Artificial intelligence is transforming healthcare at breakneck speed, but one critical voice warns the industry risks creating a dangerous gap between innovation and accountability. Donna R. Cryer, a healthcare governance expert, argues that hospitals, payers, pharmaceutical companies, and digital health organizations are rapidly deploying AI systems without meaningfully involving the patients most affected by these decisions.
What Happened
The healthcare sector is experiencing an AI explosion. From diagnostic imaging to drug discovery to administrative operations, AI systems are being integrated into clinical workflows at an unprecedented pace. However, Cryer’s analysis reveals a troubling pattern: these implementations often proceed with minimal patient input or public transparency about how AI algorithms will influence medical decisions, data usage, and treatment recommendations.
This governance vacuum creates significant risks. When AI systems make clinical recommendations or determine insurance coverage eligibility, the people most impacted—patients—rarely have a seat at the table during development, testing, or deployment phases.
Key Points
Cryer’s position centers on several critical concerns. First, AI bias in healthcare can perpetuate existing disparities if development teams lack diverse perspectives. Second, patient privacy and data governance require meaningful oversight from those whose information is being processed. Third, transparency about how AI influences medical decisions is essential for informed consent.
The current approach treats patients as data sources rather than stakeholders. Healthcare organizations collect vast amounts of patient information to train AI models, yet patients typically have little visibility into how their data is used, stored, or protected. This asymmetry of power and information undermines trust in the healthcare system itself.
Additionally, without patient leadership in AI governance, organizations risk deploying systems that don’t align with real-world patient needs or values. Clinicians and administrators might optimize for efficiency metrics that conflict with patient priorities like compassionate care or shared decision-making.
What This Means
Healthcare organizations must fundamentally shift their approach to AI governance. Rather than treating patient involvement as an afterthought, hospitals and health systems should establish patient advisory boards specifically focused on AI ethics and implementation. These groups should have genuine decision-making power, not merely consultative roles.
For patients, this is an opportunity to advocate for transparency requirements and algorithmic accountability. Healthcare consumers should demand to know when AI influences their care and have options to opt out or seek alternative approaches.
The stakes couldn’t be higher. Healthcare AI decisions literally impact life and death outcomes. Without patient leadership ensuring responsible development and deployment, the industry risks creating systems that optimize for corporate interests rather than human wellbeing. The window for establishing proper governance structures is closing rapidly—the time for meaningful patient involvement is now.
