ON-DEMAND
Presentations
Day 1
From Promise to Practice – Overcoming Barriers to AI Adoption in Healthcare
This talk will trace how long-standing, human-centered barriers to innovation adoption—leadership, culture, and self-efficacy—persist today, and identify the unique challenges of constantly evolving, semi-autonomous AI systems demands new approaches to governance, evaluation, and workforce readiness.
- Consider lessons from public-health and behavioral-science implementation science: how leadership alignment, staff confidence, and workflow fit have always driven or derailed adoption.
- Suggest that these “human” determinants remain under-addressed even in today’s AI pilots, causing the majority to stall before scale.
- Discuss the new reality of perpetual adoption: how adaptive and agentic AI systems evolve faster than organizations can “adopt,” introducing governance, trust, and accountability challenges that static frameworks don’t capture.
- Argue that the path forward requires “continuous implementation science” integrating a new kind of organizational readiness involving model governance, dynamic user training, and agile feedback loops so institutions can co-evolve with intelligent systems.
Speaker:
Linda Hermer, Chief Data Strategy Officer, AMMON LABS
Building the AI Stack in Pharma – Unifying Data, Deployment, and Model Governance
Enterprise-scale AI requires infrastructure that scales with science, not against it.
- What defines a modern, production-ready AI stack in pharma?
- How are data engineering, MLOps, and governance being unified?
- What trade-offs exist between modular tools and full-stack platforms?
- How are teams aligning across R&D, regulatory, and commercial?
Ittai Dayan, MD, CEO, RHINO FEDERATED COMPUTING
AI-Enabled Commercialization: Building a Scalable Framework for Accelerating Drug Access
Explore how a modular, privacy-first AI framework can close the post-approval commercialization gap and speed equitable patient access.- Identify key post-approval bottlenecks that delay uptake of new therapies.
- Show high-impact AI use cases across clinical engagement, patient analytics, and operations.
- Present a scalable, interoperable framework that balances explainability and data privacy.
- Discuss how AI-driven commercialization supports national goals for access and health resilience.
Speaker:
Shihan He, Machine Learning Engineer, NOVO NORDISK
PANEL: Building Equitable and Explainable AI Systems in Healthcare
This panel explores how equitable and explainable AI systems can be responsibly designed and deployed across healthcare organizations.- How AI models can support fairness, transparency, and compliance in healthcare decision-making
- Identifying and mitigating data bias in clinical and population-level AI applications
- Balancing automation with ethical and human oversight in regulated environments
- Opportunities for cross-sector collaboration to scale responsible AI
FIRESIDE CHAT: Crafting Personalized Patient Access Pathways with AI
Personalized care access is becoming table stakes—and AI is the engine behind it.
- How are AI tools optimizing prior auth, eligibility, and intake?
- What’s the balance between automation and empathetic patient support?
- What systems are enabling real-time decisions and navigation?
- How do you maintain equity when customizing access?
Operationalizing Safe & Compliant AI: From Validation to Vigilance
Deploying AI in regulated environments requires rigorous validation and auditability.
Operationalizing Safe & Compliant AI: From Validation to Vigilance
Deploying AI in regulated environments requires rigorous validation and auditability.
PANEL: Operationalizing Responsible & Explainable AI in Regulated Healthcare
Responsible AI is no longer abstract—it’s an operational requirement.
- How are teams embedding governance into data and model workflows?
- What tools are enabling real-time monitoring and oversight?
- How is responsible AI aligned with global regulatory trends?
- What does accountability look like in high-risk use cases?
Track A: Clinical Innovation, Access & Outcomes
(VIRTUAL) CASE STUDY: NLP + GenAI in the EHR – Cutting Through the Noise
Hospitals are embracing GenAI—but success depends on precision, not promise.
Track B: Accelerating Drug Development & Personalized Medicine
Day 2
Healthcare and life sciences organizations face an urgent challenge: transforming massive, complex data sets into timely, trustworthy insights that improve care, accelerate research, and streamline operations. Yet data remains trapped in silos, and manual, fragmented processes slow the pace of innovation.
Intelligent Data Automation offers a new path forward — integrating data preparation, context, governance, and AI into a single, adaptive framework that transforms raw information into intelligent, repeatable processes.
With a low-code, no-code approach, healthcare teams across disciplines can automate workflows, ensure compliance, and generate explainable insights at scale — without relying solely on technical specialists. The result is analytics that are faster, more transparent, and inherently more trustworthy.
- Key challenges in scaling AI across pharma, including silos, complexity, and organizational readiness
- Practical strategies for embedding AI across pharma ecosystem through cross-functional alignment and governance
- Opportunities for AI to drive innovation, improve decision-making, and enhance patient outcomes
Download PDF Presentation
Transforming AI from concept to clinical reality demands more than just technical tools—it requires the right people and mindset.
- Lessons on upskilling clinical and operational staff for AI adoption.
- Discover how to create a culture of innovation and experimentation.
- How to embed AI fluency across diverse healthcare roles?
PANEL: Beyond Pilots – Scaling AI in Healthcare Without Burnout: Sustaining Trust and Momentum
Many healthcare AI programs stall after initial pilots. This panel explores how to maintain momentum without overwhelming teams.
- Best practices for integrating AI into clinical workflows
- Managing cognitive load and alert fatigue in clinicians
- Organizational change management for AI scale-up
A future-focused conversation on what’s real, what’s hype, and what’s worth preparing for now.
- Are multimodal models and digital twins delivering real value today?
- What are the infrastructure and ethical considerations to scale them?
- How should healthcare leaders prioritize AI R&D over the next 3 years?