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SHIFTing artificial intelligence to be responsible in healthcare: A systematic review

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  • Siala, Haytham
  • Wang, Yichuan

Abstract

A variety of ethical concerns about artificial intelligence (AI) implementation in healthcare have emerged as AI becomes increasingly applicable and technologically advanced. The last decade has witnessed significant endeavors in striking a balance between ethical considerations and health transformation led by AI. Despite a growing interest in AI ethics, implementing AI-related technologies and initiatives responsibly in healthcare settings remains a challenge. In response to this topical challenge, we reviewed 253 articles pertaining to AI ethics in healthcare published between 2000 and 2020, summarizing the coherent themes of responsible AI initiatives. A preferred reporting items for systematic review and meta-analysis (PRISMA) approach was employed to screen and select articles, and a hermeneutic approach was adopted to conduct systematic literature review. By synthesizing relevant knowledge from AI governance and ethics, we propose a responsible AI initiative framework that encompasses five core themes for AI solution developers, healthcare professionals, and policy makers. These themes are summarized in the acronym SHIFT: Sustainability, Human centeredness, Inclusiveness, Fairness, and Transparency. In addition, we unravel the key issues and challenges concerning responsible AI use in healthcare, and outline avenues for future research.

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  • Siala, Haytham & Wang, Yichuan, 2022. "SHIFTing artificial intelligence to be responsible in healthcare: A systematic review," Social Science & Medicine, Elsevier, vol. 296(C).
    • Handle: RePEc:eee:socmed:v:296:y:2022:i:c:s0277953622000855
    DOI: 10.1016/j.socscimed.2022.114782
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    2. Roppelt, Julia Stefanie & Kanbach, Dominik K. & Kraus, Sascha, 2024. "Artificial intelligence in healthcare institutions: A systematic literature review on influencing factors," Technology in Society, Elsevier, vol. 76(C).
    3. Satish Kumar & Weng Marc Lim & Uthayasankar Sivarajah & Jaspreet Kaur, 2023. "Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis," Information Systems Frontiers, Springer, vol. 25(2), pages 871-896, April.
    4. Eliseo Sciarretta & Riccardo Mancini & Emilio Greco, 2022. "Artificial Intelligence for Healthcare and Social Services: Optimizing Resources and Promoting Sustainability," Sustainability, MDPI, vol. 14(24), pages 1-9, December.
    5. Wang, Weisha & Wang, Yichuan & Chen, Long & Ma, Rui & Zhang, Minhao, 2024. "Justice at the Forefront: Cultivating felt accountability towards Artificial Intelligence among healthcare professionals," Social Science & Medicine, Elsevier, vol. 347(C).
    6. Kusta, Olsi & Bearman, Margaret & Gorur, Radhika & Risør, Torsten & Brodersen, John Brandt & Hoeyer, Klaus, 2024. "Speed, accuracy, and efficiency: The promises and practices of digitization in pathology," Social Science & Medicine, Elsevier, vol. 345(C).
    7. Cresswell, Kathrin & Rigby, Michael & Magrabi, Farah & Scott, Philip & Brender, Jytte & Craven, Catherine K. & Wong, Zoie Shui-Yee & Kukhareva, Polina & Ammenwerth, Elske & Georgiou, Andrew & Medlock,, 2023. "The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision," Health Policy, Elsevier, vol. 136(C).
    8. Singh, Nidhi & Jain, Monika & Kamal, Muhammad Mustafa & Bodhi, Rahul & Gupta, Bhumika, 2024. "Technological paradoxes and artificial intelligence implementation in healthcare. An application of paradox theory," Technological Forecasting and Social Change, Elsevier, vol. 198(C).

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