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"html": "<p style=\"text-align: justify\"><strong>Introduction</strong></p><p style=\"text-align: justify\">The agentic capabilities of large language models (LLMs) offer considerable promise for the healthcare sector. Agentic AI refers to AI systems that can make autonomous decisions and perform tasks with a degree of independence, often with minimal human oversight. </p><p style=\"text-align: justify\">While some proponents argue that agentic AI will obviate the need for primary care doctors altogether, it would be difficult to envision a healthcare system fully managed by AI-powered agents without any human personalized interaction. Nonetheless, the transformative effects of agentic AI in healthcare cannot be overstated, particularly in light of the challenges surrounding electronic health records administration and the prevalence of physician burnout, which are driven in large part by inefficient hospital information systems. </p><p style=\"text-align: justify\">However, as these technologies advance, they present significant legal risks, including concerns around regulatory compliance, the potential for biased or inaccurate data, and safeguarding patient privacy, all of which must be carefully addressed to ensure that the benefits of agentic AI are realized without compromising patient safety or violating legal standards.</p><p style=\"text-align: justify\">Over the next three weeks, we will outline the legal implications of agentic AI in healthcare. This first blog covers regulatory compliance and provides practical tips.</p><p style=\"text-align: justify\"><strong>Regulatory compliance</strong></p><p style=\"text-align: justify\">Regulatory compliance remains a critical issue for the integration of agentic AI in healthcare as regulators and policymakers around the world grapple with balancing innovation in agentic AI with safeguarding public health. </p><p style=\"text-align: justify\">In the EU, the AI Act aims to provide a clear framework for AI applications, with a focus on transparency, accountability, and patient safety. The AI Act classifies AI systems based on their risk levels, with higher-risk applications like healthcare AI facing stricter requirements, including data governance and human oversight. The Act also includes specific rules for underlying AI models. </p><p style=\"text-align: justify\">Further, the European Health Data Space (EHDS) seeks to create a unified health data space, ensuring interoperability and addressing privacy and security concerns while also enabling health data to be exchanged and used for research and innovation purposes, including for the development of agentic AI in healthcare. After being formally adopted by the Council of the European Union in January, the new EHDS rules will become applicable in all EU member states between 2027 and 2031.</p><p style=\"text-align: justify\">Meanwhile, in the US, the regulatory landscape remains fragmented, with oversight split between federal agencies such as the Food and Drug Administration (FDA) and Federal Trade Commission and state laws that vary in scope and application. In his first week back in office, President Trump rescinded President Biden’s executive order on safe, secure, and trustworthy AI and issued his own AI order aimed at reinforcing American leadership in AI by eliminating regulatory barriers and revoking prior policies. </p><p style=\"text-align: justify\">While the federal government seeks to scale back its supervision of AI developers, companies must continue to contend with state regulation. The inconsistency between federal and state-level regulations in the US, in addition to emerging foreign regulations, poses challenges for healthcare providers and AI developers, who must navigate a complex patchwork of laws and regulations to ensure compliance while fostering innovation in the global health technology industry.</p><p style=\"text-align: justify\">An emerging regulatory focus in the US is the FDA’s classification of AI as “software as a medical device.” The FDA recently issued draft guidance for AI-enabled medical devices, providing developers with key product design, development, and documentation recommendations for initial submissions to help ensure the safety and effectiveness of such devices. </p><p style=\"text-align: justify\">The draft guidance also includes the FDA’s current thinking on strategies to address transparency and mitigate bias throughout the life cycle of a device. This guidance follows the FDA’s recently issued final guidance on predetermined change control plans for AI-enabled devices, which provides recommendations on how developers can proactively plan for device updates once the product is on the market. The FDA’s evolving framework on AI-enabled medical devices demonstrates the agency’s efforts to ensure that agentic AI applications in healthcare meet stringent standards of safety, efficacy, and performance. </p><p style=\"text-align: justify\"><strong>Practical tips:</strong></p><p style=\"text-align: justify\">For developers working with agentic AI applications in healthcare, being mindful of the fragmented and evolving regulatory landscape is important. A best practice for developers to consider would be to build a compliance program around the key common elements among the various international, federal, and state laws, regulations, and guidance, which include:</p><ul style=\"list-style-type: disc\"><li style=\"text-align: justify\">Design for transparency and explainability: Develop AI systems that are accurate and explainable, using techniques that allow clinicians to understand how the AI arrived at its decisions.</li><li style=\"text-align: justify\">Continuous validation and testing: Regularly validate the AI model with real-world data, including by conducting clinical trials. Keep track of how the system performs over time to address any changes or emerging medical trends.</li><li style=\"text-align: justify\">Clear documentation: Provide thorough and clear documentation that explains the AI model’s design, development process, data sources, testing procedures, and validation results.</li><li style=\"text-align: justify\">Focus on patient safety and ethical use: Ensure that the AI system is designed with patient safety as a top priority. Review ethical considerations to align with evolving regulations and ethical norms in healthcare.</li></ul><p style=\"text-align: justify\">These practices can aid developers in navigating the complexities of regulatory compliance while advancing AI innovation in the healthcare sector.</p><input type=\"hidden\" id=\"passle-remote-hosting-tracking-shortcode\" value=\"102k8mi\" />",
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