Biopharma sponsors are rebuilding trial infrastructure around AI, interoperable data and cloud-native apps. The goal: capture cleaner data sooner, make better decisions earlier, and move safe therapies to patients faster—without compromising compliance. Recent regulatory updates and technical standards now give clearer guardrails for doing this at scale.

Why this matters in 2026

• Regulatory clarity: FDA finalised guidance for trials with decentralised elements (home visits, telehealth, remote data) and published expectations for AI used to support regulatory decisions—emphasising risk-based credibility of models.

• Global alignment: EMA’s reflection paper frames a human-centric approach to AI across the medicines lifecycle, guiding oversight from data acquisition to evaluation.

• Digital protocol standards: ICH M11 introduces a harmonised, electronic, structured protocol (CESHARP), enabling consistent content and machine-readable exchange between sponsor systems and regulators.

Key benefits sponsors are realising

• Enhanced data accuracy & speed: AI-assisted cleaning, anomaly detection and medical coding streamline data management; near-real-time checks reduce cycle times from data entry to analysis.

• Smarter decisions earlier: Predictive insights improve feasibility, site selection and enrolment forecasts; simulation helps de-risk protocol choices before first-patient-in.

• Operational agility: Decentralised elements (ePRO/eCOA, telehealth, wearables) widen access and reduce burden, while still fitting within FDA’s final DCT guidance.

How it works

Modern stacks blend:

• Structured design (ICH M11): Start with an electronic, standardised protocol so endpoints, visits, and data requirements flow cleanly into downstream systems.

• AI-enabled data capture & quality: Use AI to automate transcription, extract values from unstructured notes, flag implausible ranges or protocol deviations, and harmonise EHR/claims data used in studies.

• Decentralised elements: Integrate remote assessments and in-home procedures with clear roles/responsibilities, data security, and validated devices—per FDA guidance.

• Governance for credibility: Apply a risk-based framework to justify each AI model’s context of use, inputs, verification/validation and performance monitoring.

Practical steps to get started (and stay compliant)

1. Adopt a structured protocol template (ICH M11). Align trial design to the new standard so systems and reviewers can consume protocol elements automatically.
   
   

2. Map data sources and standards. Prioritise interoperable capture (ePRO, EDC, EHR) and define exchange using HL7 FHIR where appropriate to reduce wrangling and support RWD use cases.
   
   

3. Implement AI for data quality. Start with supervised checks (outliers, missingness, cross-form consistency) and autocoding—then progress to anomaly detection at the subject/visit level.
   
   

4. Pilot decentralised elements. Use telehealth or home nursing for eligible visits; set SOPs for device calibration, chain of custody and investigator oversight aligned to FDA’s DCT final guidance.
   
   

5. Stand up AI governance. For every model, document context of use, datasets, validation, bias/robustness tests, and performance monitoring—per FDA’s credibility framework.
   
   

6. Design with Europe in mind. If operating in the EU/UK, consider EMA’s AI expectations and UK MHRA software/AI device guidance when using digital health tools as part of the trial.

Real-world momentum

Industry platforms report faster study start-up, cleaner datasets and fewer query cycles when AI is embedded in design and data-management workflows. Sponsors also use AI to improve feasibility and diversity planning—areas increasingly visible to regulators and payers.

Looking ahead

Regulators continue to recognise AI’s role across the drug lifecycle, including qualification of AI tools and expanded use of human-relevant methods and digital evidence. Keeping architectures modular and policy-led will help sponsors seize speed and quality gains while staying audit-ready.

Next Steps: Want an adoption plan that meets FDA/EMA expectations and your SOPs? Generation Digital can help blueprint your data flow, pilot AI-enabled quality checks, and operationalise decentralised elements across portfolios.

FAQs

Q1: Why is AI important in biopharma R&D?
It improves data quality and timeliness, enabling earlier insights for feasibility, enrolment and safety, and shortening cycles between data entry and analysis. Medidata Solutions

Q2: How does AI enhance data capture?
Through automation (e.g., extracting values from notes), autocoding, and anomaly detection, with risk-based validation to ensure models are credible for their context of use. Medidata Solutions

Q3: What are the benefits of modernising IT applications?
Structured protocols (ICH M11), interoperable data standards, and decentralised elements combine to streamline operations, reduce query burden, and support faster submissions. ICH Database

Q4: Is this approach aligned with regulators?
Yes. FDA finalised guidance on decentralised elements and issued AI credibility expectations; EMA’s reflection paper guides AI throughout the lifecycle. U.S. Food and Drug Administration

Sources

• FDA – Conducting Clinical Trials With Decentralized Elements (Final): scope, roles, oversight. U.S. Food and Drug Administration

• FDA – Considerations for the Use of AI in Regulatory Decision-Making: risk-based credibility framework. U.S. Food and Drug Administration

• EMA – Reflection Paper on AI in the Medicinal Product Lifecycle: human-centric expectations. European Medicines Agency (EMA)

• ICH M11 (Final Guideline): electronic structured protocol standard. ICH Database

• FDA – Real-World Data (EHR/claims) guidance: considerations for secondary data in studies. U.S. Food and Drug Administration

• Medidata resources: examples of AI benefits across design, start-up and data management. Medidata Solutions