Clinical Trial Technology Editorial

  1. Is It Time To Replace RECIST — Or Just Add AI? 4/16/2026

    Immunocore Chief Regulatory and Quality Officer Mark Moyer explains why new tools, including AI-based approaches, may better capture cancer treatment response than existing measures, such as ir-RECIST.

  2. RBQM And Centralized Monitoring Need Action 4/10/2026

    After COVID forced sponsors to move faster than ever, many are now confronting a harder challenge: ownership. Marci Thear explains why RBQM and centralized monitoring only work when sponsors move beyond reviewing outputs and start acting on the signals.

  3. RWE Is Ready — Decision Making For Pharmaceuticals Isn't 4/1/2026

    Gorana Capkun of Merck, KGaA, Darmstadt, Germany discusses the growing role of real-world evidence in clinical trials, highlighting a gap between regulatory momentum and pharma adoption— and the challenge of turning data into better decisions.

  4. Building A Future-Proof, GxP-Compliant IT Infrastructure 3/31/2026

    Learn how a structured IT evaluation framework can help companies model total compliance costs, request vendor qualification evidence, and embed governance requirements into infrastructure selection.

  5. Unlocking Biopharma Innovation With Real-World Evidence 3/30/2026

    Discover how how federated data networks, platform aggregators, and collaborative partnerships have evolved to meet the data demands of modern product development, regulatory strategy, and commercial success.

  6. Bayesian Digital Twins Show Potential For Predicting Prognosis And Treatment Response 3/30/2026

    Learn how Bayesian digital twins are trained, validated, and trusted in clinical settings and explore the potential for integrating these models into interventional trials.

  7. Elsa's AI Model Migration: Technical, Compliance, And Regulatory Risks For Sponsors (Part 2) 3/26/2026

    In the second part of their series on Elsa AI model migration, Kimberly Chew, Esq., and Michael Yang, Esq., analyze the risks to compliance and data residency, as well as the integrity of the regulatory record.

  8. A Clinical Machine Learning Operations (MLOps) Maturity Framework For Biopharma 3/24/2026

    Pharma has invested substantially in machine learning applications, but investment in the operational infrastructure — the MLOps layer — has lagged. The time to build that infrastructure is now — not after your next trial fails.

  9. How Deep Learning Is Changing Clinical Trial Design, Execution, And Analysis In 2026 3/10/2026

    Deep learning is reshaping clinical trial design, execution, and analysis. Learn how to deploy it safely, measurably, and at scale with help from life sciences consultant and MIT industry advisor Partha Anbil.

  10. The Uncomfortable Conversation: AI And Data Use In Clinical Trials 3/2/2026

    AI is quietly transforming clinical trials at the site level, boosting efficiency while creating hidden risks for patient data, protocols, and sponsor intellectual property.