Artificial intelligence is changing the way diabetes is monitored, managed and understood. From predictive alerts to noninvasive tracking, smart systems are opening new possibilities in chronic care. Joe Kiani, founder of Masimo, recognizes the need to guide that progress with strong safeguards that protect both safety and trust. As digital tools take on a more central role, regulators are beginning to adapt by setting clearer rules around how these technologies are built, tested and used in real life.

Regulators are paying closer attention to how AI tools work in real-world settings, not just how they perform in controlled tests. That means looking at how clearly platforms explain their decisions, how reliably they handle updates and how well they protect patient information. These priorities reflect a broader shift toward oversight that supports not just innovation, but the people expected to use it.

Adapting to a New Kind of Medical Technology

AI tools do not fit easily into the existing categories used to evaluate medical devices. Many are built to respond to new inputs, adjust over time and work across multiple data streams. Unlike static tools that operate the same way every time, AI systems often shift based on how they are used in the real world.

Regulatory bodies such as the FDA and EMA are beginning to account for these differences. New approaches focus more on how algorithms are maintained, how their training data is handled and how performance holds up beyond initial testing. These updates reflect a push to ensure safety, without slowing down access to useful tools.

In diabetes care, where even small decisions can carry weight, the margin for error is slim. That is why newer regulatory models place a strong emphasis on continuous review, transparency and real-world accountability.

Why Guardrails Matter for Everyday Care

As AI continues to shape diabetes management, its success will depend less on how advanced the tools appear and more on how they function in daily life. That includes strong protections for data use, clear oversight of how tools are evaluated and thoughtful processes for managing updates once systems are in use.

Joe Kiani explains, “As AI rapidly transforms diabetes care, regulation must evolve just as swiftly to ensure these technologies are safe, equitable, and truly beneficial for patients.”  His words reflect what is at stake for the people using these systems.

Each prompt, suggestion or pattern flagged by an algorithm influences the small, everyday choices that define care. When regulation takes those realities into account, it helps ensure that technology supports not just innovation but practical, personal progress.

Rethinking Risk and Benefits in Real Time

AI tools are not static. In diabetes care, they respond as conditions change, adjusting insulin guidance, meal suggestions or activity prompts based on ongoing data. This level of responsiveness challenges traditional regulatory models, which rely heavily on pre-approval testing and fixed product labeling.

To meet this challenge, new frameworks are incorporating post-market oversight, ongoing validation and adaptive safety measures that account for software changes over time. These updates help keep tools effective and reliable well after their initial launch.

Regulators are also prioritizing usability testing and real-world interaction. It is no longer enough for a tool to perform well in a controlled trial. It must also work for patients in everyday situations. That shift signals a deeper understanding of risk as something shaped by context, not just clinical endpoints.

Supporting Innovation Without Slowing It Down

One of the major concerns in AI regulation is that oversight might slow progress. In a fast-moving space like digital health, long approval timelines can stall breakthroughs and discourage investment.

To address this, some regulatory agencies are exploring sandbox models that allow companies to test tools in controlled environments, before broader release. Others are piloting collaborative review programs, where developers work directly with regulators throughout the design process.

These strategies are designed to speed up time to market without compromising safety. For diabetes innovators, this means clearer pathways to approval and better guidance on how to meet changing expectations.

Protecting Patients While Enabling Access

As digital tools become more integral to diabetes care, protecting patient privacy and data security is another priority for regulators. AI models rely heavily on health data, and misuse of that data can lead to both individual harm and broader public distrust.

Regulators are stepping up, putting stronger rules in place around data privacy, patient consent, and cybersecurity. In some parts of the world, new AI-specific privacy laws are emerging to work alongside existing protections for health information.

This dual approach supporting innovation while protecting patient rights is essential to building long-term trust in AI-driven healthcare. When patients feel their data is safe, they’re more likely to engage with and benefit from new technologies.

International Collaboration and Harmonization

AI tools often operate across borders, especially when delivered through apps or cloud-based platforms. That means regulators need to collaborate internationally to avoid conflicting rules and uneven enforcement.

Global organizations are beginning to align standards for AI transparency, clinical validation and ethical development. For companies creating diabetes solutions, this creates an opportunity to design once and deploy widely so long as they meet agreed-upon best practices. Harmonization also benefits patients, ensuring that wherever they live, the tools they use are subject to the same high bar for quality and accountability.

The Role of Ethics in Regulatory Strategy

As AI becomes more central to diabetes care, ethical considerations are moving from discussion to implementation. Regulators are beginning to treat fairness, inclusivity and accessibility not as optional values but as core requirements in evaluating new tools.

This shift is shaping how platforms are developed and tested. For technologies aimed at diabetes care, that means proving they work well for diverse populations, tailoring tools for low-resource environments, and designing features that are accessible across various languages and literacy levels. 

Some agencies are now incorporating ethical review into the approval process itself. By doing so, they reinforce a broader principle: innovation must work for everyone, not just for those best positioned to access it. That makes ethics not a checkpoint but a foundation for meaningful progress.

Smart Oversight for Smarter Tools

AI may be changing what is possible in diabetes care, but it is regulation that will decide what becomes practical, safe and lasting. As these tools become more embedded in real care, the role of oversight is expanding to match the complexity they bring. That means evaluating performance, not just at launch but throughout a product’s life, and setting standards that reflect how people actually use these systems day to day.

Good regulation does more than set limits. It builds the foundation for progress that serves patients, providers and public trust. When innovation is shaped by clear rules and shared responsibility, it is more likely to lead to tools that work not just in theory, but in practice.