Will Artificial Intelligence Be "Better" Than Humans in the Management of Syncope?

Autor:	Dipaola F; Internal Medicine, IRCCS Humanitas Research Hospital, Rozzano, Italy., Gebska MA; Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Gatti M; IBM Technology Expert Labs, Milan, Italy., Levra AG; Department of Biomedical Sciences, Humanitas University, Milan, Italy., Parker WH; Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Menè R; Cardiac Arrhythmia Department, Bordeaux University Hospital, INSERM, Bordeaux, France.; IHU LIRYC, Electrophysiology and Heart Modeling Institute, Bordeaux, France., Lee S; Department of Emergency Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Costantino G; Emergency Department, IRCCS Ca' Granda, Ospedale Maggiore, Milano, Italy., Barsotti EJ; Department of Epidemiology, College of Public Health, University of Iowa, Iowa City, Iowa, USA., Shiffer D; Department of Biomedical Sciences, Humanitas University, Milan, Italy., Johnston SL; Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Sutton R; Department of Cardiology, Hammersmith Hospital Campus, National Heart & Lung Institute, Imperial College, London, United Kingdom., Olshansky B; Division of Cardiovascular Medicine, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA., Furlan R; Internal Medicine, IRCCS Humanitas Research Hospital, Rozzano, Italy.; Department of Biomedical Sciences, Humanitas University, Milan, Italy.
Jazyk:	angličtina
Zdroj:	JACC. Advances [JACC Adv] 2024 Jul 31; Vol. 3 (9), pp. 101072. Date of Electronic Publication: 2024 Jul 31 (Print Publication: 2024).
DOI:	10.1016/j.jacadv.2024.101072
Abstrakt:	Clinical decision-making regarding syncope poses challenges, with risk of physician error due to the elusive nature of syncope pathophysiology, diverse presentations, heterogeneity of risk factors, and limited therapeutic options. Artificial intelligence (AI)-based techniques, including machine learning (ML), deep learning (DL), and natural language processing (NLP), can uncover hidden and nonlinear connections among syncope risk factors, disease features, and clinical outcomes. ML, DL, and NLP models can analyze vast amounts of data effectively and assist physicians to help distinguish true syncope from other types of transient loss of consciousness. Additionally, short-term adverse events and length of hospital stay can be predicted by these models. In syncope research, AI-based models shift the focus from causality to correlation analysis between entities. This prompts the search for patterns rather than defining a hypothesis to be tested a priori. Furthermore, education of students, doctors, and health care providers engaged in continuing medical education may benefit from clinical cases of syncope interacting with NLP-based virtual patient simulators. Education may be of benefit to patients. This article explores potential strengths, weaknesses, and proposed solutions associated with utilization of ML and DL in syncope diagnosis and management. Three main topics regarding syncope are addressed: 1) clinical decision-making; 2) clinical research; and 3) education. Within each domain, we question whether "AI will be better than humans," seeking evidence to support our objective inquiry. Competing Interests: The authors have reported that they have no relationships relevant to the contents of this paper to disclose.
Databáze:	MEDLINE
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