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Exploring the potential of ChatGPT as a supplementary tool for providing orthopaedic information

Kaarre, Janina, 1996 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för ortopedi,Institute of Clinical Sciences, Department of Orthopaedics,University of Gothenburg,University of Pittsburgh
Feldt, Robert, 1972 (författare)
Chalmers tekniska högskola,Chalmers University of Technology
Keeling, L. E. (författare)
University of Pittsburgh
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Dadoo, S. (författare)
University of Pittsburgh
Zsidai, Balint, 1993 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för ortopedi,Institute of Clinical Sciences, Department of Orthopaedics,University of Gothenburg
Hughes, J. D. (författare)
University of Pittsburgh
Samuelsson, Kristian, 1977 (författare)
Gothenburg University,Göteborgs universitet,Institutionen för kliniska vetenskaper, Avdelningen för ortopedi,Institute of Clinical Sciences, Department of Orthopaedics,University of Gothenburg,Sahlgrenska universitetssjukhuset,Sahlgrenska University Hospital
Musahl, V. (författare)
University of Pittsburgh
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 (creator_code:org_t)
2023
2023
Engelska.
Ingår i: Knee Surgery Sports Traumatology Arthroscopy. - 0942-2056 .- 1433-7347.
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • PurposeTo investigate the potential use of large language models (LLMs) in orthopaedics by presenting queries pertinent to anterior cruciate ligament (ACL) surgery to generative pre-trained transformer (ChatGPT, specifically using its GPT-4 model of March 14th 2023). Additionally, this study aimed to evaluate the depth of the LLM's knowledge and investigate its adaptability to different user groups. It was hypothesized that the ChatGPT would be able to adapt to different target groups due to its strong language understanding and processing capabilities.MethodsChatGPT was presented with 20 questions and response was requested for two distinct target audiences: patients and non-orthopaedic medical doctors. Two board-certified orthopaedic sports medicine surgeons and two expert orthopaedic sports medicine surgeons independently evaluated the responses generated by ChatGPT. Mean correctness, completeness, and adaptability to the target audiences (patients and non-orthopaedic medical doctors) were determined. A three-point response scale facilitated nuanced assessment.ResultsChatGPT exhibited fair accuracy, with average correctness scores of 1.69 and 1.66 (on a scale from 0, incorrect, 1, partially correct, to 2, correct) for patients and medical doctors, respectively. Three of the 20 questions (15.0%) were deemed incorrect by any of the four orthopaedic sports medicine surgeon assessors. Moreover, overall completeness was calculated to be 1.51 and 1.64 for patients and medical doctors, respectively, while overall adaptiveness was determined to be 1.75 and 1.73 for patients and doctors, respectively.ConclusionOverall, ChatGPT was successful in generating correct responses in approximately 65% of the cases related to ACL surgery. The findings of this study imply that LLMs offer potential as a supplementary tool for acquiring orthopaedic knowledge. However, although ChatGPT can provide guidance and effectively adapt to diverse target audiences, it cannot supplant the expertise of orthopaedic sports medicine surgeons in diagnostic and treatment planning endeavours due to its limited understanding of orthopaedic domains and its potential for erroneous responses.

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Ortopedi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Orthopaedics (hsv//eng)
MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Kirurgi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Surgery (hsv//eng)

Nyckelord

Large language models
ChatGPT
Anterior cruciate ligament
ACL
Artificial intelligence
Correctness
ChatGPT

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