Regulating the algorithms of health care

Artificial intelligence (AI) systems used in health care may require stronger safeguards to address bias, privacy, and patient autonomy, a narrative review of global governance frameworks for medical AI according to narrative review published in Cureous. 

The review, led by Prashant K. Singh, MD, of the Department of General Surgery at the All India Institute of Medical Sciences (AIIMS) in Patna, India, evaluated international regulations and ethical guidance governing artificial intelligence-driven health technologies. 

Artificial intelligence tools are increasingly being integrated into clinical workflows and supporting tasks such as medical image interpretation, risk prediction, and electronic health record analysis. These applications depend on large volumes of patient data, which raises multiple questions about ethical use. "The reliance of AI systems on vast amounts of personal data raises significant concerns regarding data privacy, security, and ethical governance," the review authors wrote. 

Regulatory framework review

The review examined major regulatory frameworks—including the European Union’s General Data Protection Regulation (GDPR), the U.S. Health Insurance Portability and Accountability Act (HIPAA), and guidelines from the Organisation for Economic Co-operation and Development (OECD)—alongside India’s emerging digital health regulations.  

Across these frameworks, several recurring challenges emerged, including algorithmic bias, unclear patient consent processes, data-security vulnerabilities, and limited transparency in how AI systems generate clinical recommendations. 

When training datasets do not adequately represent diverse patient populations, algorithmic bias remains a major concern. Such imbalances may lead to unequal diagnostic accuracy or treatment recommendations across demographic groups, raising concerns about fairness in AI-assisted care. 

Privacy risks present a major risk, as well. Even when patient datasets are anonymized, advanced analytical methods allow individuals to be "reidentified" by linking datasets from multiple sources, which creates new challenges for patient confidentiality. 

The narrative review also highlighted challenges surrounding transparency and explainability. Many AI systems function as complex black box models that can be difficult for clinicians or patients to interpret, complicating oversight and accountability when errors occur. 

“Meaningful transparency can be achieved by reporting training data sources, performance metrics, and known limitations,” the review authors wrote, emphasizing the importance of documenting how algorithms are developed and validated. 

The authors concluded that stronger governance frameworks and clearer implementation guidance will be needed as AI tools become more widely integrated into health care. Policy efforts, they suggested, should focus on harmonizing international standards, strengthening oversight, and promoting privacy-by-design approaches to health data use. 

The authors reported no relevant conflicts of interest.