Ethical and social considerations of applying artificial intelligence in healthcare-a two-pronged scoping review

Abstract

Background: Artificial Intelligence (AI) is being designed, tested, and in many cases actively employed in almost every aspect of healthcare from primary care to public health. It is by now well established that any application of AI carries an attendant responsibility to consider the ethical and societal aspects of its development, deployment and impact. However, in the rapidly developing field of AI, developments such as machine learning, neural networks, generative AI, and large language models have the potential to raise new and distinct ethical and social issues compared to, for example, automated data processing or more 'basic' algorithms.

Methods: This article presents a scoping review of the ethical and social issues pertaining to AI in healthcare, with a novel two-pronged design. One strand of the review (SR1) consists of a broad review of the academic literature restricted to a recent timeframe (2021-23), to better capture up to date developments and debates. The second strand (SR2) consists of a narrow review, limited to prior systematic and scoping reviews on the ethics of AI in healthcare, but extended over a longer timeframe (2014-2024) to capture longstanding and recurring themes and issues in the debate. This strategy provides a practical way to deal with an increasingly voluminous literature on the ethics of AI in healthcare in a way that accounts for both the depth and evolution of the literature.

Results: SR1 captures the heterogeneity of audience, medical fields, and ethical and societal themes (and their tradeoffs) raised by AI systems. SR2 provides a comprehensive picture of the way scoping reviews on ethical and societal issues in AI in healthcare have been conceptualized, as well as the trends and gaps identified.

Conclusion: Our analysis shows that the typical approach to ethical issues in AI, which is based on the appeal to general principles, becomes increasingly unlikely to do justice to the nuances and specificities of the ethical and societal issues raised by AI in healthcare, as the technology moves from abstract debate and discussion to real world situated applications and concerns in healthcare settings.

Keywords: Artificial intelligence; Healthcare; Medicine; Scoping review.

Fig. 1

PRISMA 2020 flow diagram for SR1

Fig. 2

PRISMA 2020 flow diagram for SR2

Fig. 3

Number of articles returned by year for SR1. Please note that the literature considered for 2023 encompasses articles published until August 2023, and this may explain the quantitative discrepancy with respect to 2022 and 2021

Fig. 4

Included timeframe and number of articles identified in reviews found by SR2. Ref. – Reference of the review article, No. of articles included – Number of articles included on the reviews, Blue line – the review had a general scope in terms of field of medicine, Orange line – the review had a specific field of medicine as a scope, Arrow – the review had no minimum date set in their search sting

Fig. 5

Proportion of methods reported by empirical studies in SR1

Fig. 6

Map showing frequency of countries included in empirical studies

Fig. 7

Groups or populations identified as subjects of empirical research in SR1

Fig. 8

Proportion of articles directed to particular audiences in SR1

Fig. 9

Ethical themes in SR1

Fig. 10

Ethical themes identified in SR2