AI is reshaping how diabetes is diagnosed, monitored and managed. From predictive alerts to personalized recommendations, smart systems are becoming part of daily care for many patients. Joe Kiani, founder of Masimo, recognizes the urgency of building these tools with responsibility and clarity. As more people turn to technology to support critical health decisions, the focus must shift toward systems that are fair, reliable and designed with patients in mind.
Innovation is moving fast. The real test now is whether AI in diabetes care can grow in ways that reflect the values of trust, equity and accountability. Without that foundation, even the most advanced tools may fall short where it matters most.
The Risks of Unchecked AI in Diabetes Tools
Without thoughtful design, AI can unintentionally worsen existing disparities. Algorithms trained on non-representative data may produce inaccurate recommendations for people from underrepresented groups. In diabetes care, this could mean misleading risk predictions or flawed insulin dosing suggestions that cause real harm.
Bias in AI is not always obvious. It may emerge from historical data, skewed sampling or even the way questions are asked during app setup. Without strong ethical guardrails, these platforms risk missing the mark for people who already struggle to access healthcare, making the very gaps they’re meant to close even wider. For AI to be useful in diabetes care, it must work reliably across different populations, lifestyles and environments. That requires deliberate, ethical engineering from the start.
Transparency Builds Trust
One of the most important elements of ethical AI is transparency. Patients must be able to understand how decisions are made and why certain suggestions are delivered. This is especially critical in diabetes care, where moment-to-moment decisions can significantly affect health outcomes.
Platforms that clearly explain how they use data, how algorithms are developed and how user information is protected are more likely to gain long-term trust. Clear language, open documentation and meaningful consent processes are essential.
Patients should never feel confused about why they’re receiving a specific alert or recommendation. The more accessible and open a system is, the more likely it will be used, and trusted, by the people who need it.
Protecting Data Means Protecting People
Managing diabetes means sharing a lot. Blood sugar trends, eating habits and activity levels become part of the record. When that information is handled with care, the technology becomes part of the support system. When it is not, trust breaks down, and people stop using it.
Respect for patient data reinforces respect for patients themselves. People managing diabetes share deeply personal details through digital platforms, often without seeing who is on the other side. That makes trust essential. Joe Kiani believes that”In digital diabetes care, ethical AI development isn’t just a responsibility; it’s a commitment to putting patients’ needs and safety at the forefront, allowing innovation to improve lives, without compromising trust or equity.”
That kind of responsibility is not about optics or compliance. It is about building systems that earn trust by protecting people and supporting their care in a way that holds up when it matters most.
Ensuring Equity in AI-Driven Diabetes Care
Health equity must be a central pillar in ethical AI development. Diabetes affects people from all walks of life, yet access to care and outcomes are still shaped by race, income and geography. AI tools that ignore these dynamics risk reinforcing them.
To design equitably, platforms must be trained on diverse datasets, account for social determinants of health and offer features that adapt to various living conditions. This might mean including low-data modes, offline functionality or multi-language support.
Ethical development also includes directly involving communities. Gathering input from those who will use the technology, especially those from underserved groups, ensures the final product reflects real needs and not just theoretical ones.
Human Oversight Still Matters
Even the most advanced AI cannot replace the role of human judgment in diabetes care. Algorithms should support, not supplant, the relationship between patients and providers. Ethical platforms include clear pathways for human review, clinician input and override options when needed.
Tools that emphasize collaboration, rather than automation, are more likely to be embraced by healthcare professionals. They also reduce the risk of overreliance, where users defer to algorithms even when results seem off. AI may power insights, but human empathy, experience and intuition remain central to delivering safe and personalized care.
Accountability From Development to Deployment
Ethical AI must be accountable at every stage, from early development to real-world deployment. This includes regular audits for bias, safety testing under different use cases and ongoing monitoring of performance across populations.
If issues are discovered, platforms must act quickly and transparently to address them. Ethical accountability isn’t just about fixing mistakes; it’s about being proactive, responsible and responsive throughout the product’s lifecycle.
This type of governance builds credibility not just with users but with regulators, partners and the broader healthcare community. It signals a long-term commitment to doing the right thing, even when it’s not the easiest path.
Setting the Standard for Responsible Innovation
As digital diabetes care continues to expand, the industry has a choice: develop AI quickly to meet market demand or do it deliberately to build sustainable, equitable systems. The most respected companies are choosing the latternot because it’s trendy but because it’s the only path that leads to real, lasting impact.
Ethical AI is not a feature or a PR strategy; it’s a foundation. Without it, even the most powerful tools risk doing more harm than good. With it, technology becomes something more than a product; it becomes a platform for better care, stronger relationships and healthier lives.
Building Trust Starts with Design
Ethical development is not a side note in digital health. It is the foundation. As AI becomes more involved in diabetes care, the way these tools are built will shape how they are used and whether they are trusted.
Patients need systems that protect their data, reflect their needs and support their decisions, without overstepping. Clinicians need tools they can stand behind. Developers need to own the responsibility that comes with influencing real lives.
Ethical AI is not just about avoiding harm. It is about showing up for people in ways that are clear, fair and grounded in care. That is what builds trust. That is what makes innovation worth scaling.