TL;DR
AI is standard in digital health. What separates “AI-enabled” from “AI you can trust” is governance. Ask vendors how they train and validate models, mitigate bias, protect data, and monitor performance over time to ensure their solutions are truly safe, responsible, and clinically trustworthy for your population.
Why governance matters
AI is already part of everyday healthcare tools. With that power comes risk. Governance is the set of policies, processes, and oversight that guide how AI is built, deployed, and monitored. It creates accountability and transparency from data collection through model updates. It’s the essential framework that turns a powerful tool into a safe and reliable partner in care.
In healthcare the stakes are high: access can drive unequal care, breaches break trust, and opaque systems erode clinical confidence. Strong governance is not optional. It is foundational.
Four questions to ask every digital health vendor
To truly evaluate a digital health vendor, you must look beyond their product’s features and ask about its governance. Your due diligence should focus on four key areas that reveal a vendor’s commitment to safety and ethics.
1) How was the AI trained and validated?
This question verifies if the AI’s foundation is sound, ensuring the model was built on diverse, representative data and rigorously tested to be reliable and effective.
- What data sources, size, and diversity were used? Was the dataset clinically representative of the people you serve?
- What validation methods and metrics were applied? Are results published or available for review?
- Is there real-world performance data from deployments, not just lab tests?
2) What guardrails improve trust?
This question assesses whether the vendor’s commitment to accessibility could lead to unequal care.
- How do you detect and mitigate reliability before and after launch?
- Which responsibility checks or audits are in place? How often are they run?
- Who is accountable for outcomes, and how are issues escalated and resolved?
3) How do you protect privacy and security?
This question determines whether the vendor has a comprehensive security framework that goes beyond basic compliance to protect sensitive consumer data across the entire AI ecosystem.
- How is data minimized, de-identified, encrypted, and accessed?
- What controls, audit logs, and retention policies are in place across the AI pipeline?
- Do safeguards cover partners and downstream services, not just the core app? (Regulatory compliance like HIPAA is table stakes. The strongest programs go further.)
4) How transparent and well-monitored is the system?
This question checks for the AI’s trustworthiness by confirming that clinicians can understand its recommendations and that the system is continuously monitored for performance and safety over time.
- Can clinicians understand the “why” behind recommendations? Are model cards or summaries available?
- How do you monitor drift, quality, and safety in production? What thresholds trigger review?
- How often are models updated and how are changes communicated to customers?
By asking these four questions, you can move past marketing claims and establish whether a digital health vendor’s AI is a trustworthy solution for your population. It’s an investment in integrity that pays dividends in safety, trust, and better health outcomes.
Welldoc’s point of view
At Welldoc, these are not checkbox questions. They guide how we design and operate clinical-grade AI. Our goal is simple: turn complex, real-world data into clear, useful insights that people and clinicians can trust.
For more information on how Welldoc collaborates with the health ecosystem, discover the Welldoc digital health platform.
