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

Cheng Yang, Digital Diagnostics Lead - Tekeda

• 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 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

• 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

• Using machine learning to separate urgent signals from clinical noise
• Reducing cognitive overload and improving situational awareness at the bedside
• Lessons from ICU decision support and sepsis surveillance deployments
• Measuring clinical trust and adoption beyond the proof-of-concept

• 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, Lead Data Scientist – IPG Health

• 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

 Anemone Kasasbeh, Lead Data Scientist – IPG Health  

• Planning, executing, and monitoring trials with less linear human intervention
• AI-driven protocol simulation and real-time enrolment optimisation
• Realistic gains in study startup timelines and database lock
• What operational readiness for autonomous trial ops actually requires

• Where virtual control arms are delivering credible efficiency gains today
• High-data therapeutic areas where twins work, and where they don’t
• How evolving regulatory guidance is shaping confidence in adoption
• Pairing operator experience with technology for a commercial-free view

• 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

• Why most AI programmes stall on people problems, not product problems
• Managing cognitive load, alert fatigue, and change resistance
• Building AI fluency across clinical, operational, and research roles
• Sustaining trust and momentum after the initial pilot wins

• The emergence of AI that reasons about the physical world the way LLMs reason about language
• Implications for preclinical molecule design and drug delivery
• Manufacturing yield and materials science as new AI frontiers
• Positioning R&D strategy ahead of an accelerating curve

• Optimising prior authorisation, eligibility, and intake with AI
• Balancing automation with empathetic, human patient support
• Enabling real-time decision-making and navigation across care pathways
• Maintaining equity while customising access at scale

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. 

• 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

• Separating what’s real, what’s hype, and what’s worth preparing for now
• Whether multimodal models and digital twins deliver value today
• Infrastructure and ethical considerations for scaling them responsibly
• How leaders should prioritise AI R&D over the next three years

Why model deployment is the beginning of the risk cycle, not the end

• Detecting drift, performance degradation, and unintended consequences in live environments

• Establishing ownership across data science, clinical, regulatory, technology, and business teams

• Designing monitoring systems that protect outcomes without slowing innovation

How AI is changing the division of work across clinicians, researchers, operations, and commercial teams

• Deciding which activities should be automated, augmented, or kept fully human

• Reskilling experienced teams without creating parallel AI organisations

• Building clear accountability when decisions are shared between people and intelligent systems

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

Bringing model performance, data quality, bias, and operational risk into one monitoring layer

• Identifying silent failures across changing populations, workflows, and clinical settings

• Creating escalation thresholds and evidence trails for high-risk AI systems

• Turning observability into an operational capability rather than a technical dashboard

• Understanding new attack surfaces created by third-party models, APIs, and training data

• Protecting sensitive health and research data from leakage, manipulation, and misuse

• Assessing vendor security without relying solely on contractual assurances

• Building resilience against model poisoning, prompt injection, and adversarial behaviour

• • Identifying where AI can remove administrative friction without fragmenting care

  • Redesigning workflows so insight arrives at the right moment rather than creating another alert

  • Preserving professional judgement as recommendations become more automated

  • Measuring impact through capacity, care quality, staff experience, and patient trust

• • Integrating remote monitoring, wearable, and patient-reported data into clinical workflows

  • Separating meaningful signals from continuous streams of low-value information

  • Designing escalation pathways that lead to timely human intervention

  • Demonstrating improved outcomes without increasing pressure on clinical teams

• Clarifying responsibility between providers, vendors, clinicians, and technology partners

• Managing liability when models influence—but do not independently make—decisions

• Establishing processes for incident response, remediation, and transparent communication

• Building partnership agreements that remain workable as technology and regulation evolve

Integrating regulatory strategy into AI development from the earliest design stages

• Defining validation standards for models that continue learning or changing after deployment

• Creating traceable evidence across data provenance, performance, and human oversight

• Preparing globally scalable approaches across FDA, EMA, and emerging AI frameworks

Integrating regulatory strategy into AI development from the earliest design stages

• Defining validation standards for models that continue learning or changing after deployment

• Creating traceable evidence across data provenance, performance, and human oversight

• Preparing globally scalable approaches across FDA, EMA, and emerging AI frameworks

• Choosing where proprietary capability creates genuine strategic advantage

• Evaluating startups, technology providers, research institutions, and platform partners

• Structuring partnerships around shared outcomes rather than isolated pilots

• Protecting intellectual property while enabling data and capability exchange

Manohar Boorlu, Associate Director, AI Lead -Novartis

• 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

The 2030 AI Operating Model — What Leaders Must Build Next

• Which capabilities healthcare and pharma organisations should own internally

• How operating models must evolve as AI becomes embedded across every function

• Preparing for increasingly autonomous systems without losing strategic or human control

• The decisions leaders must make now to create durable advantage over the next five years