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HRS scientific statement on artificial intelligence integration framework into clinical electrophysiology workflows.
Antonis A Armoundas1, Jennifer N Avari Silva2, Tina Baykaner3
1Cardiovascular Research Center, Massachusetts General Hospital, Boston, Massachusetts; Broad Institute, Massachusetts Institute of Technology, Cambridge, Massachusetts.
Artificial intelligence (AI) and digital health technologies (DHTs) offer new healthcare possibilities but require careful integration into clinical workflows. This statement provides guidance for safe AI/DHT adoption, monitoring, and ethical considerations in electrophysiology.
Area of Science:
- Healthcare Technology
- Clinical Informatics
- Biomedical Engineering
Background:
- Artificial intelligence (AI) and digital health technologies (DHTs) are revolutionizing healthcare delivery, presenting significant opportunities for improving diagnostics, treatment, and patient management.
- The rapid advancement of AI/DHT necessitates clear strategies for their integration into clinical practice, posing challenges for regulatory bodies, administrators, and clinicians.
- Understanding the safe integration points, available guidance, and monitoring strategies for AI/DHT is crucial for effective clinical adoption.
Purpose of the Study:
- To identify existing and potential AI/DHT applications in clinical electrophysiology.
- To outline resources and frameworks that guide the adoption of AI/DHT in healthcare.
- To propose strategies for continuous monitoring, recalibration, retraining, and cessation of AI/DHT use.
Main Methods:
- Review of current AI/DHT applications in clinical electrophysiology.
- Identification of existing regulatory and implementation frameworks.
- Emphasis on data interoperability, ethical considerations, and human rights principles.
Main Results:
- AI/DHT applications are expanding within electrophysiology, offering enhanced patient care capabilities.
- Frameworks and resources are available to guide the responsible adoption and management of AI/DHT.
- Data interoperability, ethical considerations (data protection, bias, transparency, accountability), and human rights are critical for successful implementation.
Conclusions:
- Responsible implementation and vigilant post-deployment evaluation are essential for harnessing AI/DHT's potential in healthcare.
- Addressing ethical imperatives and ensuring data interoperability are key to equitable and personalized patient care.
- Continuous monitoring and adaptation strategies are necessary for the safe and effective use of AI/DHT in clinical settings.
