A new report by Kev Coleman, Visiting Research Fellow at the Paragon Health Institute, outlines a focused approach to regulating artificial intelligence in healthcare. The report aims to balance patient safety with continued innovation. Rather than calling for sweeping restrictions, it promotes targeted oversight tailored to specific risks.
According to Coleman, poorly designed regulation could slow medical progress. In particular, overbroad rules may increase costs and delay life-saving technologies. Therefore, he urges policymakers to adopt a careful and informed strategy.
The Risk of Misregulation
The report identifies several barriers to effective AI governance. First, policymakers often lack a clear understanding of AI’s technical differences. As a result, they may group together machine learning, neural networks, and generative AI under one regulatory approach.
However, these technologies present different risks and use cases. When lawmakers overgeneralize, they risk creating rules that miss real concerns while restricting beneficial innovation. In addition, new rules may duplicate existing safeguards, which can create confusion and unnecessary burdens.
A Technology-Specific Framework
To address these issues, Coleman proposes technology-specific guidance. Regulations should clearly define the type of AI system under review. This ensures that oversight fits both the technology and the medical context.
Furthermore, the report recommends risk-based assessments. For example, AI used in clinical diagnosis warrants stricter review than AI used for scheduling or billing. By aligning oversight with real-world impact, regulators can protect patients without stifling innovation.
Equally important, Coleman advises against duplicating existing frameworks. Agencies such as the U.S. Food and Drug Administration (FDA) already regulate many medical technologies through premarket review pathways. Likewise, HIPAA privacy standards address data protection concerns. Therefore, policymakers should build on these systems rather than create redundant structures.
Regulatory Sandboxes and Innovation
The report also promotes regulatory sandboxes. These controlled environments allow developers to test AI tools with temporary regulatory flexibility. In turn, regulators gain insight into emerging risks while companies refine their products responsibly.
Additionally, Coleman highlights data integrity as a core issue. AI systems depend on training data, and biased or incomplete datasets can affect outcomes. Still, he notes that data quality challenges are not unique to AI. Instead, regulators should apply consistent standards across both AI and traditional healthcare technologies.
Preserving U.S. Leadership in Healthcare AI
Ultimately, the report calls for a balanced approach. It supports strong patient protections while preserving incentives for research and development. By leveraging existing agencies and focusing on specific risks, policymakers can foster innovation and maintain U.S. leadership in healthcare AI.
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