Decentralised machine learning in healthcare and life sciences: Applying the technology acceptance model

• DMLfaces low acceptance levels among platform providers. • DML has great potential for beneficial use in healthcare and life sciences. • Conceptualised model established based on TAM and UTAUT. • Sizeable opportunities exist to grow user acceptance of collaborative DML projects. • Results offer insights on a better network for decentralised collaborative training. Machine learning (ML) has significant potential for the healthcare sector. To implement novel technologies such as decentralised machine learning (DML), platform providers must overcome low acceptance levels and implementation hurdles. We used a conceptualised model for DML for healthcare and life sciences, based on the technology acceptance model and the unified theory of the acceptance and use of technology, to determine what drives DML acceptance among users. Data were generated using a quantitative approach and analysed using partial least squares structural equation modelling, following a structured analysis approach. The results show that the DML platform providers have a great opportunity to increase user acceptance for collaborative projects when addressing institutional issues. The analysis of the model reveals a well-conceptualised, adjusted acceptance model for DML. This study provides valuable initial insights for DML platform providers on how to increase acceptance among healthcare users, improve usage, and create a better network for the decentralised collaborative training of an ML model. It contributes to the literature on acceptance research based on TAMs, especially in healthcare and life sciences, and adds a new dimension by considering DML as a technology-based service and not just an application.