ChatGPT M.D.: Is there any room for generative AI in neurology?

Journal: PloS one
PMID:

Abstract

ChatGPT, a general artificial intelligence, has been recognized as a powerful tool in scientific writing and programming but its use as a medical tool is largely overlooked. The general accessibility, rapid response time and comprehensive training database might enable ChatGPT to serve as a diagnostic augmentation tool in certain clinical settings. The diagnostic process in neurology is often challenging and complex. In certain time-sensitive scenarios, rapid evaluation and diagnostic decisions are needed, while in other cases clinicians are faced with rare disorders and atypical disease manifestations. Due to these factors, the diagnostic accuracy in neurology is often suboptimal. Here we evaluated whether ChatGPT can be utilized as a valuable and innovative diagnostic augmentation tool in various neurological settings. We used synthetic data generated by neurological experts to represent descriptive anamneses of patients with known neurology-related diseases, then the probability for an appropriate diagnosis made by ChatGPT was measured. To give clarity to the accuracy of the AI-determined diagnosis, all cases have been cross-validated by other experts and general medical doctors as well. We found that ChatGPT-determined diagnostic accuracy (ranging from 68.5% ± 3.28% to 83.83% ± 2.73%) can reach the accuracy of other experts (81.66% ± 2.02%), furthermore, it surpasses the probability of an appropriate diagnosis if the examiner is a general medical doctor (57.15% ± 2.64%). Our results showcase the efficacy of general artificial intelligence like ChatGPT as a diagnostic augmentation tool in medicine. In the future, AI-based supporting tools might be useful amendments in medical practice and help to improve the diagnostic process in neurology.

Authors

  • Bernát Nógrádi
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.
  • Tamás Ferenc Polgár
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.
  • Valéria Meszlényi
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.
  • Zalán Kádár
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.
  • Péter Hertelendy
    Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Anett Csáti
    Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • László Szpisjak
    Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Dóra Halmi
    Metabolic Diseases and Cell Signaling Research Group, Department of Biochemistry, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
  • Barbara Erdélyi-Furka
    Metabolic Diseases and Cell Signaling Research Group, Department of Biochemistry, Albert Szent-Györgyi Medical School, University of Szeged, Szeged, Hungary.
  • Máté Tóth
    Second Department of Internal Medicine and Cardiology Centre, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Fanny Molnár
    Department of Family Medicine, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Dávid Tóth
    Department of Oncotherapy, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Zsófia Bősze
    Department of Internal Medicine, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • Krisztina Boda
    Department of Medical Physics and Informatics, University of Szeged, Szeged, Hungary.
  • Péter Klivényi
    Department of Neurology, Albert Szent-Györgyi Health Centre, University of Szeged, Szeged, Hungary.
  • László Siklós
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.
  • Roland Patai
    Institute of Biophysics, HUN-REN Biological Research Centre, Szeged, Hungary.