The Ethical Compass: Navigating Ethical AI Models in Healthcare

Artificial intelligence (AI) is rapidly transforming healthcare, offering unprecedented opportunities for diagnosis, treatment, research, and resource allocation. However, this powerful technology also brings complex ethical dilemmas and governance issues that demand careful consideration and proactive solutions. Without robust ethical frameworks and clear governance structures, the benefits of AI might be overshadowed by unintended consequences, ethical breaches, and a loss of public trust. This article highlights the importance of comprehensive governance and ethical frameworks to guide the development, deployment, and use of AI in healthcare, especially in sensitive areas like resource allocation. 

This author reviewed several key ethical principles, emerging governance frameworks, and practical steps for executives to ensure the responsible and beneficial adoption of AI. It is clear that any governance approach for healthcare AI must be grounded in core ethical principles that have long guided medical practice. The following are seven essential core bioethical pillars, as shown in Figure 1.

Figure 1: Ethical Pillars in Deployment and Utilization of AI Systems in Healthcare

1. Beneficence relates to doing good: AI should be used in ways that benefit patients and the healthcare system, aiming to improve outcomes, enhance efficiency, and promote well-being. Governance frameworks should ensure that AI applications are designed and implemented with a clear focus on their positive impact.

2. Non-maleficence refers to doing no harm: AI systems must be developed and deployed in a way that minimizes potential harm to patients, providers, and the healthcare system. This includes addressing risks related to bias, errors, privacy breaches, and the erosion of human elements of care. Governance should mandate rigorous testing, validation, and ongoing monitoring to identify and mitigate potential harms.

3. Autonomy refers to respecting people: Patients have the right to make informed decisions about their care. In the context of AI, this requires transparency about how AI is being used in their diagnosis, treatment, and resource allocation. Governance models should emphasize the need for clear communication, understandable explanations of AI-driven recommendations, and the right for patients to consent to or decline AI-supported interventions.

4. Justice refers to fairness and equity: AI should be used in a way that promotes fairness and equity in healthcare access and outcomes. Governance frameworks must actively address and mitigate algorithmic bias, ensuring that AI systems do not perpetuate or exacerbate existing health disparities. This requires commitment to data diversity, bias auditing, and equitable design principles.

5. Transparency and explainability refer to the decision-making processes of AI systems, especially in critical areas (e.g., diagnosis, resource allocation): Governance should encourage the development and use of explainable AI techniques to build trust and enable stakeholders to understand and challenge AI outputs when necessary.

6. Accountability and responsibility refer to clear lines of accountability and responsibility that must be established for the development, deployment, and use of AI in healthcare: Governance models should define who is responsible for the performance of AI systems, for addressing errors or biases, and for ensuring compliance with ethical and legal standards. This includes both the developers and the healthcare professionals who use and oversee AI applications.

7. Privacy and data security refer to the highly sensitive patient data that must be protected: Governance frameworks must prioritize patient privacy and data security in the design and use of AI, ensuring compliance with relevant regulations (e.g., GDPR, HIPAA) and implementing robust security measures to prevent unauthorized access or misuse of data.

Frameworks for a Moral Compass

To adhere to the above ethical pillars, several frameworks are emerging, guiding the ethical development and deployment of AI in healthcare. These frameworks offer different perspectives and approaches, and a comprehensive governance model may draw upon several of them. Some frameworks, often developed by professional organizations, academia, or international bodies, outline high-level ethical principles and values that should guide AI development and use (OECD Principles on AI, the IEEE Ethically Aligned Design, and medical society guidelines).

Although these frameworks provide a moral compass, they may require further translation into specific policies and procedures to be effective. Risk-based frameworks categorize AI applications based on their potential risk to patients and the healthcare system. Higher-risk applications, such as AI used in critical diagnosis or resource allocation, are subject to more stringent regulatory requirements and oversight. Another approach emphasizes the need for ethical considerations and governance mechanisms throughout the entire AI lifecycle, from data collection and algorithm design to deployment, monitoring, and evaluation. It recognizes that ethical issues can arise at any stage and require ongoing attention and adaptation. Some models emphasize the importance of involving a wide range of stakeholders, including patients, providers, developers, policymakers, and the public, in the development and governance of healthcare AI. This ensures that diverse perspectives are considered and that AI systems are aligned with societal values and needs.

Governance Models

To ensure the responsible and beneficial use of AI in our healthcare operations, we need to develop a comprehensive and adaptable governance model that incorporates the ethical principles and best practices outlined above. This model should include several elements. First, an interdisciplinary committee comprising clinicians, ethicists, legal experts, IT professionals, and patient representatives will be responsible for overseeing the ethical development and deployment of AI within our organization. Its responsibilities will include developing ethical guidelines, reviewing AI proposals, conducting risk assessments, and addressing ethical concerns. Second, translate high-level ethical principles into specific, actionable policies and procedures for the development, validation, deployment, and use of AI applications. These policies should address issues such as data governance, bias mitigation, transparency requirements, accountability mechanisms, and patient rights and responsibilities.

Practice Implications for Executives

To establish robust processes for evaluating the safety, efficacy, and fairness of AI systems before deployment, executives must thoroughly test, validate using diverse datasets, and continuously monitor the performance of AI in real-world settings. Moreover, executives should favor the use of AI models that are interpretable and can provide clear explanations for their recommendations, especially in high-stakes decisions. Executives in health systems need to ensure that clinicians have the tools and training to understand and communicate AI outputs to patients. This author recommends the following practical steps:

Recommendations for Executives

1. Clearly define the roles and responsibilities of all individuals involved in the AI lifecycle, from developers and data scientists to clinicians and administrators. Establish clear lines of accountability for the performance and ethical implications of AI systems.

2. Provide ongoing education and training to all staff on the ethical considerations and implications of AI in healthcare. Encourage open discussion and critical reflection on ethical challenges and promote a culture of responsible innovation.

3. Create clear channels for patients to provide feedback on their experiences with AI-supported care and to seek redress if they believe they have been unfairly impacted by an AI decision.

4. Commit to ongoing dialogue with stakeholders, monitoring emerging best practices, and adapting our governance model as needed to ensure it remains relevant and effective. Figure 2 presents the practice recommendations.

Figure 2: Practice Recommendations

Conclusion

Establishing robust governance and ethical frameworks is not merely a matter of compliance; it is fundamental to building trust in AI and ensuring its responsible and beneficial integration into healthcare. By proactively addressing the ethical challenges and implementing comprehensive governance mechanisms, health systems can harness the transformative potential of AI while upholding our commitment to patient well-being, equity, and the highest standards of ethical care. This proactive and thoughtful approach will be crucial for navigating the complex future of AI-driven healthcare.

Additional Reading

• Bouderhem R. Shaping the future of AI in healthcare through ethics and governance. Humanities and social sciences communications. 2024 Mar 15;11(1):1-2.

• Li F, Ruijs N, Lu Y. Ethics & AI: A systematic review on ethical concerns and related strategies for designing AI in healthcare. Ai. 2022 Dec 31;4(1):28-53.

• Morley J, Floridi L. The ethics of AI in healthcare: An updated mapping review. Ethics and Medical Technology: Essays on Artificial Intelligence, Enhancement, Privacy, and Justice. 2025 Jul 24:29-57.