Blog > Digital Health & AI: How Smarter Regulation is Accelerating Global Access

By GARR Staff  

October 6, 2025

Digital health and artificial intelligence (AI) have become transformative forces across global healthcare systems. From AI-assisted diagnostics to remote patient monitoring, these technologies offer powerful tools to improve care quality, expand access, and support clinical decision-making. As digital health innovation accelerates, regulatory systems worldwide are evolving to keep pace—ensuring safety while enabling responsible adoption.

 

🌍 Global Regulatory Momentum for AI & Digital Health

Across regions, regulators are developing or refining frameworks for Software as a Medical Device (SaMD) and AI-enabled medical devices (AI-MDs). Momentum is driven by the need to balance innovation with safety, transparency, and reliable performance.

 

  • The International Medical Device Regulators Forum (IMDRF) leads global harmonization efforts through working groups dedicated to AI/ML-enabled medical devices and GMLP (Good Machine Learning Practices).
  • Many national authorities now apply risk-based classifications for AI-driven software, aligning with IMDRF’s foundational SaMD principles.
  • Review pathways are increasingly structured to accommodate the unique lifecycle of digital technologies—particularly adaptive algorithms.

This movement toward convergence is helping regulators, developers, and patients benefit from shared standards and reduced duplication.

 

✅ Key Regulatory Trends Shaping Digital Health & AI
  1. Risk-Based Oversight for AI/ML Software: Regulators classify software based on intended use and patient impact. This approach ensures appropriate oversight while maintaining flexibility for innovation.  In the European Union, standalone software—including AI tools—are subject to detailed classification under the MDR to determine conformity assessment requirements. In the United States, FDA evaluates AI/ML-based SaMD through traditional 510(k), De Novo, or PMA pathways, depending on risk and novelty. 
  1. Lifecycle Management for Adaptive Algorithms: Regulatory science recognizes that AI tools evolve after deployment. As a result, new frameworks emphasize defining algorithm change control plans, monitoring real-world performance, ensuring data quality and representativeness as well as maintain transparency around model updates. These principles support ongoing safety and effectiveness throughout the device lifecycle.

 3. Convergence Around IMDRF Principles : IMDRF’s SaMD framework and GMLP guidance have become global reference points, with alignment seen in regions including Europe, U.S, Japan, Australia, and Canada. This shared foundation promotes consistency in expectations, allowing developers to plan for multi-jurisdictional submissions more efficiently.

 

 4. Global Collaboration and Reliance Approaches: Some regulatory authorities are exploring cooperation models, including reliance and work-sharing, for digital health technologies. This helps streamline review processes, particularly in regions with limited regulatory capacity, while maintaining high standards for safety and performance.

 

🚀 What This Means for Innovation and Public Health

Well-designed regulation is enabling:

  • Faster access to safe digital tools

Clearer pathways reduce uncertainty for developers and accelerate global rollout of beneficial technologies such as AI diagnostics and remote monitoring systems.

  • Increased trust in AI-enabled healthcare

Frameworks that emphasize transparency, fairness, and post-market performance monitoring help ensure the tools are reliable and clinically meaningful.

  • Equitable global adoption

Regions with emerging regulatory systems benefit from harmonized guidance, supporting access while reducing administrative burden.

  • Stronger evidence generation

Real-world data and continuous monitoring help stakeholders better understand how AI products perform across diverse populations and settings.

 

🔭 Looking Ahead: The Future of AI & Digital Health Regulation

As of 2025, the regulatory landscape for digital health and AI has become more coordinated, with increasing emphasis on:

 

  • International standards
  • Data governance and robustness
  • Algorithm transparency
  • Bias mitigation and equity
  • Post-market surveillance systems
  • Collaborative review models

Digital health and AI hold immense potential. With globally aligned and thoughtful regulation, these technologies can advance health equity, support clinicians, and improve outcomes for patients worldwide.

 

📚 References

 

 

  • Ong, J. C. L., Ning, Y., Liu, M., Ma, Y., Liang, Z., Singh, K., Chang, R. T., Vogel, S., Lim, J. C., Tan, I. S. K., Freyer, O., Gilbert, S., Bitterman, D. S., Liu, X., Denniston, A. K., & Liu, N. (2025, January 27). Regulatory Science Innovation for generative AI and large language models in health and Medicine: a global call for action. arXiv.org. https://arxiv.org/abs/2502.07794

 

 

  • Zhang, S., Li, Y., Liu, W., Chu, Q., Wang, S., Li, J., & Chen, Y. (2025). A decade of review in global regulation and research of artificial intelligence medical devices (2015–2025). Frontiers in Medicine, 12, 1630408. https://doi.org/10.3389/fmed.2025.1630408

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