In Silico Evidence and the Future of Clinical Evaluation

In silico evidence is moving from theory into regulatory reality — but what does it actually mean for clinical evaluation, clinical investigations, and the future of medical device evidence? In this Friday In-Focus session, Surash Surash (Clinical Reviewer, TÜV SÜD) explains how computer-based modeling and simulation can support medical device development across the lifecycle — from early prototyping and premarket evidence generation to post-market surveillance and rare-event analysis. You will learn: • What in silico testing actually means and how it differs from AI, synthetic data, and digital twins • How FDA, EU MDR, ISO groups, and international working groups are approaching in silico evidence • Where in silico evidence can reduce patient risk, animal testing, trial burden, and development timelines • Why validation against real-world clinical data remains essential • How simulation can support high-risk populations, orphan devices, and hard-to-enroll studies • The biggest barriers: cost, expertise, validation complexity, diversity, and regulatory acceptance • Why in silico evidence is an augmentation tool today — not a full replacement for clinical data Speaker: • Surash Surash — Clinical Reviewer, TÜV SÜD Replay and chapters below. 00:05 – Opening question: career dreams and early path into neurosurgery 01:47 – Career shift: leaving neurosurgery and moving toward regulatory impact 03:09 – Combining clinical expertise, medical law, and regulatory affairs 04:31 – Global regulatory work and the opportunity to impact more patients 06:04 – What in silico testing means in plain language 07:44 – Regulatory landscape: FDA leadership, EU MDR evolution, and global harmonization 10:40 – Key players advancing in silico evidence: Avicenna Alliance, MHRA-linked groups, academia 13:29 – Market adoption: early stage, growing investment, and the road ahead 15:45 – White paper and deeper learning resources 17:41 – Clinical impact: reducing patient exposure in high-risk trials 20:25 – Lifecycle use: prototyping, premarket evidence, and post-market surveillance 24:37 – Why real-world data is essential for validation 26:04 – Verification, validation, and why in silico is augmentation, not replacement 33:01 – Barriers for small manufacturers: cost, software, hardware, and expertise 34:29 – Academic partnerships as a practical path for SMEs 38:21 – Population diversity, bias, and modeling global patient cohorts 42:17 – Common myths: what in silico can and cannot do today 46:42 – Validation complexity and lessons from aerospace and engineering 50:28 – Usability testing: what simulations cannot replace 51:45 – Enrollment challenges and how simulation can supplement limited patient data 54:50 – Animal testing limitations and the ethical case for in silico methods 58:03 – Future outlook, collaboration, and closing thoughts