AI in Healthcare & Pharma Summit schedule

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. 

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? 

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? 

Explore how a modular, privacy-first AI framework can close the post-approval commercialization gap and speed equitable patient access. 

• Bullet Point: 

• 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. 

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? 

Panellists:  

Jerry Calvanese, Senior Director, Technical Sales,INFORMATICA 

Tina S. Lai, Public Health Bioinformatician, NYC - DEPT OF HEALTH AND MENTAL HYGIENE 

Moderator: Ellis Wong, Chief Information Security Officer, JST CAPITAL 

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 

Panellist: Manasi Ghogare, Senior Technical Product Manager, TAKEDA 

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? 

As artificial intelligence becomes integral to the life sciences, the true differentiator for clinical organizations is no longer access to technology but the ability to develop AI fluency—the competence to apply, evaluate, and govern AI responsibly. 

This presentation will share practical insights from the Clinical AI Innovators Network (CAIIN), a multidisciplinary forum of professionals learning and experimenting with AI in real clinical and operational contexts. Attendees will gain a firsthand view of how structured experimentation, role-based AI coaches, and agentic automation prototypes are reshaping monitoring oversight, onboarding, and operational training. 

Beyond tools and workflows, the session explores the human side of transformation: how curiosity, trust, and humor drive adoption and cultural change. The discussion introduces a scalable AI fluency framework built on four pillars—exposure, experimentation, enablement, and ethics—offering a roadmap for embedding AI literacy across functions. 

Participants will leave with actionable strategies and replicable practices for building AI-ready teams, fostering innovation safely, and transforming clinical operations into learning organizations prepared for the next era of digital leadership. 

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? 

Moderator: Rahul Kashyap, Medical Director Research, WELLSPAN HEALTH 

Panelists:

Ellie Norris, Director, D&A Strategy and Technology Partnership/ MRL IT Clinical & Real-World Evidence Generation, MERCK

Ravi Kiran Koppichetti, IT Data Engineer III, NOVO NORDISK  

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.

By uniting governed automation with human expertise, Intelligent Data Automation empowers organizations to close the gap between data and decision — enabling a new generation of AI-ready healthcare enterprises that learn, adapt, and act with confidence.

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 

• 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 

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 

Moderator: Tina S. Lai, Public Health Bioinformatician, (NYC - DEPT OF HEALTH AND MENTAL HYGIENE) 

Panellists:

Matt Dixon, Cloud Architect, NORTHWELL HEALTH 

Ramya Palacholla, Director Digital Science, EVINOVA (Healthtech Company of AstraZeneca)

Arta Subagiarta, Associate Medical Director, SANOFI 

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?