AI in Healthcare & Pharma Summit schedule

• Why most 2023–2024 pilots stalled at scale, and what separated the organisations that broke through
• The “clean sheet” mindset: asking whether a process should exist at all before applying AI
• Treating AI fluency as foundational infrastructure rather than a bolt-on capability
• What a credible, board-ready scaling roadmap looks like over the next 12–18 months

 Arta Subagiarta, Medical Lead, Hemophilia B - SANOFI  

• Where autonomous, think-plan-act agents are already delivering measurable time savings
• Embedding agents into documentation, prior authorisation, and revenue cycle operations
• Reclaiming clinician hours and easing the documentation burden behind burnout
• Oversight models that keep humans accountable for agent-driven decisions

• Why few AI tools are widely used by clinicians and staff after deployment
• Bringing AI guidance into the applications where work happens
• Creating comfort and confidence in AI tools so their benefits can be realized
• Showing leaders where adoption stalls, by role and by site

• Why plans firm up too late: country, site and enrollment decisions are usually settled after cost and value are already locked in.
• Testing eligibility before it's written in: an automated patient funnel shows what each criterion costs in reachable patients.
• From protocol to sites and enrollment: dose-escalation simulation for Phase 1, plus site ranking and bottom-up enrollment forecasts for late-phase studies.
• A plan ready at protocol final: enrollment drives resourcing and start-up, so by protocol final the operational decisions are already worked through.

• A practical, validation-first approach to selecting clinical AI tools
• Embedding LLM-powered tools directly and safely into EHR workflows
• Guardrails for hallucination, accuracy, and specialty coverage
• Evidencing real-world clinical performance, not benchmark scores

Anemone Kasasbeh,Data Science Manager– Ascension

Amir Tahmasebi, Head of AI Algorithms and Infrastructure – BD 

• Shifting from reactive treatment to anticipatory, data-driven care
• Identifying at-risk populations earlier across diverse communities
• Designing for equity so models serve underserved groups fairly
• Connecting predictive insight to action across provider and payer workflows

• Defining ROI measures for AI adoption: usage, proficiency, errors, and staff time returned
• Linking adoption data to outcomes such as claim accuracy, support volume, and workforce productivity
• How to differentiate tool performance challenges from workforce adoption challenges
• Sharing what works across sites, roles, and high-turnover teams

• Turning continuous patient data into meaningful clinical evidence.
• Where AI can uncover signals that scheduled trial visits miss.
• Tackling data quality, patient adherence and digital endpoint validation.
• What it takes to move wearable-enabled trials from promise to practice.

Tim Pagliaro, Co-Founder -Orchid Research Platform

Elias Abou Zeid, PhD, Principal Data Scientist, AI & Analytics – Novo Nordisk

• • Moving governance from a compliance checkbox to a genuine strategic enabler
  • Cutting through the noise: separating real capability from vendor hype
  • Explainability, bias auditing, and audit trails built into the model lifecycle
  • Aligning internal playbooks with evolving FDA, EMA, and EU AI Act expectations
    

  Manasi Ghogare, Senior Technical Product Manager, Digital Health – Takeda

  Bickkie Solomon, Director of Pharmacy; PGY-2 HSPAL Residency Program Director – HCA Florida North Florida Hospital

• Breaking silos and building cross-functional alignment at scale
• Embedding governance without slowing scientific progress
• Moving from efficiency gains to genuinely new decision-making capability
• Translating enterprise AI investment into measurable patient outcomes

Manohar Boorlu, Associate Director, AI Lead –Novartis

Adem Albayrak, Chief Data & AI Officer - Alzheon

Lily Xu, PhD, Director, Clinical Data Science – Vertex Pharmaceuticals

Marktus Atanga, Principal Advanced Analytics and Data Automation Engineer – Moderna

Brian Schick, Director, Digital Product Strategy & Development Lead – Takeda

• Quantifying and communicating AI’s return to the C-suite and board
• Building scalable infrastructure that meets regulatory expectations
• Navigating budget constraints and cross-functional resistance
• Defining the metrics that signal an AI programme is genuinely working

Parisa Farzam, Blavatnik Fellow in Healthcare and Life Sciences - Harvard Business School

 Vanessa Kitova, Staff Data Scientist - AI/ML, Enterprise AI -GE HealthCare  

• Why few AI tools are widely used by clinicians and staff after deployment
• Bringing AI guidance into the applications where work happens
• Creating comfort and confidence in AI tools so their benefits can be realized
• Showing leaders where adoption stalls, by role and by site

• Beyond the demo: what separates technical success from measurable patient value?
• Scale, improve or stop: designing AI pilots around clear decisions, budgets and accountability.
• Who pays, who leads, who owns the risk? Tackling the unanswered questions that stall adoption.
• Making AI work in clinical practice: aligning safety, sustainable funding and everyday workflows.

• Integrating patient-reported outcomes, wearable data and remote monitoring into clinical workflows to build a more complete picture of patients between visits.
• Using conversational AI, voice and SMS to collect patient updates, identify emerging concerns and support more personalised follow-ups.
• Turning continuous patient data into actionable insights, with clear escalation pathways that help care teams distinguish meaningful clinical signals from noise.
• Measuring whether AI-enabled patient engagement improves care navigation, early intervention and patient outcomes without increasing clinicians’ workload.

Erlyn Macarayan, PhD, Vice President of Data Science - PatientsLikeMe (Fuze Health)

• Translating experimental AI capability into a validated, supported business offering

• Aligning medical, regulatory, market access, commercial, and technology teams

• Building a credible value proposition for providers, payers, researchers, and patients

• Avoiding the transition gap between successful prototype and sustainable product

 Cheng Yang, Digital Diagnostics Lead - Tekeda  

• Deciding where to build proprietary AI capabilities, buy external solutions or partner for greater impact.
• Evaluating technology providers, startups and research partners beyond technical promises and vendor hype.
• Defining ownership, accountability and liability when AI systems influence critical decisions.
• Structuring partnerships that protect intellectual property, enable data sharing and distribute risk while delivering measurable outcomes.

Manohar Boorlu, Associate Director, AI Lead –Novartis

Amir Tahmasebi, Head of AI Algorithms and Infrastructure – BD

Jacey Zhang, Associate Director - AI & Digital Enablement Lead -Takeda

• Moving beyond broad principles to visible evidence of safety, fairness, and accountability

• Communicating how AI is used without overwhelming patients or frontline professionals

• Responding transparently when models underperform or produce unintended outcomes

• Making trust a measurable operational asset rather than a communications exercise

Marissa Seligman, Vice President, Clinical Data Optimization - Optum

Alyce Kuklinski, Vice President, Client Solutions- Optum

• Why fragmented EHR ecosystems remain the single biggest barrier to AI at scale
• Building the data foundations before scaling advanced AI use cases
• The CMS Interoperability & Prior Authorization rule as a 2026 compliance catalyst
• Standardising and governing data sharing across providers, payers, and pharma
  
  Marktus Atanga, Principal Advanced Analytics and Data Automation Engineer – Moderna