We present barriers of AI adoption in healthcare on macro to micro level and respective actions to overcome these challenges for each stakeholder group. The findings are verified with results from literature. We used two qualitative methods:(1) a systematic literature review and (2) expert interviews with seven AI experts and nine physicians. We applied a deductive coding scheme. The barriers can be classified in social, ethical, political, economic, technological, educational and organizational barriers. The findings provide that the most hindering barriers are of technological, political and organizational nature. Social and economic barriers are less difficult to overcome, in particular when the benefits of AI application become apparent in practice. From our results, we infer the following four actions: enlightment, regulation, incentives and collaboration. We linked all derived actions with the identified barriers and stakeholders. Thus, we provide a guidance to overcome the adoption barriers of AI in healthcare.
Arlinghaus, Tim; Kus, Kevin; Behne, Alina; and Teuteberg, Frank, "How to Overcome the Barriers of AI Adoption in Healthcare: A Multi-Stakeholder Analysis" (2022). PACIS 2022 Proceedings. 4.
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