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      A descriptive study based on the comparison of ChatGPT and evidence-based neurosurgeons

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          Summary

          ChatGPT is an artificial intelligence product developed by OpenAI. This study aims to investigate whether ChatGPT can respond in accordance with evidence-based medicine in neurosurgery. We generated 50 neurosurgical questions covering neurosurgical diseases. Each question was posed three times to GPT-3.5 and GPT-4.0. We also recruited three neurosurgeons with high, middle, and low seniority to respond to questions. The results were analyzed regarding ChatGPT’s overall performance score, mean scores by the items’ specialty classification, and question type. In conclusion, GPT-3.5’s ability to respond in accordance with evidence-based medicine was comparable to that of neurosurgeons with low seniority, and GPT-4.0’s ability was comparable to that of neurosurgeons with high seniority. Although ChatGPT is yet to be comparable to a neurosurgeon with high seniority, future upgrades could enhance its performance and abilities.

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          • •

            GPT-3.5’s ability was comparable to neurosurgeons with low seniority

          • •

            GPT-4.0’s ability was comparable to neurosurgeons with high seniority

          • •

            Future upgrades of ChatGPT could enhance its performance and abilities

          Abstract

          Health informatics; Neurology; Neurosurgery; Artificial intelligence applications

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          Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models

          Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla … (2023)
          We evaluated the performance of a large language model called ChatGPT on the United States Medical Licensing Exam (USMLE), which consists of three exams: Step 1, Step 2CK, and Step 3. ChatGPT performed at or near the passing threshold for all three exams without any specialized training or reinforcement. Additionally, ChatGPT demonstrated a high level of concordance and insight in its explanations. These results suggest that large language models may have the potential to assist with medical education, and potentially, clinical decision-making.
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            ChatGPT: five priorities for research

            Eva A. M. van Dis, Johan Bollen, Willem  Zuidema … (2023)
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              Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success.

              Kensaku Kawamoto, Caitlin Houlihan, E. Balas … (2005)
              To identify features of clinical decision support systems critical for improving clinical practice. Systematic review of randomised controlled trials. Literature searches via Medline, CINAHL, and the Cochrane Controlled Trials Register up to 2003; and searches of reference lists of included studies and relevant reviews. Studies had to evaluate the ability of decision support systems to improve clinical practice. Studies were assessed for statistically and clinically significant improvement in clinical practice and for the presence of 15 decision support system features whose importance had been repeatedly suggested in the literature. Seventy studies were included. Decision support systems significantly improved clinical practice in 68% of trials. Univariate analyses revealed that, for five of the system features, interventions possessing the feature were significantly more likely to improve clinical practice than interventions lacking the feature. Multiple logistic regression analysis identified four features as independent predictors of improved clinical practice: automatic provision of decision support as part of clinician workflow (P < 0.00001), provision of recommendations rather than just assessments (P = 0.0187), provision of decision support at the time and location of decision making (P = 0.0263), and computer based decision support (P = 0.0294). Of 32 systems possessing all four features, 30 (94%) significantly improved clinical practice. Furthermore, direct experimental justification was found for providing periodic performance feedback, sharing recommendations with patients, and requesting documentation of reasons for not following recommendations. Several features were closely correlated with decision support systems' ability to improve patient care significantly. Clinicians and other stakeholders should implement clinical decision support systems that incorporate these features whenever feasible and appropriate.
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                Author and article information

                Contributors
                Dongdong Wu
                Chengliang Yin
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 09 August 2023
                Publication date Collection: 15 September 2023
                Publication date (Electronic): 09 August 2023
                Volume: 26
                Issue: 9
                Electronic Location Identifier: 107590
                Affiliations
                [1 ]Department of Neurosurgery, the First Medical Centre, Chinese PLA General Hospital, Beijing 100853, China
                [2 ]School of Health Humanities, Peking University, Beijing 100191, China
                [3 ]Department of Graduate School, Xinjiang Medical University, Urumqi 830001, China
                [4 ]Department of Information, Daping Hospital, Army Medical University, Chongqing 400042, China
                [5 ]Faculty of Medicine, Macau University of Science and Technology, Macau 999078, China
                Author notes
                [∗ ]Corresponding author 604269346@ 123456qq.com
                [∗∗ ]Corresponding author chengliangyin@ 123456163.com
                [6]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(23)01667-X Publisher ID: 107590
                DOI: 10.1016/j.isci.2023.107590
                PMC ID: 10495632
                PubMed ID: 37705958
                SO-VID: 9884c748-f6a7-44cf-8e72-d4326514ebbf
                Copyright © © 2023 The Authors
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 31 May 2023
                Date revision received : 21 June 2023
                Date accepted : 4 August 2023
                Categories
                Subject: Article

                Keywords: health informatics,neurology,neurosurgery,artificial intelligence applications
                Data availability:
                Keywords: health informatics, neurology, neurosurgery, artificial intelligence applications

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