How Should We Prioritize AI Opportunities in Healthcare?
Image credits: CDC| Unsplash
Not every algorithm is worth our time—or our trust.
AI is having a moment in healthcare. Every week brings headlines about new models promising to out-diagnose doctors, streamline operations, or personalize care. And yet, beneath the excitement, one question lingers:
How do we know which AI opportunities are actually worth it?
This is a human question, not just a technical one. Most of us think that healthcare isn’t a playground for “move fast and break things.”
Plus, every shiny new algorithm comes with opportunity costs: budget diverted from proven interventions, clinician time spent onboarding yet another pilot, patient trust eroded when hype outruns results.
To turn AI from gadgetry into genuine health gains, we need a better filter.
The High-Value AI Funnel
In my work helping healthcare leaders and systems navigate the AI revolution, I’ve found that three deceptively simple questions can cut through the noise:
Does this AI-powered approach target a priority problem?
Is there strong evidence that it works in the real world?
Will patients and clinicians actually trust and use it?
Together, these form the core of what I call a High-Value AI Funnel: a structured process to help organizations choose wisely and avoid the all-too-familiar fate of the “zombie project.”
Let’s take a brief tour through each gate of the funnel.
Gate 1: Strategic Fit
Image credits: Freedomz| Adobe Stock
The headline here is: Solve the right problems, not just the exciting ones.
Too often, AI in healthcare gets deployed where it’s easy to make a splash instead of where it’s most needed. But with limited budgets, rising workloads, and climate goals to consider, healthcare systems should prioritize tools that meet real needs and can run on existing infrastructure.
That means aligning with:
💡 Disease burden in the population that a particular system serves,
💡 Geriatric and chronic care demands,
💡 Gaps in access to care (broadband, clinicians, geography), and
💡 Sustainability and cost-containment targets.
If you’re working on AI in healthcare, always ask: Does this tool tackle a problem that’s both urgent and underserved? If not, you might be facing a distraction instead of a breakthrough.
Gate 2: Evidence Strength
Image credits: CDC | Unsplash
Trust the data, not the demo.
Healthcare runs on trust … and trust demands evidence. If a model lacks empirical evidence that it can improve clinical outcomes, reduce costs, and/or support equity across diverse populations, then it’s probably not ready to scale.
Keep your eyes open for:
💡 Clear KPIs (including health outcomes and environmental impact),
💡 Rigorous pilots across multiple sites,
💡 Transparent publishing of results (good, bad, or ugly), and
💡 A consistent evaluation rubric for comparing tools.
This part of the funnel isn’t about slowing innovation. It’s about speeding the adoption of what actually works.
Gate 3: Community Fit
Image credits: Count Chris | Unsplash
The exam room is not the right place for a black box.
An algorithm can be brilliant in the lab and still flop in practice if it’s hard to use, poorly explained, or fails to earn the trust of those it’s meant to help.
To avoid that, watch for AI tools that have been designed with humans at the center:
💡 Co-create with clinicians and patients early,
💡 Make model logic and limits understandable,
💡 Ensure access for low-bandwidth, multilingual, or offline users,
💡 Integrate with existing workflows, and
💡 Track real trust metrics, not just clicks.
Enthusiastic adoption (not just reluctant compliance) helps turn tech into real partnership, which is how true transformation happens.
The Investment Is Real. So Is the Payoff.
Building a High-Value AI Funnel isn’t a side hustle. It requires time, money, and executive attention. But the payoff is a repeatable, trustworthy process that helps healthcare organizations pick winners—and avoid tech that burns money, erodes morale, or harms patients.
Ultimately, instead of focusing on smarter AI, we should be building systems that:
💡 Focus on meaningful outcomes,
💡 Enhance staff well-being,
💡 Deliver equity and sustainability, and
💡 Earn and keep public trust.
PS I’m expanding this thinking into a full white paper to support healthcare systems, funders, and innovators who want to bring strategic discipline to AI decisions. Stay tuned!
If you’re wrestling with how to separate signal from noise in the AI-for-health gold rush or want to be part of shaping the High-Value AI Funnel, I’d love to hear from you.
About Tiffany
Dr. Tiffany Vora speaks, writes, and advises on how to harness technology to build the best possible future(s). She is an expert in biotech, health, & innovation.
For a full list of topics and collaboration opportunities, visit Tiffany’s Work Together webpage.
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