Mireille Gomes - Can Algorithms Heal? Reimagining Health Equity with AI and Data Justice

What if our smartest health tools still miss the people who need them most? We sit down with AI and digital health scientist Mireille Gomes to examine how innovation can serve dignity, not just efficiency—and what it takes to build technology that works from Geneva to rural clinics without electricity.

The journey of Mireille Gomes spans continents and roles, from vaccine strategy at Gavi to AI diagnostics at Merck. Together, we unpack the real barriers to deployment—uneven infrastructure, overworked staff, and data voids that erase entire communities from the record. We look at consent‑first design, why open data must be truly anonymous, and how representation in civil registration and vital statistics underpins every “fair” algorithm. You’ll hear pragmatic ideas for triage tools that flag urgency in seconds, health education in local languages, and micro‑local models that adapt to context while sharing standards globally.

We also push on the hard questions: Who decides which data matters? Can algorithms be biased toward justice if the world is not? Where is the line between breakthrough and overreach when crises demand speed? Mirielle argues for building abuse cases into development, testing for misuse before launch, and preserving community storytelling—especially Indigenous knowledge—alongside dashboards. The goal is health equity by design, so no one’s care depends on their birthplace or bandwidth.

If you care about AI in healthcare, data justice, and solutions that actually work on the ground, this conversation offers a clear roadmap and candid guardrails. If it resonates, subscribe, leave a review, and share it with someone shaping the future of digital health.

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The views and opinions expressed (by the host and guest(s)) in this podcast are strictly their own and do not necessarily reflect the official policy or position of the entities with which they may be affiliated. This podcast should in no way be construed as promoting or criticizing any particular government policy, institutional position, private interest or commercial entity. Any content provided is for informational and educational purposes only.