How AI Is Transforming Clinical Trials and Data Access - with Mathew Paruthickal of Sanofi
🎯 Summary
Podcast Episode Summary: How AI Is Transforming Clinical Trials and Data Access - with Mathew Paruthickal of Sanofi
This 20-minute episode features Mathew Paruthickal, Global Head of Data Architecture, Utilization in AI Engineering at Sanofi, discussing the strategic integration of AI and advanced data workflows to revolutionize clinical trials. The core narrative moves from simple digitization efforts to a deep focus on creating intelligent, connected data ecosystems that drive proactive decision-making while strictly adhering to regulatory and patient safety mandates.
1. Focus Area
The primary focus is the transformative role of AI and advanced data architecture in clinical trials. Specific applications discussed include optimizing protocol design, predicting site risks, surfacing early safety signals, and automating regulatory documentation. The conversation heavily emphasizes shifting AI from a “side project” to a strategic organizational capability built upon interoperable data systems.
2. Key Technical Insights
- Three-Pillar Data Architecture: Sanofi is structuring its AI foundation around three key vertical archetypes: 1) Building a modern Data Lakehouse/Fabric for structured data; 2) Implementing Document Intelligence (NLP/GenAI) to extract insights from unstructured documents (PDFs, safety reports); and 3) Ensuring Interoperability between these two intelligence layers.
- Modern Context Protocols (MCPs): The team is developing proprietary pipelines and tools (akin to MCPs) to process massive volumes of diverse data (clinical, manufacturing, audit reports) and ensure all data, even structured, is contextually connected.
- Agentic Capabilities: The goal is to build live agents on top of this connected intelligence that can perform complex tasks like summarizing information, auto-generating clinical study reports and protocols, and creating safety chatbots, all grounded in truth (compliance/governance).
3. Business/Investment Angle
- Strategic Capability over Side Project: The biggest mistake is treating AI as isolated tools; true impact requires embedding it as a core strategic capability across the organization.
- Focus on Business Outcomes: AI implementation must be tied directly to measurable business results, such as fewer protocol amendments, faster safety reviews, and better audit preparation. An example cited is drastically reducing the three-month lead time for regulatory review of marketing materials.
- Accessibility and Embedding: For scalability, AI must be accessible by embedding intelligence directly into the interfaces and tools that teams (science, regulatory, legal) are already using daily, rather than forcing them to learn new data science platforms.
4. Notable Companies/People
- Mathew Paruthickal (Sanofi): Global Head of Data Architecture, Utilization in AI Engineering, providing the strategic and technical roadmap for AI integration in clinical operations.
- Sanofi: Highlighted as a leader moving beyond basic digitization toward intelligent, proactive clinical trial management.
- Metable: Sponsor of the special podcast series on AI and clinical workflows.
5. Future Implications
The industry is moving toward a proactive state, where systems automatically flag signals, generate narratives, and prepare necessary documentation before an event or audit occurs. Protocols could “write themselves” based on prior trial data, current patient populations, and real-world outcomes. While AI will automate insights and reduce compliance risk, the human element—especially regarding empathy and complex decision-making related to patient outcomes—will remain firmly in the domain of human experts.
6. Target Audience
This episode is highly valuable for Life Sciences Leaders, Clinical Operations Executives, Data Science/Architecture Professionals in Pharma, and Regulatory/Compliance Officers who are responsible for scaling AI initiatives and ensuring data governance within highly regulated environments.
🏢 Companies Mentioned
💬 Key Insights
"in clinical, you know, the stakes include patient lives and global regulatory scrutiny. So compliance, safety, traceability, these are not optional, you know, they are the baseline and that's exactly what we are looking for."
"tying AI to a real business outcomes, you know, it's not just a cool demo thing that we want to do. We're talking about like, you know, a business outcome as in like fewer protocol amendments, faster safety reviews, better audit prep."
"We're building model governance, washing control, security, compliance, all from day one, you know, already to go from day one."
"accessibility is everything, like it only if it's known in the hands of data scientists, we've kind of failed. So it needs to be embedded in the tool that they're already using today."
"the biggest mistake we're seeing in AI systems across not just some of even many starters, we treat everything like a side project."
"We're just giving them intelligent tools that surface insights faster, you know, reduce the compliance risk and let them focus on the decisions that matter most, which is patient safety."