Towards explainable artificial intelligence and explanation user interfaces to open the ‘black box’ of automated ECG interpretation

This an exploratory paper that discusses the use of artificial intelligence (AI) in ECG interpretation and opportunities for improving the explaina-bility of the AI (XAI) when reading 12-lead ECGs. To develop AI systems, many principles (human rights, well-being, data agency, effectiveness, transparency, accountability, awareness of misuse and competence) must be considered to ensure that the AI is trustworthy and applicable. The current computerised ECG interpretation algorithms can detect different types of heart diseases. However, there are some challenges and shortcomings that need to be addressed, such as the explainability issue and the interaction between the human and the AI for clinical decision making. These challeng-es create opportunities to develop a trustworthy XAI for automated ECG interpretation with a high performance and a high confidence level. This study reports a proposed XAI interface design in automatic ECG interpreta-tion based on suggestions from previous studies and based on standard guidelines that were developed by the human computer interaction (HCI) community. New XAI interfaces should be developed in the future that fa-cilitate more transparency of the decision logic of the algorithm which may allow users to calibrate their trust and use of the AI system.