Abstract
This an exploratory paper that discusses the use of artificial intelligence (AI) in ECG interpretation and opportunities for improving the explainability 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 challenges 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 interpretation 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 facilitate more transparency of the decision logic of the algorithm which may allow users to calibrate their trust and use of the AI system.
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Acknowledgment
This work is supported by the European Union’s INTERREG VA programme, managed by the Special EU Programmes Body (SEUPB). The work is associated with the project – ‘Centre for Personalised Medicine – Clinical Decision Making and Patient Safety’. The views and opinions expressed in this study do not necessarily reflect those of the European Commission or the Special EU Programmes Body (SEUPB).
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Rjoob, K. et al. (2021). Towards Explainable Artificial Intelligence and Explanation User Interfaces to Open the ‘Black Box’ of Automated ECG Interpretation. In: Reis, T., Bornschlegl, M.X., Angelini, M., Hemmje, M.L. (eds) Advanced Visual Interfaces. Supporting Artificial Intelligence and Big Data Applications. AVI-BDA ITAVIS 2020 2020. Lecture Notes in Computer Science(), vol 12585. Springer, Cham. https://doi.org/10.1007/978-3-030-68007-7_6
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