Healthcare & Life Sciences AI Summit

AI is moving rapidly from experimentation to enterprise-scale deployment, reshaping how healthcare and life sciences organizations conduct research, deliver care and run their businesses. This opening session examines where AI is delivering measurable value today, what organizations are learning as they scale, and the governance, infrastructure and workforce capabilities needed to prepare for the next generation of AI.

• Moving from isolated AI pilots to enterprise-scale deployment across clinical, operational, and research functions
• Exploring which AI use cases are delivering measurable ROI today, from ambient documentation and revenue cycle optimization to drug discovery and clinical development
• Adopting the governance, interoperability, cybersecurity, and workforce capabilities required to scale AI safely and sustainably across the enterprise
• Ramping up for what’s next as healthcare and life sciences organizations prepare for agentic AI, autonomous workflows, and the next generation of intelligent enterprise operations

There is no single rulebook governing AI across healthcare and life sciences. As federal agencies apply existing authorities and states move ahead with their own requirements, this session will map who regulates what, where requirements are diverging, and how organizations can build AI programs that remain scalable as the U.S. regulatory landscape evolves.

• Navigating the healthcare AI regulatory landscape spanning FDA, HHS, CMS, FTC, and rapidly evolving state laws
• Exploring how reimbursement, privacy, safety, consumer protection, and medical device regulation intersect to shape AI deployment and adoption
• Strategizing to navigate conflicting federal and state requirements while building scalable AI governance programs
• Preparing for the next wave of healthcare AI oversight by understanding where regulators are focusing enforcement, accountability, transparency, and patient protection efforts

Agentic AI raises the stakes by moving beyond systems that generate recommendations to systems capable of initiating actions and executing multi-step workflows with increasing autonomy. This session examines where agentic AI is beginning to emerge across healthcare and life sciences, the new risks created when humans are no longer involved in every decision, and the guardrails needed before organizations allow these systems to act at scale.

• Evaluating the compliance, regulatory, quality and risk implication of agentic workflows
• Defining guardrails, human-in-the-loop requirements and boundaries on autonomous action in clinical and regulated environments
• Implementing frameworks for monitoring, controlling, and validating autonomous AI workflows at scale
• Determining practical use cases for agentic AI across clinical development, regulatory affairs, quality, and healthcare operations, along with the opportunities and challenges they present

As AI spreads across the enterprise, responsibility can no longer sit with a single legal, compliance or technology function. This session examines how healthcare and life sciences organizations are structuring AI governance, assigning decision rights and creating clear lines of accountability so innovation can move forward without creating gaps in oversight.

• Benchmarking best practices for structuring enterprise AI governance to balance innovation, accountability, and compliance
• Creating clear ownership, decision rights, stakeholder communications and escalation paths across Legal, Compliance, Technology, Clinical Operations, Data, and Business leadership
• Deploying governance models ranging from Chief AI Officers and AI governance boards to federated operating structures with centralized guardrails
• Implementing a practical framework for managing AI risk, monitoring performance, and demonstrating accountability to regulators, patients, customers, and boards
• Establishing reporting, escalation and accountability mechanisms that give senior leadership and boards meaningful visibility into enterprise AI risk

• Leveraging where AI delivers the greatest impact from target identification and molecule discovery to clinical development and regulatory submissions
• Balancing embedding AI into existing drug development workflows versus standalone AI applications creating new efficiencies and capabilities
• Navigating broader AI adoption barriers such as data quality, validation requirements, governance challenges, regulatory uncertainty and organizational readiness
• Adhering to emerging FDA expectations for AI-generated insights and models, including considerations for validation, documentation, and regulatory review
• Maximizing AI investments while balancing innovation, compliance, and scientific rigor across the drug development process

• Examining how liability may be allocated among clinicians, health systems, and AI vendors when AI-informed decisions are challenged, and how allegations of a breached standard of care may be evaluated
• Understanding how courts and regulators evaluate physician judgment when AI recommendations conflict with clinical expertise, including the discovery and litigation implications of AI-generated outputs, documentation, audit trails, model versioning, and clinicians’ acceptance or rejection of AI recommendations
• Analyzing organizational risk arising from AI adoption, including responsibilities related to tool selection, configuration, validation, governance, training, oversight, and enterprise-wide deployment
• Identifying practical risk management strategies to clarify accountability, demonstrate appropriate oversight, support clinical decision-making, and reduce malpractice and liability exposure in an AI-enabled care environment

• Exploring how AI is accelerating drug discovery by predicting molecular interactions, protein structures, and antibody-antigen binding, enabling researchers to identify promising candidates faster and reduce costly trial-and-error experimentation
• Exploring how AI-powered modeling, synthetic data, and digital twins are generating predictive insights before clinical testing begins, helping teams optimize development strategies and make more informed decisions earlier in the lifecycle
• Understanding how regulators are evaluating AI-generated evidence and what organizations must demonstrate around transparency, validation, reliability, and scientific rigor to support regulatory decision-making
• Examining how AI is transforming clinical trial design and execution by improving patient recruitment, optimizing site selection, and accelerating enrollment to bring therapies to market faster

• Navigating how CMS, CPT coding, and emerging reimbursement pathways are shaping the commercial future of AI-enabled healthcare technologies
• Knowing what evidence payers, providers, and health systems require to support coverage, reimbursement, and large-scale adoption
• Implementing strategies for generating clinical, economic, and real-world evidence that demonstrate measurable value and accelerate market access
• Sharing insights on successfully moving AI solutions from FDA authorization and pilot programs to sustainable reimbursement and ROI

• Assessing ownership and IP risks associated with AI training data, third-party datasets, models and AI-generated outputs
• Navigating inventorship and patentability when AI contributes to identifying or developing new drugs, biologics, devices and other innovations
• Documenting meaningful human contributions to AI-assisted R&D to strengthen patent positions and support defensible claims of inventorship
• Evaluating freedom-to-operate, trade secret and competitive risks when incorporating third-party AI models, tools

• Exploring how patient-facing AI tools, including ChatGPT, Microsoft Copilot, Claude, and healthcare-specific chatbots, are reshaping access to care, patient engagement, and self-service health support
• Reviewing FDA oversight, evolving regulatory frameworks, and emerging expectations for deploying patient-facing AI responsibly
• Navigating privacy, consent, transparency, and liability considerations when AI systems influence healthcare decisions.
• Evaluating the potential for patient-facing AI to reduce administrative burden, alleviate clinician burnout, and improve access through faster, more convenient interactions

An AI model begins producing inaccurate outputs, patient risk emerges, and no one is immediately sure who owns the response. In this interactive session, attendees will work through a rapidly evolving AI incident, confronting the real-time decisions around containment, escalation, investigation, vendor involvement and stakeholder communication that healthcare and life sciences organizations need to be prepared to make.

• Identifying the warning signs that an AI system is failing and determining when an issue requires escalation
• Deciding when to restrict, suspend or shut down an AI system while investigating inaccurate, biased or unsafe outputs
• Coordinating the response across clinical, legal, compliance, technology, cybersecurity and vendor teams
• Working through evolving facts and determining what to communicate, document and remediate as the incident unfolds