Your browser doesn't support javascript.
loading
Artificial Intelligence and Machine Learning in Prediction of Surgical Complications: Current State, Applications, and Implications.
Hassan, Abbas M; Rajesh, Aashish; Asaad, Malke; Nelson, Jonas A; Coert, J Henk; Mehrara, Babak J; Butler, Charles E.
Affiliation
  • Hassan AM; Department of Plastic and Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
  • Rajesh A; Department of Surgery, 14742University of Texas Health Science Center, San Antonio, TX, USA.
  • Asaad M; Department of Plastic Surgery, 6595University of Pittsburgh Medical Center, Pittsburgh, PA, USA.
  • Nelson JA; Department of Plastic and Reconstructive Surgery, 5803Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Coert JH; Department of Plastic and Reconstructive Surgery, 8124University Medical Center Utrecht, Utrecht, Netherlands.
  • Mehrara BJ; Department of Plastic and Reconstructive Surgery, 5803Memorial Sloan Kettering Cancer Center, New York, NY, USA.
  • Butler CE; Department of Plastic and Reconstructive Surgery, 571198The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
Am Surg ; 89(1): 25-30, 2023 Jan.
Article in En | MEDLINE | ID: mdl-35562124
Surgical complications pose significant challenges for surgeons, patients, and health care systems as they may result in patient distress, suboptimal outcomes, and higher health care costs. Artificial intelligence (AI)-driven models have revolutionized the field of surgery by accurately identifying patients at high risk of developing surgical complications and by overcoming several limitations associated with traditional statistics-based risk calculators. This article aims to provide an overview of AI in predicting surgical complications using common machine learning and deep learning algorithms and illustrates how this can be utilized to risk stratify patients preoperatively. This can form the basis for discussions on informed consent based on individualized patient factors in the future.
Subject(s)
Key words

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Surgeons Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Am Surg Year: 2023 Document type: Article Affiliation country: United States Country of publication: United States

Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Artificial Intelligence / Surgeons Type of study: Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: Am Surg Year: 2023 Document type: Article Affiliation country: United States Country of publication: United States