Applications and Challenges of Artificial Intelligence and Big Data in Drug Regulation
Clinical Snapshot
PICO Framework
| P — Population | Drug regulatory agencies and systems globally (FDA, EMA, China NMPA, Japan, Canada) |
| I — Intervention | Implementation of artificial intelligence and big data technologies in drug regulation |
| C — Comparator | Traditional drug regulatory approaches and systems |
| O — Outcomes | Regulatory efficiency, scientific decision-making, challenges in implementation, international cooperation frameworks |
Bottom Line
This narrative review examines the current state of AI and big data implementation in global drug regulation, highlighting both opportunities and significant challenges. While major agencies like the FDA and EMA have established strategic frameworks and pilot programs, implementation remains largely exploratory. Key barriers include limited model reliability and interpretability, insufficient data standardisation, regulatory gaps, and public trust concerns. The review emphasises that successful implementation will require strengthened regulatory standards, enhanced data governance, improved regulatory capacity, and deeper international collaboration. For Australian healthcare professionals, this provides context for understanding global regulatory trends that may influence TGA processes and pharmaceutical approval pathways. However, the lack of systematic methodology and quantitative outcomes limits the strength of evidence for specific implementation strategies.
Key Findings
P Value: Not applicable - descriptive review
Effect Size: Not quantified - descriptive analysis only
Primary Outcome: AI and big data implementation in drug regulation remains exploratory with significant challenges
Nnt Or Sensitivity: Not applicable - regulatory policy review without clinical outcomes
Confidence Interval: Not applicable - no statistical analysis
Clinical Application
Implementation challenges identified include technical limitations, data standardisation issues, and regulatory capacity constraints Relevant to TGA modernisation efforts and potential adoption of AI-assisted regulatory processes, though Australia not specifically covered in the review Regulatory professionals, pharmaceutical industry stakeholders, healthcare policy makers
Abstract
Artificial intelligence (AI) and big data are increasingly applied in drug regulation and have demonstrated significant potential worldwide. The U.S. Food and Drug Administration (FDA) has developed a relatively comprehensive approach through strategic frameworks, regulatory guidelines, and pilot programs. The European Medicines Agency (EMA) has promoted AI adoption via the Big Data Task Force, DARWIN EU®, and a multi-annual work plan, while Japan, Canada, and other countries have also advanced relevant initiatives and strengthened international cooperation. In China, smart regulation has been incorporated into the "14th Five-Year Plan" and subsequent strategies, with progress in establishing national regulatory data platforms, pharmaceutical traceability systems, and pilot AI applications. Nevertheless, AI in drug regulation remains at an exploratory stage, facing challenges such as limited model reliability and interpretability, insufficient data standards and interoperability, regulatory gaps, and ethical as well as public trust concerns. Future progress will depend on strengthening regulatory standards, enhancing data governance, improving regulatory capacity, and deepening international collaboration to achieve more scientific, intelligent, and efficient drug regulation.
References
- 1.Liu, Zhao, Zheng, Yang, Fang, Jun, Yuan, Lin. (2026). Applications and Challenges of Artificial Intelligence and Big Data in Drug Regulation. Therapeutic Innovation & Regulatory Science. https://doi.org/10.1016/j.compeleceng.2024.109370
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