Should Hospitals Be Required to Disclose AI Error Rates to Patients?
The debate centers on balancing patient transparency with the complexities and risks of using AI in healthcare.
THE DILEMMA
Revealing AI error rates could empower patients but also confuse them and create legal and ethical challenges.
Do patients have the right to know when the AI guiding their care gets it wrong?
As artificial intelligence becomes more common in healthcare, a new policy question has emerged: should hospitals be required to disclose the error rates of AI systems to patients? AI is now used to assist with medical imaging, patient triage, and diagnostic decisions, yet patients are rarely told when an algorithm influences their care. This raises concerns about whether informed consent is truly possible when patients do not understand the reliability or limitations of the technology involved. Supporters of mandatory disclosure argue that transparency is essential to patient autonomy. If an AI system plays a meaningful role in diagnosis or treatment, patients arguably have a right to know how accurate that system is. Research has shown that some medical algorithms perform less accurately for racial minorities, women, and patients with rare conditions, making disclosure especially important for equity and accountability. Requiring hospitals to report AI error rates could also push healthcare systems and technology companies to test algorithms more rigorously and monitor their performance over time, similar to how hospitals already report infection rates and surgical outcomes. Opponents, however, argue that AI error rates are difficult to define and easy to misunderstand. AI performance can vary depending on how clinicians use it and what data it receives, so a single statistic may oversimplify a complex process. There are also concerns that disclosure could increase patient anxiety, undermine trust in care, and expose hospitals to greater legal liability. Additionally, many AI systems are proprietary, making it difficult for hospitals to access or standardize detailed performance data. Ultimately, this debate reflects a broader challenge in health policy: balancing transparency with complexity. As AI becomes more influential in medical decision-making, pressure is likely to grow for clearer disclosure standards. A middle-ground approach, such as requiring hospitals to disclose when AI is used and explain its general role and limitations, may offer a practical compromise that protects patient rights without overwhelming them.
Works Cited
U.S. Food and Drug Administration. “Artificial Intelligence and Machine Learning in Medical Devices.”
Obermeyer, Ziad et al. “Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations.” Science.
