Assessing the Soft Tissue Infection Expertise of ChatGPT and Bard Compared to IDSA Recommendations.

AI Concordance Guidelines Infectious disease diagnosis Soft tissue infections

Journal

Annals of biomedical engineering
ISSN: 1573-9686
Titre abrégé: Ann Biomed Eng
Pays: United States
ID NLM: 0361512

Informations de publication

Date de publication:
21 Oct 2023
Historique:
received: 01 09 2023
accepted: 12 09 2023
medline: 22 10 2023
pubmed: 22 10 2023
entrez: 21 10 2023
Statut: aheadofprint

Résumé

The aim of the study was to evaluate whether ChatGPT-3.5 and Bard provide safe and reliable medical answers to common topics related to soft tissue infections and their management according to the guidelines provided by the Infectious Disease Society of America (IDSA). IDSA's abridged recommendations for soft tissue infections were identified on the IDSA official website. Twenty-five queries were entered into the LLMs as they appear on the IDSA website. To assess the concordance and precision of the LLMs' responses with the IDSA guidelines, two infectious disease physicians independently compared and evaluated each response. This was done using a 5-point Likert scale, with 1 representing poor concordance and 5 excellent concordance, as adapted from the validated Global Quality Scale. The mean ± SD score for ChatGPT-generated responses was 4.34 ± 0.74, n = 25. This indicates that raters found the answers were good to excellent quality with the most important topics covered. Although some topics were not covered, the answers were in good concordance with the IDSA guidelines. The mean ± SD score for Bard-generate responses was 3.5 ± 1.2, n = 25, indicating moderate quality. Despite LLMs did not appear to provide wrong recommendations and covered most of the topics, the responses were often found to be generic, rambling, missing some details, and lacking actionability. As AI continues to evolve and researchers feed it with more extensive and diverse medical knowledge, it may be inching closer to becoming a reliable aid for clinicians, ultimately enhancing the accuracy of infectious disease diagnosis and management in the future.

Identifiants

pubmed: 37865615
doi: 10.1007/s10439-023-03372-1
pii: 10.1007/s10439-023-03372-1
doi:

Types de publication

Letter

Langues

eng

Sous-ensembles de citation

IM

Informations de copyright

© 2023. The Author(s) under exclusive licence to Biomedical Engineering Society.

Références

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Auteurs

Mario Alessandri-Bonetti (M)

Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 Locust Street, Pittsburgh, PA, G10315219, USA.

Riccardo Giorgino (R)

Residency Program in Orthopaedics and Traumatology, University of Milan, 20122, Milan, Italy. riccardo.giorgino@unimi.it.
Harvard Medical School, Boston, MA, USA. riccardo.giorgino@unimi.it.

Michelle Naegeli (M)

Department of Pathology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
Harvard Medical School, Boston, MA, USA.

Hilary Y Liu (HY)

Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 Locust Street, Pittsburgh, PA, G10315219, USA.

Francesco M Egro (FM)

Department of Plastic Surgery, University of Pittsburgh Medical Center, 1350 Locust Street, Pittsburgh, PA, G10315219, USA.

Classifications MeSH