Table 2.

Barriers and solutions to clinical implementation of AI-enabled decision support

Challenges	Solutions
Lack of data standardization impairs multi-centre validation and dissemination	Use common data models or federated learning to maintain data security while sharing models
Patients and clinicians mistrust “black-box” models and fear the possibility of egregious errors	Implement model interpretability, explainability and uncertainty estimation mechanisms
Models applied outside of their training environment and patient population could cause harm	Perform technology readiness assessments, perform model stress testing with simulated data
Manual data entry requirements and additional work drive clinician apathy	Integrate automated decision-support systems with existing clinical and digital workflows
AI models cannot incorporate some subjective finding and the wisdom of experience	Preserve human intuition in decision-making processes that are augmented by recommendations from AI models
Accountability for errors associated with AI-enabled decision support remains challenging	Guide the development and implementation of AI models toward social benefit with altruism, creativity and clinical expertise

AI, artificial intelligence.