Abstract
Background: Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years' significant academic AI advancements and the comparably low level of practical application in healthcare.Methods: A literature and real life cases analysis was conducted in Scopus and OpacPlus as well as the Google advanced search database. The according search queries have been defined based on success factor categories for AI implementation derived from a prior World Health Organization survey about barriers of adoption of Big Data within 125 countries. The eligible publications and real life cases were identified through a catalog of in- and exclusion criteria focused on concrete AI application cases. These were then analyzed to deduct and discuss success factors that facilitate or inhibit a broad-scale implementation of AI in healthcare.Results: The analysis revealed three categories of success factors, namely (1) policy setting, (2) technological implementation, and (3) medical and economic impact measurement. For each of them a set of recommendations has been deducted: First, a risk adjusted policy frame is required that distinguishes between precautionary and permissionless principles, and differentiates among accountability, liability, and culpability. Second, a “privacy by design” centered technology infrastructure shall be applied that enables practical and legally compliant data access. Third, the medical and economic impact need to be quantified, e.g., through the measurement of quality-adjusted life years while applying the CHEERS and PRISMA reporting criteria.Conclusions: Private and public institutions can already today leverage AI implementation based on the identified results and thus drive the translation from scientific development to real world application. Additional success factors could include trust-building measures, data categorization guidelines, and risk level assessments and as the success factors are interlinked, future research should elaborate on their optimal interaction to utilize the full potential of AI in real world application.
Highlights
Artificial Intelligence (AI) is having the potential for a significant impact on the entire healthcare industry
Given the need to access large amounts of data under strict privacy regulations and the dependence on managerial acceptance, it became evident that AI implementation needs to be tailored further to fit into existing healthcare routines
The key identified barriers for AI implementation relate to the following fundamental issues: [1] non-privacy focused technological implementation, [2] shortcomings in current policy settings, and [3] the lack of medical and economic impact measurements
Summary
Artificial Intelligence (AI) is having the potential for a significant impact on the entire healthcare industry. Based on the growing amount of digital health applications, the high expectations related to medical, social, and economic improvements, as well as the need for digital health routines triggered by COVID-19, the success factors for AI implementation need to be defined now. Artificial Intelligence (AI) in healthcare has demonstrated high efficiency in academic research, while only few, and predominantly small, real-world AI applications exist in the preventive, diagnostic and therapeutic contexts. Our identification and analysis of success factors for the implementation of AI aims to close the gap between recent years’ significant academic AI advancements and the comparably low level of practical application in healthcare
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