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Policy·The Trace·Dual reading·Published 2026-08-15

use of artificial intelligence tools in the medicine lifecycle

Source article: Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance

Abstract: Regulatory bodies play a central role in providing guidance that enables safe and effective use of artificial intelligence tools in medicine development and evaluation. Regulators can also act as catalysts for regulatory science research. To inform these efforts, a European-wide survey was conducted to solicit stakeholder perspectives on the priority areas for regulatory science research related to the use of artificial intelligence in the medicine lifecycle. Twenty-eight regulatory science research questions we…

TRV-2026-0772Peer-reviewedPermanent record — cite & verify
Trace impact reading

Contested: both sides are scored from claims and sources, not community votes.

P 71The P score combines the specificity and measured human impact of the grounded problem claim with the strength of this Trace’s cited sources.G 67The G score combines the specificity and measured human impact of the grounded gain claim with the strength of this Trace’s cited sources.
Regulatory Research Priorities for AI Use in the Medicine Lifecycle: A European Perspective with Global Relevance

Ibadan School of Government and Public Policy, Awolowo Avenue, Ibadan by SuperSwift. CC BY-SA 4.0 · https://creativecommons.org/licenses/by-sa/4.0

The quick read

On 2026-08-13, a peer-reviewed article reported a European-wide survey to set regulatory science research priorities for AI use in the medicine lifecycle. Authors developed 28 research questions across seven domains and collected 273 responses from regulators, industry, patients and consumers, academics, and healthcare professionals, finding convergence in rankings across groups.

The prioritization matters because regulators are positioned as catalysts for research that enables safe and effective AI use in medicine development and evaluation. The resulting 10 priority areas concentrate in accuracy and reliability, data governance, and ethics and bias, but the source does not report measured outcomes of implementing those priorities, leaving uncertainty about how research will translate into guidance.

Main points
  • European-wide survey developed 28 regulatory science research questions across seven thematic domains including accuracy and reliability, data governance, and ethics.
  • 273 responses were collected from regulators, pharmaceutical industry professionals, patients and consumers, academics, and healthcare professionals.
  • Rankings of research challenges frequently converged across stakeholder groups and levels of AI experience.
  • Top-ranked questions were weighted by domain importance to produce 10 priority areas, with majority in accuracy and reliability, data governance, and ethics fairness and bias prevention.
Gain

Regulatory guidance enables safe and effective use of AI tools in medicine development and evaluation across the medicine lifecycle.

Problem

Stakeholders flag unresolved risks for AI in the medicine lifecycle around accuracy and reliability, data governance confidentiality and consent, and ethics fairness and bias prevention requiring further regulatory science research.

The rundown

The survey structured 28 questions into seven domains: research integrity and intellectual property; accuracy and reliability; data governance, confidentiality, and consent; regulation and oversight; ethics, fairness, and bias prevention; resources and support; and impact on jobs and skills, with four challenges ranked per domain.

Results were synthesized by weighting top-ranked questions within each domain by overall domain importance to identify 10 priority areas intended to support researchers and funding bodies in addressing knowledge gaps on AI in the medicines lifecycle.

Sources

Reader signal

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The debate