Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications

By Daniel Schönberger. 2019

193 citations recorded by the source corpus.

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In Health Informatics

Citations in the corpus

Ethical Implementation of Artificial Intelligence to Select Embryos in In Vitro Fertilization (2021, background): “might evaluate the doctor’s decision-making, and subsequently, it would be unclear how responsibility for injury may be adjudicated [55, 56].”
The ethics of AI in health care: A mapping review. (2020, methodology): “…as the algorithm in this scenario would be considered a diagnostic support tool—just like a blood test—with no decision making capacity, so it is the HCP's responsibility to act appropriately based on the information provided (Price et al., 2019; Schönberger, 2019; Sullivan and Schweikart, 2019).”
Artificial Intelligence and Patient-Centered Decision-Making (2020, background): “4 For some recent discussions of some of the broader ethical and legal issues surrounding the use of AI in healthcare, see Ploug and Holm (2019) and Schönberger (2019).”
A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare (2023, background): “Contrary to this issue however, if a professional fails to use or abide by the advice of an AI tool and there is a poor outcome, it is conceivable that this may be considered clinical negligence [68].”
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians (2019, background): “It is often assumed that algorithmic decision-making might lead to fairer and more robust outcomes than human judgment [37].”
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective (2020, background): “In addition, the law and its interpretation and implementation have to constantly adapt to the evolving state-of-the-art in technology [13].”
Drawbacks of Artificial Intelligence and Their Potential Solutions in the Healthcare Sector (2023, background): “Since the algorithm training involves access to large datasets that preferably characterize different population groups, appre-hensions about approval and efficacious de-identification and anonymization of data remain critical [28].”