Interventional cardiologists' perspectives and knowledge towards artificial intelligence.

artificial intelligence interventional cardiology perspectives

Journal

The Journal of invasive cardiology
ISSN: 1557-2501
Titre abrégé: J Invasive Cardiol
Pays: United States
ID NLM: 8917477

Informations de publication

Date de publication:
08 Apr 2024
Historique:
medline: 10 4 2024
pubmed: 10 4 2024
entrez: 10 4 2024
Statut: aheadofprint

Résumé

Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field. We conducted a global, web-based, anonymous survey of IC fellows and attendings to assess the knowledge and perceptions of interventional cardiologists regarding AI use in IC. A total of 521 interventional cardiologists participated in the survey. The median age range of participants was 36 to 45 years, most (51.5%) practice in the United States, and 7.5% were women. Most (84.7%) could explain well or somehow knew what AI is about, and 63.7% were optimistic/very optimistic about AI in IC. However, 73.5% believed that physicians know too little about AI to use it on patients and most (46.1%) agreed that training will be necessary. Only 22.1% were currently implementing AI in their personal clinical practice, while 60.6% estimated implementation of AI in their practice the next 5 years. Most agreed that AI will increase diagnostic efficiency, diagnostic accuracy, treatment selection, and healthcare expenditure, and decrease medical errors. The most tried AI-powered tools were image analysis (57.3%), ECG analysis (61.7%), and AI-powered algorithms (45.9%). Interventional cardiologists practicing in academic hospitals were more likely to have AI tools currently implemented in their clinical practice and to use them, women had a higher likelihood of expressing concerns regarding AI, and younger interventional cardiologists were more optimistic about AI integration in IC. Our survey suggests a positive attitude of interventional cardiologists regarding AI implementation in the field of IC.

Sections du résumé

BACKGROUND BACKGROUND
Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field.
METHODS METHODS
We conducted a global, web-based, anonymous survey of IC fellows and attendings to assess the knowledge and perceptions of interventional cardiologists regarding AI use in IC.
RESULTS RESULTS
A total of 521 interventional cardiologists participated in the survey. The median age range of participants was 36 to 45 years, most (51.5%) practice in the United States, and 7.5% were women. Most (84.7%) could explain well or somehow knew what AI is about, and 63.7% were optimistic/very optimistic about AI in IC. However, 73.5% believed that physicians know too little about AI to use it on patients and most (46.1%) agreed that training will be necessary. Only 22.1% were currently implementing AI in their personal clinical practice, while 60.6% estimated implementation of AI in their practice the next 5 years. Most agreed that AI will increase diagnostic efficiency, diagnostic accuracy, treatment selection, and healthcare expenditure, and decrease medical errors. The most tried AI-powered tools were image analysis (57.3%), ECG analysis (61.7%), and AI-powered algorithms (45.9%). Interventional cardiologists practicing in academic hospitals were more likely to have AI tools currently implemented in their clinical practice and to use them, women had a higher likelihood of expressing concerns regarding AI, and younger interventional cardiologists were more optimistic about AI integration in IC.
CONCLUSIONS CONCLUSIONS
Our survey suggests a positive attitude of interventional cardiologists regarding AI implementation in the field of IC.

Identifiants

pubmed: 38598250
doi: 10.25270/jic/24.00052
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Auteurs

Michaella Alexandrou (M)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Athanasios Rempakos (A)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Deniz Mutlu (D)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Ahmed Al Ogaili (A)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Bavana V Rangan (BV)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Olga C Mastrodemos (OC)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Konstantinos Voudris (K)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Anastasios Milkas (A)

Athens Naval Hospital, Athens, Greece.

M Nicholas Burke (MN)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Yader Sandoval (Y)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA.

Yiannis S Chatzizisis (YS)

Center for Digital Cardiovascular Innovations, Division of Cardiovascular Medicine, University of Miami Miller School of Medicine, Miami, Florida, USA.

Konstantinos C Siontis (KC)

Department of Cardiovascular Medicine, Mayo Clinic, Rochester, Minnesota, USA.

Emmanouil S Brilakis (ES)

Minneapolis Heart Institute and Minneapolis Heart Institute Foundation, Abbott Northwestern Hospital, Minneapolis, Minnesota, USA. Email: esbrilakis@gmail.com.

Classifications MeSH