Reimagining drug regulation in the age of AI: a framework for the AI-enabled Ecosystem for Therapeutics
Bibliographic record
Abstract
Artificial intelligence (AI) is increasingly integrated into drug development and regulatory decision-making; however, the regulatory landscape governing these technologies remains fragmented. While agencies such as the US Food and Drug Administration (FDA) and European Medicines Agency (EMA) have begun issuing guidance on AI applications in human therapeutics, these frameworks differ substantially in scope, terminology, and application. This lack of alignment complicates regulatory interpretation, creates barriers to regulatory coordination, and impedes equitable access to AI-enabled therapies. In this article, we introduce the AI-enabled Ecosystem for Therapeutics (AI2ET) framework, a conceptual and policy-oriented model designed to support the federation of regulatory knowledge and promote regulatory alignment. The AI2ET shifts regulatory focus from individual AI-generated products to the broader AI-enabled systems, platforms, and processes that underpin drug development. This approach addresses current regulatory gaps in AI oversight by articulating clear definitions of the components that constitute the ecosystem, establishing risk-based decision-making pathways, and finally offering regulatory guidance to navigate the ecosystem. The article offers six key policy recommendations that include strengthening international cooperation, establishing shared regulatory definitions, and investing in regulatory capacity building. By laying down a conceptual foundation for regulatory science-based oversight of AI in therapeutic development, the AI2ET framework offers a path forward for inclusive, effective, and equitable oversight of AI in regulating human therapeutics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.042 |
| Scholarly communication | 0.021 | 0.020 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".