What Is the Role of Ethics Committees in AI Healthcare?

Author: Rasit Dinc

Published on: Dec 20, 2025

As artificial intelligence (AI) continues to permeate every facet of healthcare, from diagnostics and treatment personalization to patient care management, the need for robust ethical oversight has never been more critical. The integration of AI into healthcare research and practice promises unprecedented advancements, but it also brings to the forefront significant ethical challenges that must be addressed to maintain public trust, ensure patient safety, and uphold data integrity [1]. This is where ethics committees, including Institutional Review Boards (IRBs), play a pivotal role. These bodies are essential in navigating the complex moral terrain of AI in healthcare, ensuring that innovation aligns with fundamental ethical principles.

The Imperative for Ethical Oversight in AI Healthcare

The rapid development and deployment of AI technologies in healthcare necessitate a structured ethical framework to guide their application. Without such oversight, there is a risk of ethical lapses that could undermine public trust, compromise patient privacy, and exacerbate existing healthcare disparities [2]. Ethics committees serve as a crucial checkpoint for AI developments, ensuring that these powerful tools are developed and deployed responsibly. Their primary function is to provide a systematic review of AI projects, focusing on potential risks, benefits, and broader ethical considerations.

Core Responsibilities of Ethics Committees in the Age of AI

Ethics committees are tasked with a range of responsibilities to ensure the ethical implementation of AI in healthcare. These responsibilities are an extension of their traditional roles but are adapted to the unique challenges posed by AI.

1. Ethical Review and Approval of AI Projects

Before any AI system is deployed in a clinical setting, it must undergo a rigorous ethical review. Ethics committees are responsible for evaluating AI projects for their ethical compliance, ensuring they adhere to the core principles of medical ethics: respect for autonomy, beneficence, non-maleficence, and justice [2]. This includes a thorough assessment of the AI model's design, the data used to train it, and its potential impact on patients and healthcare providers.

2. Ongoing Monitoring and Oversight

The role of ethics committees does not end with the approval of an AI project. Continuous monitoring and oversight of deployed AI systems are crucial to identify and address any new ethical issues that may arise [2]. This includes regular audits of AI systems for bias, performance, and unintended consequences. As AI models can evolve over time, ongoing oversight ensures that they remain aligned with ethical principles and societal values.

3. Development of Ethical Guidelines and Best Practices

Given the novelty of AI in healthcare, there is a need for clear ethical guidelines and best practices. Ethics committees are instrumental in developing these guidelines, which are tailored to the specific context of their institution and the populations it serves. These guidelines provide a framework for researchers and developers to follow, ensuring that ethical considerations are integrated into every stage of the AI lifecycle.

4. Fostering Multidisciplinary Collaboration

The ethical challenges of AI in healthcare are complex and multifaceted, requiring a multidisciplinary approach. Ethics committees bring together experts from various fields, including medicine, data science, law, and ethics, to address these challenges collaboratively. This interdisciplinary collaboration is essential for a comprehensive and well-rounded evaluation of AI systems.

Key Ethical Challenges and How Committees Address Them

Ethics committees are at the forefront of addressing the key ethical challenges posed by AI in healthcare.

Bias and Fairness

AI algorithms are susceptible to bias, which can perpetuate and even exacerbate existing healthcare disparities. Ethics committees play a crucial role in mitigating this risk by mandating that AI systems are trained on diverse and representative datasets and that they are regularly audited for bias [2].

Transparency and Explainability

The so-called "black box" nature of some AI models can make it difficult to understand how they arrive at their decisions. This lack of transparency and explainability is a significant ethical concern, particularly in a high-stakes field like healthcare. Ethics committees advocate for the use of explainable AI (XAI) models and ensure that there are mechanisms in place to explain AI-driven decisions to both clinicians and patients [2].

Privacy and Data Protection

AI systems in healthcare often require access to large amounts of sensitive patient data. Protecting patient privacy and ensuring data security are paramount. Ethics committees are responsible for ensuring that strict protocols are in place to protect patient data, in compliance with regulations such as the Health Insurance Portability and Accountability Act (HIPAA) [2].

Accountability and Liability

When an AI system makes an error, determining accountability can be challenging. Ethics committees help to clarify lines of accountability by establishing clear guidelines on the roles and responsibilities of developers, healthcare institutions, and clinicians in the deployment and use of AI systems [3].

Conclusion

The integration of AI into healthcare holds immense promise for improving patient care and advancing medical research. However, to realize this potential, it is essential that AI is developed and deployed in an ethical and responsible manner. Ethics committees are indispensable in this endeavor, providing the necessary oversight and guidance to navigate the complex ethical landscape of AI in healthcare. By ensuring that AI systems are fair, transparent, and accountable, ethics committees play a vital role in building trust among patients and the public, and in ensuring that AI serves the best interests of humanity.

References

[1] Abujaber, A. A., Nashwan, A. J., & Fadlalla, A. (2022). Enabling the adoption of machine learning in clinical decision support: A Total Interpretive Structural Modeling Approach. Inform Med Unlocked, 33, 101090.

[2] Abujaber, A. A., & Nashwan, A. J. (2024). Ethical framework for artificial intelligence in healthcare research: A path to integrity. World J Methodol, 14(3), 94071.

[3] Iserson, K. V. (2024). Informed consent for artificial intelligence in emergency medicine: A practical guide. Am J Emerg Med, 76, 225–230.