaedic trauma surgeons independently assessed ChatGPT's responses by using a three-point response scale with a response range between 0 and 2, where a higher number indicates better performance (correctness, completeness and adaptiveness). Results: A total of 18 (52.9%) of all responses were assessed to be correct (2.0) for the patient target group, while 22 (64.7%) and 24 (70.5%) of the responses were determined to be correct for nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Moreover, a total of 18 (52.9%), 25 (73.5%) and 28 (82.4%) of the responses were assessed to be complete (2.0) for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. The average adaptiveness was 1.93, 1.95 and 1.97 for patients, nonorthopaedic medical doctors and expert orthopaedic surgeons, respectively. Conclusion: The study results indicate that ChatGPT can yield valuable and overall correct responses in the context of orthopaedic trauma surgery across different target groups, which encompassed patients, nonorthopaedic medical surgeons and expert orthopaedic surgeons. The average correctness scores, completeness levels and adaptiveness values indicated the ability of ChatGPT to generate overall correct and complete responses adapted to the target group. Level of Evidence: Not applicable.

artificial intelligence

AI

large language models

LLMs trauma orthopaedics

Författare

Janina Kaarre

Sahlgrenska universitetssjukhuset

UPMC Sports Medicine

Göteborgs universitet

Robert Feldt

Chalmers, Data- och informationsteknik, Software Engineering

Bálint Zsidai

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Eric Hamrin Senorski

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Emilia Möller Rydberg

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Olof Wolf

Uppsala universitet

Sebastian Mukka

Umeå universitet

Michael Möller

Sahlgrenska universitetssjukhuset

Göteborgs universitet

Kristian Samuelsson

Göteborgs universitet

Sahlgrenska universitetssjukhuset

Journal of Experimental Orthopaedics

2197-1153 (eISSN)

Vol. 11 3 e12047

Ämneskategorier

Kirurgi

Ortopedi

DOI

10.1002/jeo2.12047

Mer information

Senast uppdaterat

2024-07-02