Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter

Abstract

Objective: Although the role of artificial intelligence (AI) in medicine is increasingly studied, most patients do not benefit because the majority of AI models remain in the testing and prototyping environment. The development and implementation trajectory of clinical AI models are complex and a structured overview is missing. We therefore propose a step-by-step overview to enhance clinicians' understanding and to promote quality of medical AI research.

Methods: We summarised key elements (such as current guidelines, challenges, regulatory documents and good practices) that are needed to develop and safely implement AI in medicine.

Conclusion: This overview complements other frameworks in a way that it is accessible to stakeholders without prior AI knowledge and as such provides a step-by-step approach incorporating all the key elements and current guidelines that are essential for implementation, and can thereby help to move AI from bytes to bedside.

Keywords: artificial intelligence; data science; machine learning.

Figure 1

Global evolution of research in artificial intelligence in medicine. The number of AI papers in humans on PubMed.com was arranged by year, 2011–2020. The blue bars represent the number of studies. The following search was performed: (“artificial intelligence”[MeSH Terms] OR (“artificial”[All Fields) and “intelligence”[All Fields]) OR “artificial intelligence”[All Fields]) OR (“machine learning”[MeSH Terms] OR (“machine”[All Fields] AND “learning”[All Fields]) OR “machine learning”[All Fields]) OR (“deep learning”[MeSH Terms] OR (“deep”[All Fields] AND “learning”[All Fields]) OR “deep learning”[All Fields]).

Figure 2

Structured overview of the clinical AI development and implementation trajectory. Crucial steps within the five phases are presented along with stakeholder groups at the bottom that need to be engaged: knowledge experts (eg, clinical experts, data scientists and information technology experts), decision-makers (eg, hospital board members) and users (eg, physicians, nurses and patients). Each of the steps should be successfully addressed before proceeding to the next phase. The colour gradient from light blue to dark blue indicates AI model maturity, from concept to clinical implementation. The development of clinical AI models is an iterative process that may need to be (partially) repeated before successful implementation is achieved. Therefore, a model could be adjusted or retrained (ie, return to phase I) at several moments during the process (eg, after external validation or after implementation). AI, artificial intelligence.