Will artificial intelligence be “better” than humans in the management of syncope?
File(s)
Author(s)
Type
Journal Article
Abstract
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.
Date Issued
2024-09-01
Date Acceptance
2024-04-29
Citation
JACC: Advances, 2024, 3 (9 Part 2)
ISSN
2772-963X
Publisher
Elsevier
Start Page
101072
End Page
101072
Journal / Book Title
JACC: Advances
Volume
3
Issue
9 Part 2
Copyright Statement
Published by Elsevier on Behalf of The American College of Cardiology Foundation. This is an Open Access Article under the CC BY License (http://creativecommons.org/licenses/by/4.0/).
License URL
Identifier
https://www.ncbi.nlm.nih.gov/pubmed/39372450
PII: S2772-963X(24)00266-7
Subjects
artificial intelligence
clinical decision
education
research
syncope
Publication Status
Published
Coverage Spatial
United States
Article Number
101072
Date Publish Online
2024-07-31