Real-World Intelligence: The Key to Healthtech Equity in Mexico

In a rural clinic in Oaxaca, a state-of-the-art diagnostic device sits idle, wrapped in plastic, untouched since its arrival six months ago. It was part of a national initiative to bring high-tech equipment to underserved regions. It came with promise, with pride, with press releases. But it also came with software that requires Wi-Fi, instructions in English, and a user interface designed for a hospital in Germany, not a health post two hours from the nearest paved road.

This isn’t a rare story. In fact, it’s a painfully common one across rural and underserved regions of Mexico. While the global medical device industry races to embed artificial intelligence into every diagnostic and monitoring solution, it often overlooks something far more urgent:  real-world intelligence or contextual intelligence. That is, the ability of a system, not just a person or algorithm, to adapt to the environment in which it operates. Millions of dollars in medical technology are lost not because the tools are broken, but because they were never designed for such scenarios in the first place.

While the global healthtech industry races ahead with artificial intelligence, machine learning, and cloud-based analytics, the fundamental problem in much of the region is not a lack of innovation. It’s a lack of this so-called real-world intelligence — the kind of design that begins not with the question “What can we build?” but “Who will use this, and where, and how?”

Yes, AI matters, and matters a lot. Its ability to detect diseases earlier, personalize treatment plans, and process massive volumes of clinical data is game-changing. But in communities without stable electricity, without trained staff, without broadband infrastructure, AI alone is not enough. What matters most is context — and the humility to design for it.

Let’s be clear: this is not an anti-technology rant. Quite the opposite. Artificial intelligence can be a powerful enabler of equity in healthcare, if we first ground it in context.

It means understanding that a blood analyzer that needs daily calibration won’t survive in a clinic that only has an assistant nurse three days a week. That a device requiring smartphone pairing is useless where patients don’t own phones. That an AI-powered diagnostic platform isn’t innovative if it fails to run offline in an emergency room with no signal and a power outage.

In other words, intelligence that works in the real world.

Again, let’s be clear: This isn’t about lowering standards or giving up on innovation. On the contrary, it’s about applying intelligence more wisely, more inclusively, more humanely.

The World Health Organization estimates that nearly half of the global population lacks access to essential diagnostics. In Latin America, the problem is not just availability, it’s adaptability. Rural clinics may lack stable internet. Community health workers may speak Indigenous languages. Transportation may be by mule, not ambulance. Designing for this reality demands more than software updates. It requires a shift in mindset. 

Take, for instance, the concept of “frugal innovation” — developing cost-effective solutions tailored to low-resource settings. The Aravind Eye Care System in India used this model to become the largest provider of eye surgeries in the world, designing their own low-cost diagnostic tools when commercial ones didn’t meet their needs. In the diagnostics space, startups like Zipline have reimagined medical supply chains using drone delivery for blood and lab samples in Rwanda and Ghana. These are not "cheap" solutions. They are smart solutions, designed with context at the core.

These aren’t stories of less — they’re stories of better design.

In Mexico, the opportunity is even greater. With its growing healthtech sector, local manufacturing capacity, and the nearshoring wave, the country could become a regional hub of context-driven innovation. But to get there, we need to change the question.

Stop asking: “What’s the most advanced diagnostic we can import?”
Start asking: “What’s the smartest diagnostic we can use here, today, sustainably?”

The answer may not lie in another AI algorithm. It may lie in:

• Devices that switch to manual mode when the app fails.
• Interfaces in Spanish and Indigenous languages.
• Dashboards that work with or without cloud access.
• Portable systems powered by rechargeable batteries or solar cells.
• Modular instruments that are repairable locally, not just by foreign engineers.

Contextual intelligence, unlike artificial intelligence, cannot be bought off the shelf. It requires immersion, empathy, and an honest look at how systems function when the white coats and PowerPoint slides are gone. It’s not about replacing AI, it’s about rooting it in the real world.

Above all, it means involving the right people early. Real-world intelligence is co-designed. It listens to the nurse who will carry the device. The technician who will clean it. The community health worker who will explain the results. And it asks: “Does this make your job easier, faster, safer — or more complicated?”

Because when a device increases workload instead of reducing it, it doesn’t matter how “smart” it is.

Procurement systems also need to evolve. Today, many public purchases are made based on technical specs and lowest cost. But what if RFPs required evidence of field usability? Or measured a device’s ability to integrate into low-resource workflows? What if real-world pilots in underserved regions were mandatory before national rollout?

Mexico’s healthcare transformation, especially with IMSS-Bienestar’s ambitious expansion, deserves more than imported complexity. It deserves tools designed to function where the need is greatest. Not where the infrastructure is best.

And let’s not forget the human side.

Empathy, not just engineering, should lead medical device design. Because the true enemy is not outdated tech—it’s arrogance. The arrogance of believing that a device that works in Boston will automatically work in Campeche. That cloud access is universal. That every user reads English. That AI trumps local insight.

The real future of healthcare isn’t more artificial intelligence. It’s more human intelligence.

It’s about building with eyes open, with boots on the ground, with listening at the center of innovation. It’s about blending the promise of AI with the wisdom of field nurses, the creativity of rural doctors, and the voice of the communities we claim to serve.

Real-world intelligence is not a buzzword. It’s a moral obligation.

Let’s stop designing for awards and start designing for results. Let’s measure innovation not by patents or code, but by how many people it actually helps.

Let’s stop putting cutting-edge devices in environments that barely have a working fridge.

And let’s start building solutions that work not in theory, but in Tehuantepec, Tapachula, or Tula — on real days, with real people, under real pressure.

Because it’s not that we lack artificial intelligence.

It’s that too many devices still lack empathy.