Program Overview
This program explores how AI and advanced data analytics are transforming research and development across the Life Sciences and MedTech ecosystem — from molecule discovery to clinical validation and regulatory submissions. Participants will learn how predictive modeling, real-world evidence, digital twins, and generative AI are enabling faster, smarter, and more cost-effective innovation while ensuring ethical and compliant practices. Through global case studies, simulations, and expert-led discussions, the session equips professionals with actionable strategies to integrate AI into their R&D workflows, improve decision-making, and strengthen collaboration between scientific, digital, and regulatory teams.
Features
- Understand how AI and data analytics are revolutionizing Life Sciences & MedTech R&D
- Apply predictive modeling, digital twins, and real-world data to accelerate discovery and trials
- Navigate ethical, quality, and regulatory requirements for AI-enabled R&D processes
- Design an actionable roadmap for AI integration in research, clinical, and product development environments
Target audiences
- R&D Professionals
- Data Scientists
- Clinical Researchers
- Regulatory and Quality leaders
- Digital Leaders
- MedTech Innovators
Curriculum
- 5 Sections
- 25 Lessons
- 1 Day
Expand all sectionsCollapse all sections
- The New Frontier — AI in Life Sciences & MedTech R&D5
- 1.1R&D pain points: high attrition, long cycles, fragmented data
- 1.2Core technologies: Machine Learning, Deep Learning, NLP, Computer Vision, Generative AI
- 1.3Key applications: target identification, molecule generation, toxicity prediction, patient stratification
- 1.4Evolution from descriptive to prescriptive & generative analytics
- 1.5DeepMind’s AlphaFold, Insilico Medicine, and Medtronic’s predictive maintenance AI
- Data-Driven Research — From Lab to Real-World Evidence5
- 2.1Data sources: omics data, EHR, real-world data (RWD), sensor & device data
- 2.2Data engineering & integration: FAIR data principles, semantic ontologies, interoperability (FHIR, CDISC)
- 2.3Building research data lakes and AI-ready datasets
- 2.4Predictive analytics and RWE (Real-World Evidence) for clinical decision-making
- 2.5Case study — AI in oncology trials and companion diagnostics.
- Regulatory, Ethical & Quality Considerations in AI-Driven R&D5
- 3.1Regulatory frameworks: FDA’s Good Machine Learning Practice (GMLP), EU MDR/IVDR, ISO 13485, EMA guidelines
- 3.2Data governance, model validation, explainability, and audit trails
- 3.3AI bias, ethics, and patient data privacy (GDPR, HIPAA, DPDPA)
- 3.4Role of Quality Management Systems (QMS) in AI lifecycle management
- 3.5Lessons from AI-driven SaMD (Software as a Medical Device) approvals
- Building an AI-Accelerated R&D Framework5
- 4.1Simulate an R&D pipeline using AI checkpoints
- 4.2Identify bottlenecks and use predictive insights to optimize decision gates
- 4.3Evaluate trade-offs between innovation speed, data integrity, and compliance
- 4.4Design an AI-enabled governance model for cross-functional R&D alignment
- 4.5AI-in-Action Activity: From Molecule to Market
- Future Outlook — From Smart Labs to Autonomous Discovery5
- 5.1AI Labs, Digital Twins, and Augmented Scientists
- 5.2Cloud-native R&D and collaboration with tech partners/startups
- 5.3Next-gen innovations: Quantum computing in drug design, federated learning in healthcare data
- 5.4Building the organizational readiness and talent pipeline for AI-powered R&D
- 5.5Reflection, discussion, and action planning for implementation within organizations
Instructor
