Why This Session Matters
Pharma teams often rebuild the evidence behind an asset for every new question. Study data, publications and previous decisions sit across systems and functions.
How can AI connect them into reliable decision support while preserving context, source traceability and expert oversight?
What You’ll Learn
In this session, we’ll explore how to
- Connect study data, publications and internal assessments to identify evidence gaps
- Reuse scientific rationale and market experience in launch preparation
- Bring historical trial knowledge into early feasibility discussions
- Define shared foundations for terminology, context and access across workflows
- Evaluate AI outputs for source traceability, relevance and expert review
- Scope a first use case around business value and organizational readiness
Who Should Attend
Designed for pharma leaders responsible for AI capabilities and the business workflows they support:
- CTOs, VPs and Heads of AI, Data and Digital
- AI platform, enterprise architecture and knowledge management leaders
- Medical Affairs and Evidence Generation leaders
- Market Access, HEOR and RWE teams
- Launch Excellence and asset program owners
- Clinical Development, Operations and Feasibility leaders
This session is especially relevant if your organization is asking:
- “How can we use what we already know about an asset across teams?”
- “Where could AI reduce repeated evidence gathering and review?”
- “Which capabilities should we share across use cases and functions?”
- “How do we preserve source traceability, permissions and decision context?”
- “What should we validate before investing in a broader AI rollout?”
Meet the Speakers
Michel Van Speybroeck
Founder, MedSphere 42
Former Head of Data Science EMEA, Johnson & Johnson
Michel led Johnson & Johnson’s EMEA data science team, delivering capabilities across R&D, Medical Affairs, Market Access and commercial functions. His experience includes European health data initiatives such as EHDEN, EMIF and HARMONY, connecting fragmented data for research and evidence generation. At MedSphere 42, he focuses on federated data networks and applying AI to organizational knowledge, directly relevant to this session’s discussion of evidence reuse and launch readiness.
Szymon Komorowski
Life Sciences & Healthcare Advisor, deepsense.ai
Szymon Komorowski is Life Sciences & Healthcare Advisor at deepsense.ai. He brings 17 years of experience across pharma and life sciences, including leadership roles at IQVIA, Deloitte, Delta Pharma Adria, and SoftForYou, spanning pharma strategy, technology deployment, commercial analytics, digital HCP engagement, patient pathways, and healthcare policy. At deepsense.ai he connects the company’s AI engineering expertise with real-world healthcare and pharma challenges, including work with partners such as Anthropic, Google, OpenAI, and AWS.
Pharma AI Exchange: Executive Conversations on AI in Practice
A series by deepsense.ai
Pharma AI Exchange brings industry leaders and applied AI practitioners together to discuss concrete use cases, implementation experience and the decisions behind adoption.
Join to inform your next AI investment, learn from peers’ implementation choices and assess what could work in your organization, with practical attention to reliability, governance and measurable outcomes.
Explore the series: topics and approach
The series explores five connected areas:
- Clinical Development & Operations
- Enterprise AI Platforms
- Market Access & Evidence
- Medical Affairs & Content Operations
- R&D & Drug Discovery
Topics range from identifying feasibility risks before protocol lock to supporting evidence synthesis, medical content review, and secure access to organizational knowledge.
Each conversation examines the business problem, the data and systems involved, and what it takes to make AI useful in practice, including where human judgment remains essential, how to evaluate quality and value, and which capabilities can be reused across functions.
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