Evaluation of ChatGPT pathology knowledge using board-style questions.

ChatGPT artificial intelligence chatbot natural language processing neural networks pathology education

Journal

American journal of clinical pathology
ISSN: 1943-7722
Titre abrégé: Am J Clin Pathol
Pays: England
ID NLM: 0370470

Informations de publication

Date de publication:
02 Dec 2023
Historique:
received: 08 06 2023
accepted: 16 10 2023
medline: 2 12 2023
pubmed: 2 12 2023
entrez: 2 12 2023
Statut: aheadofprint

Résumé

ChatGPT is an artificial intelligence chatbot developed by OpenAI. Its extensive knowledge and unique interactive capabilities enable its use in various innovative ways in the medical field, such as writing clinical notes and simplifying radiology reports. Through this study, we aimed to analyze the pathology knowledge of ChatGPT to advocate its role in transforming pathology education. The American Society for Clinical Pathology Resident Question Bank 2022 was used to test ChatGPT, version 4. Practice tests were created in each subcategory and answered based on the input that ChatGPT provided. Questions that required interpretation of images were excluded. We analyzed ChatGPT performance and compared it with average peer performance. The overall performance of ChatGPT was 56.98%, lower than that of the average peer performance of 62.81%. ChatGPT performed better on clinical pathology (60.42%) than on anatomic pathology (54.94%). Furthermore, its performance was better on easy questions (68.47%) than on intermediate (52.88%) and difficult questions (37.21%). ChatGPT has the potential to be a valuable resource in pathology education if trained on a larger, specialized medical data set. Those relying on it (in its current form) solely for the purpose of pathology training should be cautious.

Identifiants

pubmed: 38041797
pii: 7457992
doi: 10.1093/ajcp/aqad158
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© The Author(s) 2023. Published by Oxford University Press on behalf of American Society for Clinical Pathology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Auteurs

Saroja D Geetha (SD)

Department of Pathology and Laboratory Medicine, North Shore University Hospital and Long Island Jewish Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Greenvale, NY, US.

Anam Khan (A)

Department of Pathology and Laboratory Medicine, North Shore University Hospital and Long Island Jewish Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Greenvale, NY, US.

Atif Khan (A)

Department of Pathology and Laboratory Medicine, North Shore University Hospital and Long Island Jewish Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Greenvale, NY, US.

Bijun S Kannadath (BS)

Department of Internal Medicine, University of Arizona College of Medicine, Phoenix, AZ, US.

Taisia Vitkovski (T)

Department of Pathology and Laboratory Medicine, North Shore University Hospital and Long Island Jewish Medical Center, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell Health, Greenvale, NY, US.

Classifications MeSH