June 10, 2025
AI in Healthcare Compliance: How to Identify and Manage Risk
Contents
As more healthcare organizations incorporate AI into their processes, we’re beginning to see impressive transformations in the patient experience. In a 2024 survey conducted by the American Medical Association, 66% of the nearly 1,200 physicians surveyed reported using AI to assist with their work. These rates are increasing, and practitioners have a legal obligation to use AI responsibly. For those using AI in healthcare, compliance with laws and regulations is of the utmost importance.
The Growing Role of AI in Healthcare
Hospitals, health systems, and practitioners are integrating AI into their processes in ways that are creating incredible outcomes for patients. Diagnostic tools assist in detecting anomalies in medical imaging which the human eye might miss. Predictive analytics forecast patient deterioration, enabling providers to take preventative measures. Additional use cases for AI in health and life sciences are continuing to arise, and the possibilities seem endless.
As adoption scales, though, so do AI risks. And when patient health is on the line, unmitigated risks could lead to severe consequences up to and including life-threatening events. If an organization or practitioner is found to have used AI irresponsibly, they could be at risk for malpractice lawsuits and other legal and reputational repercussions.
The AI risk landscape, though, can often be complex and difficult to understand. Bias in training data for an AI system can lead to unequal treatment across demographic groups, and lack of explainability can pose difficulties for clinicians trying to understand or challenge AI-generated decisions. As AI’s role continues to grow, healthcare organizations must shift from viewing AI risk management as an optional add-on, to embedding governance and compliance into the system design.
Regulatory Pressures
Medical organizations are already subject to a number of regulations to protect patients, and introducing AI into operations and clinical decision-making must not jeopardize compliance. To further complicate the regulatory landscape, new laws are being passed to mandate strict requirements around transparency, robustness, human oversight, and conformity assessments.
Several key frameworks are shaping the compliance requirements for AI in healthcare. A few of them include:
- HIPPA (Health Insurance Portability and Accountability Act): In the U.S., HIPAA remains the foundational regulation for data privacy and security in healthcare. It ensures that patient information is protected during data collection, and processing, including when used to train or operate AI models.
- FDA Guidance on AI/ML in Software as a Medical Device (SaMD): The FDA has introduced draft guidance recognizing the unique nature of AI/ML-based medical software. This includes a “total product lifecycle” approach that emphasizes ongoing performance monitoring, real-world validation, and transparency to support safety and efficacy.
- EU AI Act: For global healthcare organizations, the EU AI Act introduces risk-based classifications for AI systems, with healthcare AI often falling under the “high-risk” category. The legislation has a certain set of strict requirements associated with this risk level, and it applies to both organizations located in the European Union as well as those that aren’t but impacts EU citizens.
- Emerging State-Level AI Legislation (US): Across the U.S., states like California, Colorado, and New York are enacting laws that add complexity to AI in healthcare compliance. Depending on each organization’s AI use, they may be affected by new regulations governing generative AI use, which may or may not be focused on the healthcare industry.
Identifying AI Risks in Healthcare
In healthcare, AI risks encompass much more than regulatory noncompliance. AI systems used in diagnosis or treatment recommendations influence real people’s quality of life, and errors could cause misdiagnosis and unnecessary procedures. AI bias also holds a different weight in the medical field. AI systems are trained with historical data, and if you’re not careful, it would be easy to duplicate past medical biases and incorporate them into healthcare practices.
So, how do you realize the efficiency gains that AI has to offer, without putting patients at risk? A strong AI risk management framework should include at least these methods. AI governance software can help to automate and centralize these activities.
A Comprehensive AI Registry
Maintain a detailed inventory of all AI and ML tools in use across your organization. This allows for traceability and explainability if an algorithmic decision comes into question.
Cross-Functional AI Risk Audits
Bring together clinical, technical, legal, and compliance teams to conduct comprehensive reviews of all AI systems. These audits assess system use, decision impact, risk exposure, and governance readiness.
Data Lineage and Model Input Tracing
Map out the origin, flow, and transformation of data used to train and operate AI models. This helps detect potential sources of bias, validate model inputs, and support transparency in audits or regulatory reviews.
The Role of AI Governance Platforms
Modern AI governance platforms like Lumenova AI are designed to bridge the gap between innovation and oversight by embedding compliance and risk management directly into the AI lifecycle. They reduce the administrative burden of regulatory reporting and internal reviews by automating and centralizing AI oversight.
Let us show you how. Book a demo today.