Advances in artificial intelligence (AI), machine learning, and publicly accessible language model tools such as ChatGPT-3.5 continue to shape the landscape of modern medicine and patient education. ChatGPT's open access (OA), instant, human-sounding interface capable of carrying discussion on myriad topics makes it a potentially useful resource for patients seeking medical advice. As it pertains to orthopedic surgery, ChatGPT may become a source to answer common preoperative questions regarding total knee arthroplasty (TKA) and total hip arthroplasty (THA). Since ChatGPT can utilize the peer-reviewed literature to source its responses, this study seeks to characterize the validity of its responses to common TKA and THA questions and characterize the peer-reviewed literature that it uses to formulate its responses. Preoperative TKA and THA questions were formulated by fellowship-trained adult reconstruction surgeons based on common questions posed by patients in the clinical setting. Questions were inputted into ChatGPT with the initial request of using solely the peer-reviewed literature to generate its responses. The validity of each response was rated on a Likert scale by the fellowship-trained surgeons, and the sources utilized were characterized in terms of accuracy of comparison to existing publications, publication date, study design, level of evidence, journal of publication, journal impact factor based on the clarivate analytics factor tool, journal OA status, and whether the journal is based in the United States. A total of 109 sources were cited by ChatGPT in its answers to 17 questions regarding TKA procedures and 16 THA procedures. Thirty-nine sources (36%) were deemed accurate or able to be directly traced to an existing publication. Of these, seven (18%) were identified as duplicates, yielding a total of 32 unique sources that were identified as accurate and further characterized. The most common characteristics of these sources included dates of publication between 2011 and 2015 (10), publication in The Journal of Bone and Joint Surgery (13), journal impact factors between 5.1 and 10.0 (17), internationally based journals (17), and journals that are not OA (28). The most common study designs were retrospective cohort studies and case series (seven each). The level of evidence was broadly distributed between Levels I, III, and IV (seven each). The averages for the Likert scales for medical accuracy and completeness were 4.4/6 and 1.92/3, respectively. Investigation into ChatGPT's response quality and use of peer-reviewed sources when prompted with archetypal pre-TKA and pre-THA questions found ChatGPT to provide mostly reliable responses based on fellowship-trained orthopedic surgeon review of 4.4/6 for accuracy and 1.92/3 for completeness despite a 64.22% rate of citing inaccurate references. This study suggests that until ChatGPT is proven to be a reliable source of valid information and references, patients must exercise extreme caution in directing their pre-TKA and THA questions to this medium.
Download full-text PDF |
Source |
---|---|
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729512 | PMC |
http://dx.doi.org/10.1155/aort/5534704 | DOI Listing |
Ann Transl Med
December 2024
Division of Advanced Gastrointestinal and Bariatric Surgery, Mayo Clinic, Jacksonville, FL, USA.
Background: Addressing language barriers through accurate interpretation is crucial for providing quality care and establishing trust. While the ability of artificial intelligence (AI) to translate medical documentation has been studied, its role for patient-provider communication is less explored. This review evaluates AI's effectiveness in clinical translation by assessing accuracy, usability, satisfaction, and feedback on its use.
View Article and Find Full Text PDFAdv Orthop
January 2025
Orlando Health Jewett Orthopedic Institute, Orlando, Florida, USA.
Advances in artificial intelligence (AI), machine learning, and publicly accessible language model tools such as ChatGPT-3.5 continue to shape the landscape of modern medicine and patient education. ChatGPT's open access (OA), instant, human-sounding interface capable of carrying discussion on myriad topics makes it a potentially useful resource for patients seeking medical advice.
View Article and Find Full Text PDFFront Child Adolesc Psychiatry
July 2023
Ingham Institute, Liverpool, NSW, Australia.
Objective: To examine and synthesize the literature on the use of universal developmental screening and surveillance tools in high-income countries in relation to (1) psychometric properties; (2) knowledge, acceptability, and feasibility of tools; and (3) follow-up taken following screening/surveillance.
Method: A PRISMA-compliant systematic review was performed in the PsychInfo, PubMed, and Embase databases. Studies published in the English language were included if they reported results evaluating a universal developmental screening or surveillance measurement tool.
Front Parasitol
August 2023
Division of Infectious Diseases, Department of Medicine, Washington University School of Medicine, St. Louis, MO, United States.
Introduction: Soil transmitted nematodes are impediments to human health and agricultural production. Poor anthelmintic efficiencies, the emergence of resistant strains, and the persistence of infective stages highlight the need for more effective control strategies. Parasitic nematodes elicit a Th2-type immune response that most often is not protective.
View Article and Find Full Text PDFFront Antibiot
August 2023
Digital One Health Lab, Roslin Institute, The Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, United Kingdom.
Antimicrobial resistance (AMR) is a major threat to global health and a key One Health challenge linking humans, animals, and the environment. Livestock are a key target for moderation of antimicrobial use (AMU), which is a major driver of AMR in these species. While some studies have assessed AMU and AMR in individual production systems, the evidence regarding predictors of AMU and AMR in livestock is fragmented, with significant research gaps in identifying the predictors of AMU and AMR common across farming systems.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!