Ethical concerns and strategies for implementing artificial intelligence in healthcare: a review of empirical studies

TL;DR

This paper reviews ethical concerns and strategies for using AI in healthcare, emphasizing the need for transparency, fairness, and stakeholder involvement.

Contribution

A systematic review of empirical studies identifying six ethical concern categories and actionable strategies for responsible AI implementation in healthcare.

Findings

Key ethical concerns in AI healthcare include transparency, bias, privacy, accountability, ethics, and regulation.

Strategies for ethical AI implementation involve transparency, bias mitigation, auditing, and stakeholder governance.

The review provides actionable insights for developers, healthcare professionals, and policymakers.

Abstract

Artificial intelligence (AI) is profoundly transforming the healthcare landscape, presenting unprecedented opportunities to enhance patient care and clinical outcomes. However, the rapid integration of AI technologies has raised significant ethical concerns, requiring rigorous scrutiny to ensure their responsible and equitable use. This study aimed to explore the ethical considerations and strategies related to the implementation of AI in healthcare through a systematic review. A systematic search identified 243 publications published between 2019 and 2025 that were initially identified. After applying inclusion and exclusion criteria, 22 papers were selected for final synthesis to assess ethical concerns and strategies related to AI in healthcare. The analysis identified key ethical concerns, categorizing them into six distinct groups: (1) Transparency and Trust, (2) Bias and Fairness, (3) Privacy and Data Security, (4) Accountability and Responsibility, (5) Ethical and Moral, (6) Regulatory and Legal. Additionally, several ethical strategies were identified in the implementation of AI systems, including adherence to ethical principles, standards, and frameworks; transparency and bias mitigation; monitoring and auditing of AI systems; and stakeholder involvement and governance in decision-making processes. This review emphasizes the importance of addressing these ethical concerns to ensure the successful implementation of AI technologies in healthcare. The findings provide valuable insights and recommendations for stakeholders, including developers, healthcare professionals, and policymakers, to guide the ethical deployment of AI decision support systems in healthcare.

The online version contains supplementary material available at 10.1186/s12910-026-01404-8.

Figures