Table 2.

Barriers to Artificial Intelligence Implementation in Congenital Heart Disease and Proposed Strategies to Overcome Them

1. Insufficient data access, storage, and sharing strategies for CHD patient data. Data limitations are due to lack of accurately labelled data. Methods such as transfer learning, self-supervised learning, and predictive learning to increase these data may help overcome these barriers to increase opportunities for external validation

2. Lack of AI in medicine awareness from stake holders in health care (ie, clinicians, patients, and hospital administrators). Clinicians need more education about data and AI, and patients need more education to understand the need for and benefits of collaboration on real-world data and not just registries and randomized control trials. Developing institutional educational series and profession society webinars (American College of Cardiology Innovation and Adult Congenital and Pediatric Cardiology sections) may help address these challenges.

3. Absence of forums to facilitate communication between clinicians and data scientists. Providing computer and data scientists with more knowledge regarding the proposed deficits in health care to target the development of meaningful AI solutions. Increasing clinician-to-data scientist synergy for mutual understanding of the dual perspectives of both domains.

4. Difficulty harnessing collaboration. Recruitment of multidisciplinary team members, particularly AI champions, to drive AI implementation.

5. Current CHD research is unidimensional. Leveraging multimodal AI for cardiology to incorporate the full spectrum of data: genomics, imaging, demographic, ICU, wearable, and so on to accelerate precision medicine.

6. Concern that AI methods are not transparent enough for the medical community. Utilizing explainable AI to minimize the “black box” perception of AI and requiring studies provide documentation that they completed the recommended Minimum Information About Clinical Artificial Intelligence Modeling Checklist.

7. Critique that AI projects are not created in the context of clinical applicability. Utilizing design thinking to select proper AI methodology relevant to the clinical context.

8. Poor acceptance of AI in the research community and concern that AI requires too much time to establish sufficiently large data sets. Using innovative AI methods to leverage the power of small data sets. Executing more realistic projects that are easier to accomplish, with demonstrable value and return on investment (ROI) may help get “buy-in” from the administrative and clinical leadership.

AI = artificial intelligence; CHD = congenital heart disease; ICU = intensive care unit.