AI in Healthcare & Pharma Summit schedule

As GenAI tools gain traction, health systems are rethinking how AI can support clinicians, not replace them. 

• How is GenAI being used to reduce administrative burden in clinical workflows? 

• What are the best practices for deploying AI-driven CDS tools at scale? 

• How can we ensure accuracy, trust, and integration across clinical settings? 

• What are the early measurable impacts on clinician time and patient care? 

Turning real-world data into real-world impact requires breaking down institutional and regulatory barriers. 

• What are the critical enablers of cross-sector RWD collaboration? 

• How are organizations aligning governance, privacy, and data access strategies? 

• What shared use cases are driving value across payers, providers, and pharma? 

• How can we standardize and scale collaborative data ecosystems? 

Deploying AI in regulated environments requires rigorous validation and auditability. 

• What does a regulatory-grade AI lifecycle look like in practice? 

• How are leading teams addressing traceability, bias, and explainability? 

• What frameworks support FDA, EMA, and EU AI Act compliance? 

• How can we balance speed of innovation with operational risk mitigation? 

AI is driving the next generation of telehealth—more intelligent, efficient, and inclusive. 

• How is AI being integrated into telehealth triage and decision-making? 

• What gains have been achieved in access and patient stratification? 

• How are escalation protocols and safety nets embedded into AI design? 

• What are the lessons in serving rural, elderly, and high-risk populations?

GenAI offers promise in mental health support—but with sensitive ethical implications. 

• Where is GenAI currently being used in mental health triage or companionship? 

• What safeguards are essential for managing hallucination and misuse? 

• How can GenAI improve access without displacing human care? 

• What boundaries should guide deployment in emotionally vulnerable contexts? 

Join us for the Innovation Showcase, featuring two fast-paced 10-minute presentations that highlight groundbreaking AI applications transforming healthcare and pharma.  

The compliance landscape is evolving fast—particularly for AI in early-stage and clinical research. 

• What are the latest regulatory shifts impacting AI in drug development? 

• How are teams preparing for audits and ensuring model transparency? 

• What documentation and oversight mechanisms are needed? 

• How does the EU AI Act compare to U.S. and global frameworks? 

Pharma R&D teams are experimenting with GenAI to unlock novel targets—but what actually works? 

• What are successful approaches to GenAI for target prediction? 

• Where do these tools outperform traditional methods—or fail? 

• How are teams validating GenAI hypotheses with human-in-the-loop feedback? 

• What infrastructure is needed to scale GenAI across discovery teams? 

Join us for the Innovation Showcase, featuring two fast-paced 10-minute presentations that highlight groundbreaking AI applications transforming healthcare and pharma. 

Payers are deploying AI to personalize coverage decisions and reduce systemic bias. 

• How are AI models being used to support equitable policy and pricing? 

• What are the risks around data bias in underwriting or claims adjudication? 

• How do payers balance automation with regulatory oversight? 

• What opportunities exist for payer-provider collaboration? 

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? 

Moderator: Matt Dixon, Cloud Architect, NORTHWELL HEALTH 

Hospitals are embracing GenAI—but success depends on precision, not promise. 

• How are health systems managing hallucination and overfitting? 

• What integrations with EHRs and clinical workflows are most effective? 

• How are teams measuring usability and clinical trust? 

• What guardrails ensure GenAI improves—not complicates—care? 

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? 

AI is helping align discovery outputs with patient segmentation, pricing, and launch planning. 

• How is AI improving market access predictions and planning? 

• What RWE signals matter most for launch success? 

• How are teams using AI to optimize trial-to-launch transitions? 

• Where are the pitfalls in aligning clinical and commercial data? 

AI is making pharma manufacturing more predictive, responsive, and sustainable. 

• What use cases are succeeding in quality control and batch prediction? 

• How are digital twins being applied in production? 

• What are the measurable gains in cost, waste, and time? 

• How is AI being validated in GMP environments? 

Precision medicine is being redefined through AI-enabled platforms and patient models. 

• What breakthroughs are we seeing in predictive patient modeling? 

• How are digital twins guiding adaptive treatment plans? 

• What data infrastructure is needed to support this shift? 

• How close are we to routine deployment in real-world care? 

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? 

Equity must be built into AI—not bolted on after deployment. 

• What strategies ensure datasets reflect diverse populations? 

• How are bias audits being standardized and enforced? 

• How can equity align with ROI and regulatory readiness? 

• What lessons from failed deployments can guide inclusive design? 

• How are pharma and providers aligning on outcomes-based agreements using RWE? 

• What infrastructure is required to collect and analyze RWE at scale? 

• What barriers still hinder cross-industry data collaboration? 

Interoperability remains a critical bottleneck in realizing the full potential of AI in healthcare. This session delves into practical strategies for unifying disparate data systems.  

• Frameworks for connecting EHRs with real-world datasets and research platforms 

• How to future-proof your AI systems through modular, interoperable architecture 

• Use cases showing tangible clinical and operational impact 

As organizations move beyond experimentation, scaling AI responsibly requires strategic alignment across the C-suite.  

• How to quantify and communicate clinical ROI to leadership 

• Building scalable infrastructure that meets regulatory expectations 

• Strategies for navigating budget constraints and cross-functional resistance 

This talk examines what it means to build equitable AI from the ground up—and the risks of failing to do so.  

• How to detect and correct bias in AI training data? 

• Incorporating social determinants of health into model design 

• What fairness means in the context of clinical decision support tools 

• Learn governance structures for global AI operations 

• How to balance innovation with risk and compliance?  

• What worked—and what didn’t—during AstraZeneca’s enterprise rollout.  

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? 

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?