Assessing the need for coronary angiography in high-risk non-ST-elevation acute coronary syndrome patients using artificial intelligence and computed tomography
Preliminary evidence suggests that ChatGPT can effectively assist in making ICA decisions for high-risk NSTE-ACS patients, potentially reducing unnecessary procedures. However, the study underscores the importance of data accuracy and calls for larger, more diverse investigations to refine artificial intelligence's role in clinical decision-making.
Authors
- Ioannis Skalidis (ORCID: https://orcid.org/0000-0002-4374-0389)
- Adil Salihu (ORCID: https://orcid.org/0000-0002-9659-0396)
- Salah D. Qanadli (ORCID: https://orcid.org/0000-0003-1330-660X)
- David C. Rotzinger (ORCID: https://orcid.org/0000-0002-6321-3180)
- Aurelien Cagnina
- Wongsakorn Luangphiphat (ORCID: https://orcid.org/0000-0001-5691-0204)
- Stéphane Fournier (ORCID: https://orcid.org/0000-0002-9422-9521)
- Aurelia Zimmerli
- Emmanuel Abbé (ORCID: https://orcid.org/0000-0002-1831-656X)
- David Meier (ORCID: https://orcid.org/0000-0002-5524-5844)
- Olivier Müller (ORCID: https://orcid.org/0000-0003-2441-5799)
- Benjamin Faltin
Institutions
- Centre Hospitalier Universitaire Vaudois (CH)
- École Polytechnique Fédérale de Lausanne (CH)
- University of Lausanne (CH)
Publication Details
- Journal
- The International Journal of Cardiovascular Imaging
- Published
- 2024-11-08
- DOI
- https://doi.org/10.1007/s10554-024-03283-9
- Citations
- 3
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 1.21
Funders
- Université de Lausanne