Artificial intelligence chatbot vs pathology faculty and residents: Real-world clinical questions from a genitourinary treatment planning conference.

artificial intelligence genitourinary pathology natural language processing treatment planning conference

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:
28 Jun 2024
Historique:
received: 03 01 2024
accepted: 29 05 2024
medline: 28 6 2024
pubmed: 28 6 2024
entrez: 28 6 2024
Statut: aheadofprint

Résumé

Artificial intelligence (AI)-based chatbots have demonstrated accuracy in a variety of fields, including medicine, but research has yet to substantiate their accuracy and clinical relevance. We evaluated an AI chatbot's answers to questions posed during a treatment planning conference. Pathology residents, pathology faculty, and an AI chatbot (OpenAI ChatGPT [January 30, 2023, release]) answered a questionnaire curated from a genitourinary subspecialty treatment planning conference. Results were evaluated by 2 blinded adjudicators: a clinician expert and a pathology expert. Scores were based on accuracy and clinical relevance. Overall, faculty scored highest (4.75), followed by the AI chatbot (4.10), research-prepared residents (3.50), and unprepared residents (2.87). The AI chatbot scored statistically significantly better than unprepared residents (P = .03) but not statistically significantly different from research-prepared residents (P = .33) or faculty (P = .30). Residents did not statistically significantly improve after research (P = .39), and faculty performed statistically significantly better than both resident categories (unprepared, P < .01; research prepared, P = .01). The AI chatbot gave answers to medical questions that were comparable in accuracy and clinical relevance to pathology faculty, suggesting promise for further development. Serious concerns remain, however, that without the ability to provide support with references, AI will face legitimate scrutiny as to how it can be integrated into medical decision-making.

Identifiants

pubmed: 38940388
pii: 7700986
doi: 10.1093/ajcp/aqae078
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© American Society for Clinical Pathology, 2024.

Auteurs

Matthew X Luo (MX)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Adam Lyle (A)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Phillip Bennett (P)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Daniel Albertson (D)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Deepika Sirohi (D)

Department of Pathology and Laboratory Medicine, University of California, San Francisco, San Francisco, CA, US.

Benjamin L Maughan (BL)

Department of Medical Oncology, University of Utah, Salt Lake City, UT, US.

Valarie McMurtry (V)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Jonathon Mahlow (J)

Department of Pathology, University of Utah, Salt Lake City, UT, US.

Classifications MeSH