AI in healthcare is an increasingly important area of study, as artificial intelligence (AI) continues to transform the way medical professionals diagnose, treat, and prevent diseases. Gardner Law, PLLC proudly co-hosted a thought-provoking CLE event with the Mitchell Hamline School of Law Health Law Institute, titled “AI in Healthcare: Adapting to World-Changing Technology.” With over 100 participants, the event featured four in-depth presentations and a lively panel discussion exploring the cutting edge of AI in medicine. Keynote speakers included Gardner Law attorneys Mark Gardner, Nathan Downing, and Paul Rothermel, along with distinguished healthcare leaders Barbara Colombo, Director of the Health Law Institute at Mitchell Hamline; Kelly Henderson, VP of Risk Services; Dr. Brita Hansen, Medical Director of Analytics and Digital Innovation at Allina Health; and Dr. Christopher Tignanelli, Associate Dean for Data Science, co-Director of the University of Minnesota’s Program for Clinical Artificial Intelligence and its Quality Outcomes, Discovery and Evaluation Core. The event provided a comprehensive overview of AI in healthcare, covering applications such as diagnostic assistance, predictive analytics for patient outcomes, and enhancing practitioner capabilities. Overview of AI in Healthcare
Mark Gardner began the presentation by introducing AI as a collection of computational systems designed to mimic human intelligence, emphasizing its significant impact in healthcare through applications such as diagnostic assistance, predictive analytics for patient outcomes, and enhancing practitioner capabilities.

• Diagnostic assistance: AI can help doctors analyze medical images and provide accurate diagnoses
• Predictive analytics for patient outcomes: AI can analyze large amounts of data to predict patient outcomes and help doctors make informed decisions
• Enhancing practitioner capabilities: AI can help doctors with routine tasks, freeing up time to focus on more complex cases

Gardner underscored the importance of AI in efficiently managing vast data sets and described practical healthcare applications like Radiology Partners for diagnostic assistance and patient outcome predictions post-surgery. The presentation provided a brief history of AI and a helpful glossary of common terms and definitions. Regulation, Risk, and Reality
Nathan Downing, Managing Attorney at Gardner Law, provided an in-depth examination of the FDA regulatory framework for AI in healthcare. He explained that AI poses unique regulatory challenges due to its rapid evolution, which consistently outpaces regulatory frameworks.

• Unique regulatory challenges: AI’s rapid evolution outpaces regulatory frameworks
• Need for effective communication: Companies must effectively communicate complex AI science to the FDA
• Strategies for navigating regulatory challenges: Early engagement with the FDA, clear documentation of AI functionalities and limitations

Downing stressed the importance of early engagement with the FDA and clear documentation of AI functionalities and limitations. “We used to live in a data-limited world. Now we live in a hypothesis-limited world.”
He also discussed practical strategies for navigating regulatory challenges, stressing the importance of early engagement with the FDA and clear documentation of AI functionalities and limitations. Privacy at a Crossroads
Senior Attorney Paul Rothermel explored the rapidly shifting patchwork of U.S. and state-level privacy laws that impact AI. Specifically, he addressed critical privacy and cybersecurity considerations, highlighting how emerging AI technologies intersect with privacy laws such as HIPAA, GDPR, and the California Consumer Privacy Act (CCPA).

• Critical privacy and cybersecurity considerations: AI’s use of large-scale data necessitates stringent privacy protections
• Intersections with privacy laws: AI technologies intersect with HIPAA, GDPR, and CCPA
• State-level developments in AI regulation: Legislation specific to generative AI

Rothermel examined how the use of large-scale data in AI necessitates stringent privacy protections to avoid discrimination, misuse, and potential bias in decision-making. “Many state privacy laws now contain provisions that effectively regulate AI systems through automated decision-making rules.”
Scope of Practice and AI’s Place in Clinical Decision-Making
Kelly Henderson and Dr. Brita Hansen next discussed practical considerations for implementing AI in clinical settings. They emphasized the importance of clearly defined oversight and evaluation frameworks within healthcare institutions.

• Clearly defined oversight and evaluation frameworks: Institutions need robust frameworks to ensure safe and effective AI use
• Data quality, model validation, and clinician training: Critical components of AI implementation
• Generative AI: Adds an extra layer of complexity to clinical workflows

Dr. Hansen detailed Allina Health’s structured approach, distinguishing between deterministic, predictive, and generative AI, explaining how each type requires distinct evaluation methodologies and governance. A Glimpse into the Future
In the closing panel led by Dr. Chris Tignanelli and moderated by Mitchell Hamline student Molly VandeVoort, Dr. Tignanelli focused on the practical implementation of AI solutions in clinical environments.

• Developing novel AI tools addressing specific clinical problems
• Ensuring AI systems are explainable, trustworthy, and transparent
• Establishing robust methods for monitoring and optimizing AI performance post-implementation

Dr. Tignanelli further reiterated the necessity of creating clear guidelines to determine when an AI system should be adjusted or discontinued due to performance concerns. “We don’t just develop AI tools—we monitor them. We’re asking questions like, ‘When should we turn this system off?’”
The event provided a comprehensive overview of AI in healthcare, highlighting the importance of careful consideration and planning in implementing AI solutions in clinical environments. With the rapid evolution of AI in healthcare, it is essential to address the unique regulatory, privacy, and operational challenges associated with its use. Conclusion
The successful co-hosting of the CLE event marked a significant step forward in promoting awareness and understanding of the complex issues surrounding AI in healthcare. As AI continues to transform the healthcare landscape, it is crucial to address these challenges and ensure that AI solutions are developed and implemented responsibly.