Contrasting attitudes towards current and future artificial intelligence applications for computerised interpretation of electrocardiograms: a clinical stakeholder interview study

Abstract

Objectives: To investigate clinicians' attitudes towards current automated interpretation of ECG and novel AI technologies and their perception of computer-assisted interpretation.

Materials and methods: We conducted a series of interviews with clinicians in the UK. Our study: (1) explores the potential for AI, specifically future "human-like" computing approaches, to facilitate ECG interpretation and support clinical decision making, and (2) elicits their opinions about the importance of explainability and trustworthiness of AI algorithms.

Results: We performed inductive thematic analysis on interview transcriptions from 23 clinicians and identified the following themes: (1) a lack of trust in current systems, (2) positive attitudes towards future AI applications and requirements for these, (3) the relationship between the accuracy and explainability of algorithms, and (4) opinions on education, possible deskilling, and the impact of AI on clinical competencies.

Discussion: Clinicians do not trust current computerised methods, but welcome future "AI" technologies. Where clinicians trust future AI interpretation to be accurate, they are less concerned that it is explainable. They also preferred ECG interpretation that demonstrated the results of the algorithm visually. Whilst clinicians do not fear job losses, they are concerned about deskilling and the need to educate the workforce to use AI responsibly.

Conclusion: Clinicians are positive about the future application of AI in clinical decision-making. Accuracy is a key factor of uptake and visualisations are preferred over current computerised methods. This is viewed as a potential means of training and upskilling, in contrast to the deskilling that automation might be perceived to bring.

Keywords: artificial intelligence; computerised interpretation; electrocardiogram; qualitative research.

Figure 1.

Example of a visualisation using our explainable algorithm shown to participants during interviews. Cooler colours indicate a normal QT interval (upper ECG) whilst warmer colours indicate a prolonged QT interval (lower ECG).