Artificial Intelligence and the Implementation Challenge

By James Shaw, Frank Rudzicz, Trevor Jamieson, Avi Goldfarb. Journal of Medical Internet Research, 2019

320 citations recorded by the source corpus.

Background Applications of artificial intelligence (AI) in health care have garnered much attention in recent years, but the implementation issues posed by AI have not been substantially addressed. Objective In this paper, we have focused on machine learning (ML) as a form of AI and have provided a framework for thinking about use cases of ML in health care. We have structured our discussion of challenges in the implementation of ML in comparison with other technologies using the framework of Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies (NASSS). Methods After providing an overview of AI technology, we describe use cases of ML as falling into the categories of decision support and automation. We suggest these use cases apply to clinical, operational, and epidemiological tasks and that the primary function of ML in health care in the near term will be decision support. We then outline unique implementation issues posed by ML initiatives in the categories addressed by the NASSS framework, specifically including meaningful decision support, explainability, privacy, consent, algorithmic bias, security, scalability, the role of corporations, and the changing nature of health care work. Results Ultimately, we suggest that the future of ML in health care remains positive but uncertain, as support from patients, the public, and a wide range of health care stakeholders is necessary to enable its meaningful implementation. Conclusions If the implementation science community is to facilitate the adoption of ML in ways that stand to generate widespread benefits, the issues raised in this paper will require substantial attention in the coming years.

Subfields: Artificial Intelligence, Health Informatics

View Paper: https://www.semanticscholar.org/paper/b944b71f789393902e161bf5946b71036caa4863

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Hock Hai Teo
2. Maryam Ghasemaghaei
3. Nima Kordzadeh
4. Jiamin Yin
5. Kee Yuan Ngiam

In Health Informatics

1. Hock Hai Teo
2. Jiamin Yin
3. Kee Yuan Ngiam
4. Emilio Ferrara

Citations in the corpus

• Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review (2020, background): “Examples of key challenges that need to be addressed include data sharing and privacy issues, lack of algorithm transparency, the changing nature of health care work, financial concerns, and the demanding regulatory environment [1,3,9-13].”
• Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden (2022, background): “AI is thus not one single type of technology but rather many different types within various application areas, e.g., diagnosis and treatment, patient engagement and adherence, and administrative activities [5, 6].”
• Algorithmic bias: review, synthesis, and future research directions (2021, background): “…explains the sources, consequences, and mitigation strategies in a range of domains from employment and people analytics (Gal et al., 2017) and healthcare (Shaw et al., 2019) to education (Williams et al., 2018), credit markets (Fuster et al., 2018), and criminal justice (Mulligan et al., 2019).”