Qualitative Evaluation of an Artificial Intelligence-Based Clinical Decision Support System to Guide Rhythm Management of Atrial Fibrillation: Survey Study.
John StacyRachel S KimChristopher D BarrettBalaviknesh SekarSteven T SimonFarnoush Banaei-KashaniMichael Aaron RosenbergPublished in: JMIR formative research (2022)
Safety of ML applications was the highest priority of the providers surveyed, and trust of such models remains varied. Widespread clinical acceptance of ML in health care is dependent on how much providers trust the algorithms. Building this trust involves ensuring transparency and interpretability of the model.
Keyphrases
- artificial intelligence
- clinical decision support
- machine learning
- atrial fibrillation
- deep learning
- health information
- healthcare
- big data
- electronic health record
- heart failure
- catheter ablation
- left atrial
- cross sectional
- social media
- oral anticoagulants
- heart rate
- direct oral anticoagulants
- percutaneous coronary intervention
- blood pressure