Artificial Intelligence (AI) is rapidly transforming healthcare, promising improved diagnoses, personalized treatments, and greater operational efficiency. However, as AI’s role in healthcare expands, so too do the ethical challenges associated with its implementation. The responsible development and deployment of AI in healthcare requires careful consideration of fairness, transparency, accountability, and patient-centered care.
The Current Landscape of Healthcare AI Ethics
Healthcare AI presents unique ethical challenges compared to AI applications in other sectors. When an algorithm makes a recommendation about patient care, the stakes can be life-altering—even life-threatening. Despite remarkable advances in AI capabilities, fundamental ethical concerns remain about how these technologies should be responsibly integrated into clinical environments.
A recent international consensus framework, FUTURE-AI, has established six guiding principles for trustworthy AI in healthcare: Fairness, Universality, Traceability, Usability, Robustness, and Explainability. This framework provides structured recommendations for ensuring that AI technologies are developed and implemented responsibly, with a focus on ethical considerations throughout their lifecycle.[^1]
As the FDA accelerates its plan to integrate generative AI across all its departments to enhance the efficiency of evaluating drugs, foods, medical devices, and diagnostic tests, questions about data security, transparency, and appropriate guardrails are becoming increasingly urgent. FDA Commissioner Marty Makary has ordered immediate deployment of AI across all offices with a tight deadline, emphasizing the technology’s potential to drastically reduce review times for new therapies from “days to just minutes.”[^2]
Key Ethical Challenges in Healthcare AI
Justice and Fairness
Healthcare AI systems can perpetuate or even exacerbate existing biases, often resulting from non-representative datasets and opaque model development processes. A study examining ethical challenges in the integration of AI into clinical practice highlighted justice and fairness as the most frequently cited concerns, appearing in more than half of all analyzed literature.[^3]
One dramatic example involved a widely used healthcare algorithm that assigned equal risk levels to black and white patients, despite black patients being significantly sicker. The algorithm used healthcare costs as a proxy for medical need, unintentionally incorporating racial bias since less money is typically spent on black patients. When this disparity was identified and corrected, the percentage of black patients receiving additional care increased from 17.7% to 46.5%.[^3]
Transparency and Explainability
The “black box” nature of many AI systems creates a crisis of interpretability that is particularly problematic in healthcare, where clinicians must understand and explain AI-driven decisions to patients. Current AI explanations are often generated post-hoc and can be inaccurate or misleading, undermining trust in the system.
Healthcare providers face significant uncertainty in their decision-making due to this lack of transparency, highlighting the critical importance of developing explainable AI systems. Patients deserve to understand the process behind healthcare decisions influenced by AI, requiring caregivers to effectively communicate both the capabilities and limitations of AI-driven recommendations.[^3]
Consent and Confidentiality
AI models in healthcare often require vast amounts of patient data, creating inherent conflicts between comprehensive data collection and patient privacy rights. Even when confidentiality measures are in place, data breaches remain a significant risk, emphasizing the importance of robust informed consent processes.
For AI systems that continuously update using patient data, questions arise about whether patient consent should be periodically renewed. Patients should maintain the right to opt out at any time, though this may impact models that have already incorporated their data into learning algorithms.[^3]
Accountability
When AI systems provide erroneous or unsafe recommendations, determining accountability becomes challenging. The interplay between model developers, organizational leaders, and healthcare providers creates complex responsibility chains, with each party potentially reluctant to accept liability.
This misalignment of risk and reward requires careful ethical consideration. AI developers may prioritize financial concerns over ethical responsibilities, while medical professionals may develop a false sense of immunity when relying on AI systems, potentially increasing patient risk.[^3]
Frameworks for Responsible Innovation
As the field evolves, several promising frameworks are emerging to guide the ethical implementation of healthcare AI:
Multi-Scale Ethics
Rather than focusing solely on individual impacts, this framework considers AI as a “socio-technical” system with ethical implications at multiple levels of community, from individual patients to entire populations. By identifying patterns of risks and benefits across these levels, developers can better anticipate ethical challenges and implement appropriate safeguards.[^3]
SHIFT Principles
The SHIFT acronym—Sustainability, Human Centeredness, Inclusiveness, Fairness, and Transparency—offers a standardized approach to responsible AI development. A thematic analysis of 253 research articles identified specific subthemes within these principles, with algorithmic bias, informed consent, explainability, and privacy emerging as the most prevalent concerns.[^3]
Algorithmovigilance
Inspired by pharmacovigilance in drug development, this approach demands consistent evaluation of algorithms to mitigate bias and ensure fairness. The process requires vigilance at every stage of AI system development, as developers may unconsciously introduce biases through sample selection, historical data patterns, or self-serving analysis methods.[^3]
The Path Forward: Building Ethical AI in Healthcare
For organizations developing or implementing AI in healthcare, several approaches can help ensure ethical compliance:
Diverse Development Teams
Many experts attribute flaws in AI systems to homogeneous development teams. Encouraging collaboration among clinicians, data scientists, ethicists, and patient advocacy groups can help identify and address potential biases early in the development process.
Oversight Review Mechanisms
Organizations should consider implementing oversight reviews before deploying AI in healthcare settings. These reviews should involve multidisciplinary teams that actively search for bias, assess explainability, and ensure transparency.
Continuous Monitoring and Updating
Ethical AI implementation requires ongoing vigilance, not just pre-deployment evaluation. Systems should be regularly monitored for performance drift, emerging biases, or unexpected consequences, with clear processes for updating models when issues are identified.
Patient-Centered Design
Ultimately, healthcare AI must prioritize patient outcomes and experiences. This means involving patients in design processes, clearly communicating AI capabilities and limitations, and ensuring that AI augments rather than replaces the human elements of healthcare delivery.
Conclusion
The integration of AI into healthcare offers tremendous potential benefits, but realizing these benefits ethically requires deliberate attention to fairness, transparency, accountability, and patient-centered care. By adopting frameworks like FUTURE-AI and implementing robust oversight mechanisms, healthcare organizations can navigate the complex ethical landscape of AI implementation.
The goal isn’t to slow innovation but to ensure that innovation moves forward responsibly—balancing technological advancement with ethical considerations and prioritizing patient well-being above all else. As AI becomes increasingly embedded in healthcare systems, our commitment to ethical implementation will determine whether these powerful tools truly advance the fundamental mission of healthcare: improving patient outcomes and experiences.
To discuss how your organization can implement AI ethically in healthcare settings, contact The Pharma:Health Practice today.
Footnotes
- Lekadir, Karim, et al. “FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare.” bmj 388 (2025).
- “FDA’s plan to roll out AI agencywide raises questions,” Axios, May 2025.
- Ellison B., et al. “Ethical challenges and evolving strategies in the integration of artificial intelligence into clinical practice.” PLOS Digital Health 4.4 (2025): e0000810.