ChatGPT in Occupational Medicine: A Comparative Study with Human Experts.

ChatGPT artificial intelligence digital health health promotion large language model occupational health and safety

Journal

Bioengineering (Basel, Switzerland)
ISSN: 2306-5354
Titre abrégé: Bioengineering (Basel)
Pays: Switzerland
ID NLM: 101676056

Informations de publication

Date de publication:
06 Jan 2024
Historique:
received: 07 12 2023
revised: 01 01 2024
accepted: 04 01 2024
medline: 22 1 2024
pubmed: 22 1 2024
entrez: 22 1 2024
Statut: epublish

Résumé

The objective of this study is to evaluate ChatGPT's accuracy and reliability in answering complex medical questions related to occupational health and explore the implications and limitations of AI in occupational health medicine. The study also provides recommendations for future research in this area and informs decision-makers about AI's impact on healthcare. A group of physicians was enlisted to create a dataset of questions and answers on Italian occupational medicine legislation. The physicians were divided into two teams, and each team member was assigned a different subject area. ChatGPT was used to generate answers for each question, with/without legislative context. The two teams then evaluated human and AI-generated answers blind, with each group reviewing the other group's work. Occupational physicians outperformed ChatGPT in generating accurate questions on a 5-point Likert score, while the answers provided by ChatGPT with access to legislative texts were comparable to those of professional doctors. Still, we found that users tend to prefer answers generated by humans, indicating that while ChatGPT is useful, users still value the opinions of occupational medicine professionals.

Identifiants

pubmed: 38247934
pii: bioengineering11010057
doi: 10.3390/bioengineering11010057
pii:
doi:

Types de publication

Journal Article

Langues

eng

Auteurs

Martina Padovan (M)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Bianca Cosci (B)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Armando Petillo (A)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Gianluca Nerli (G)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Francesco Porciatti (F)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Sergio Scarinci (S)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Francesco Carlucci (F)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Letizia Dell'Amico (L)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Niccolò Meliani (N)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Gabriele Necciari (G)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Vincenzo Carmelo Lucisano (VC)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Riccardo Marino (R)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Rudy Foddis (R)

Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.

Alessandro Palla (A)

Intel Corporation, Santa Clara, CA 95054, USA.

Classifications MeSH