AI Regulatory Research Priorities in Medicine Lifecycle Management

AI Regulatory Research Priorities have crystallised around one central requirement: establishing trustworthy performance of artificial intelligence systems across the entire pharmaceutical development and evaluation pathway. Stakeholders from every corner of the European medicines regulatory network agree that AI—machine-based systems capable of inferring predictions or decisions from input data—cannot safely guide patient-impacting decisions until regulators close critical gaps in accuracy, data protection, and bias control. These AI Regulatory Research Priorities send a unambiguous message to research funders about where scarce resources must be directed first.

Ranking Research Needs Under Resource Constraints

Researchers built a cross-sectional survey following an exhaustive scoping review of existing AI literature. The instrument presented seven thematic domains, each containing four concrete research questions. Participants first selected their three highest-priority domains under forced budget limits, then completed head-to-head ranking exercises inside every domain. This dual approach delivered both raw preference orders and statistically weighted importance scores.

By inverting mean ranks and multiplying them by the percentage of respondents who had elevated each domain into their top three, the analysis produced a composite score that respected both intra-domain sequencing and overall thematic urgency. The method ensured that foundational topics such as accuracy carried appropriate influence while still reflecting concerns from every domain.

Accuracy and Ethical Data Use Dominate the Agenda

Among 273 completed responses, national regulators and pharmaceutical professionals formed nearly half the sample, with most participants reporting at least basic AI experience. Accuracy and reliability emerged as the clearest frontrunner, followed closely by data governance together with ethics, fairness, and bias prevention. Impact on jobs and skills ranked last.

After weighting, the ten highest-priority questions addressed assurance of model accuracy in clinical evidence, robustness against data drift or performance decay, regulatory thresholds for explainability, appropriate consent models for secondary data use, auditability of training datasets, practical bias detection and mitigation techniques, transparent communication of AI limitations, current gaps in benefit-risk and pharmacovigilance guidance, strengthened reproducibility standards, and essential technical validation controls. These AI Regulatory Research Priorities demonstrated striking convergence across stakeholder types and AI experience levels.

From Priorities to Practical Regulatory Standards

The ranked questions now supply regulatory science organisations with an evidence-based roadmap for allocating research funding. The resulting methodological standards will clarify expectations for data handling, continuous performance monitoring, and bias management—reducing uncertainty for sponsors and promoting consistent decision-making across jurisdictions.

HEOR teams generating real-world evidence and economic models stand to gain immediately: more reliable AI tools will sharpen effectiveness estimates and patient-relevant outcomes used in reimbursement submissions. By concentrating effort on these ten questions, the European medicines regulatory network and aligned global initiatives can accelerate scientifically robust practices that protect patients while enabling timely, responsible integration of AI throughout the medicine lifecycle. The full findings are available in this stakeholder-driven European study.