JMIR AI
Acceptance of Machine Learning for Medication Selection in Epilepsy to Inform Clinical Trial Design: Co-Design Survey Study
By Smith MJ, Reeder S, Waugh F and 6 others.
Abstract
Background
Antiseizure medications (ASMs) are the mainstay of epilepsy treatment; however, there is currently no reliable way to predict which medication will be most effective for an individual patient. Machine learning (ML) approaches are increasingly being explored to support personalized ASM selection, but successful implementation will depend on acceptance by both people with epilepsy and treating neurologists. Understanding factors influencing acceptability of ML in clinical decision-making is therefore critical to support engagement, trust, and adherence. The extended unified theory of acceptance and use of technology (UTAUT2) framework has previously been applied to evaluate acceptance of health care technologies, but its suitability for ML-supported ASM selection has not been established.
Objective
The objective of this study was to co-design a UTAUT2-based questionnaire to measure ML technology acceptability that is suitable for use in a clinical trial.