Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

doi:10.3389/fsurg.2022.862322

Paper

Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

Published Mar 14, 2022 · Nithesh Naik, B. Hameed, Dasharathraj K. Shetty

Frontiers in Surgery

Q2 SJR score

296

Citations

6

Influential Citations

Open Access PDF Full text Semantic Scholar

Abstract

The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.

Highly Cited

Study Snapshot

AI in healthcare faces legal and ethical challenges, requiring algorithmic transparency, privacy, and cybersecurity to protect patients and ensure accurate procedures and protocols.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

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References

Identifying Ethical Considerations for Machine Learning Healthcare Applications

This paper presents a systematic approach for identifying ethical concerns in machine learning healthcare applications, aiding in ethical evaluations and interdisciplinary collaboration among stakeholders.

Legal and human rights issues of AI: Gaps, challenges and vulnerabilities

AI faces legal and human rights challenges, particularly affecting vulnerable individuals and groups, requiring constant evaluation and agility in approach to protect human well-being.

Clinical AI: opacity, accountability, responsibility and liability

Opacity in AI systems in clinical decision making raises concerns about accountability and responsibility, with unclear allocation of responsibility in case of patient harm.

The ethics of AI in health care: A mapping review.

AI in healthcare faces epistemic, normative, and traceability ethical issues, requiring careful consideration by policymakers, regulators, and developers to maximize benefits while avoiding potential harms.

There Is No Techno-Responsibility Gap

Moral responsibility is a dynamic and flexible process that can effectively encompass emerging technological entities, challenging the belief in a technology-based responsibility gap.

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