Ethical Conundrums in the Application of Artificial Intelligence (AI) in Healthcare—A Scoping Review of Reviews
Open accessAbstract
Background: With the availability of extensive health data, artificial intelligence has an inordinate capability to expedite medical explorations and revamp healthcare.Artificial intelligence is set to reform the practice of medicine soon. Despite the mammoth advantages of artificial intelligence in the medical field, there exists inconsistency in the ethical and legal framework for the application of AI in healthcare. Although research has been conducted by various medical disciplines investigating the ethical implications of artificial intelligence in the healthcare setting, the literature lacks a holistic approach. Objective: The purpose of this review is to ascertain the ethical concerns of AI applications in healthcare, to identify the knowledge gaps and provide recommendations for an ethical and legal framework. Methodology: Electronic databases Pub Med and Google Scholar were extensively searched based on the search strategy pertaining to the purpose of this review. Further screening of the included articles was done on the grounds of the inclusion and exclusion criteria. Results: The search yielded a total of 1238 articles, out of which 16 articles were identified to be eligible for this review. The selection was strictly based on the inclusion and exclusion criteria mentioned in the manuscript. Conclusion: Artificial intelligence (AI) is an exceedingly puissant technology, with the prospect of advancing medical practice in the years to come. Nevertheless, AI brings with it a colossally abundant number of ethical and legal problems associated with its application in healthcare. There are manifold stakeholders in the legal and ethical issues revolving around AI and medicine. Thus, a multifaceted approach involving policymakers, developers, healthcare providers and patients is crucial to arrive at a feasible solution for mitigating the legal and ethical problems pertaining to AI in healthcare.
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Patient privacy and autonomy, informed consent, transparency, equality and biases are some of the major concerns regarding AI.
“It should be noted that patient privacy and autonomy, informed consent, transparency, equality and biases are some of major concerns [50].”
From Medical, dental, and nursing students’ attitudes and knowledge towards artificial intelligence: a systematic review and meta-analysis (2024) · cites this paper as [50]
AI systems have shown proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.
“For instance, AI systems have shown remarkable proficiency in structuring vast medical data, identifying clinical patterns, and validating medical hypotheses.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [5]
AI can offer computational assistance for complex clinical decisions such as CPR and DNAR status that aligns with patient preferences while mitigating human biases under stress and time pressure.
“In scenarios like cardio-pulmonary resuscitation and determining Do Not Attempt to Resuscitate (DNAR) status, AI can offer computational assistance that aligns with patient preferences while mitigating human biases in decision-making under stress and time pressure [4][5][6].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [5]
As models become more accurate, they can provide better predictions and recommendations, improving patient outcomes.
“As models become more accurate, they can provide better predictions and recommendations, improving patient outcomes.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [5]
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“It should be noted that patient privacy and autonomy, informed consent, transparency, equality and biases are some of major concerns [50].”
BackgroundRony, M. K. K., Numan, S. M., Akter, K., Tushar, H., Debnath, M., Johra, F. T., Akter, F., Mondal, S., Das, M., Uddin, M. J., et al. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e36702 · Cited by 93 · see citing works
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- Authors
- Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M.
- Affiliations
- Panimalar Medical College Hospital & Research Institute (IN)
- Corresponding author
- Surapaneni, Krishna Mohan
- Venue
- Journal of Personalized Medicine
- Publisher
- MDPI AG
- Published
- 2022-11-16
- Volume
- 12
- Issue
- 11
- Pages
- 1914
- ISSN
- 2075-4426 (ISSN-L)
- PMC
- PMC9698424
- OpenAlex
- W4309604980
- Cited by
- 159
- References
- 43
- Citations by year
- 2026: 33 · 2025: 43 · 2024: 59 · 2023: 24
- FWCI
- 2.16
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Personalized Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Law › Digitalization, Law, and Regulation
- Keywords
- artificial intelligence in healthcare, AI ethics, legal framework for AI, legal and ethical issues, health data privacy, AI regulation, knowledge gaps
- MeSH terms
- Review Literature As Topic, Review Literature as Topic
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