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Clinical and Surgical Applications of Large Language Models: A Systematic Review.
Pressman, Sophia M; Borna, Sahar; Gomez-Cabello, Cesar A; Haider, Syed Ali; Haider, Clifton R; Forte, Antonio Jorge.
Afiliación
  • Pressman SM; Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Borna S; Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Gomez-Cabello CA; Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Haider SA; Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
  • Haider CR; Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, MN 55905, USA.
  • Forte AJ; Division of Plastic Surgery, Mayo Clinic, Jacksonville, FL 32224, USA.
J Clin Med ; 13(11)2024 May 22.
Article en En | MEDLINE | ID: mdl-38892752
ABSTRACT

Background:

Large language models (LLMs) represent a recent advancement in artificial intelligence with medical applications across various healthcare domains. The objective of this review is to highlight how LLMs can be utilized by clinicians and surgeons in their everyday practice.

Methods:

A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Six databases were searched to identify relevant articles. Eligibility criteria emphasized articles focused primarily on clinical and surgical applications of LLMs.

Results:

The literature search yielded 333 results, with 34 meeting eligibility criteria. All articles were from 2023. There were 14 original research articles, four letters, one interview, and 15 review articles. These articles covered a wide variety of medical specialties, including various surgical subspecialties.

Conclusions:

LLMs have the potential to enhance healthcare delivery. In clinical settings, LLMs can assist in diagnosis, treatment guidance, patient triage, physician knowledge augmentation, and administrative tasks. In surgical settings, LLMs can assist surgeons with documentation, surgical planning, and intraoperative guidance. However, addressing their limitations and concerns, particularly those related to accuracy and biases, is crucial. LLMs should be viewed as tools to complement, not replace, the expertise of healthcare professionals.
Palabras clave

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Med Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Clin Med Año: 2024 Tipo del documento: Article País de afiliación: Estados Unidos Pais de publicación: Suiza