Explaining the Slow Adoption of AI Innovations in Health Care: Network Analysis Approach

Artificial intelligence (AI) is a topic of considerable hype, with many actors sensing its high potential for health care applications. Despite this, the adoption has been slow, with few applications being implemented in clinical practice.

The aim of our study was to investigate the challenges associated with using AI in health care, as well as provide suggestions for how further adoption of AI within health care organizations can be facilitated.

A qualitative case study with a mixed methods approach was conducted at one of Sweden's largest hospitals. Regulatory approved AI medical devices were analyzed, and primary qualitative data from 14 expert interviews were collected and cross-referenced with secondary quantitative data. The framework of technological innovation systems was used to analyze the system factors and their dynamics to identify blocking mechanisms and areas for improvement.

The challenges related to knowledge development, diffusion, legitimation, and resource mobilization could trigger a cascade of positive activities, thereby significantly enhancing the overall performance of the innovation system. Creating dedicated testing environments to evaluate safety and efficacy would facilitate the routine clinical use and reinforce the use of AI innovations in health care organizations.

This analysis shows that the adoption of AI health care technology innovations can be accelerated through targeted strategies and supportive mechanisms triggering virtuous cycles that facilitate clinical validation and generate compelling use cases. The interconnection between guidance of search and entrepreneurial experimentation has been confirmed, providing the initial conditions for knowledge development, diffusion, and legitimation in the early stages of emerging technologies.