In 2009, a child living in a village in Rajasthan got a fever. It wasn’t something new during the rainy season; mosquitoes are common, and they often spread sickness.

Maybe it was dengue? The family didn’t panic. They walked for two hours to reach the closest clinic, hoping to find out what was wrong.

What is the result for the child in Rajasthan? It was typhoid.

Six months later, in Berlin, a child had the same signs: high fever and chills. But this time, things were different.

The parents didn’t have to leave home. They spoke to a doctor through a video call. After a few questions and a few clicks, the doctor said it was just the seasonal flu, and they were right.

Two kids. Two countries. Same symptoms. But the way they got care? Completely different.

And the information used to find those answers? Even more different.

One Symptom, A Thousand Contexts

For many years, healthcare technology has been designed using general averages from around the world. This helped improve things like tests, scans, and predicting diseases.

Healthcare that works well for one person might not work for another. Why? Because health is shaped by food, culture, weather, and where people live. Context makes all the difference.

And the information used to make those diagnoses? It needs to match your real life, not just global numbers.

Take fever as an example.

A fever might not seem like a big deal in the West. In an Indian village during the rainy season, a fever could mean something serious. Even if the symptom looks the same, the cause might be different. It depends on things like rain, insects, water quality, and the kind of house you live in.

AI tools in healthcare are growing fast, but most of them learn from data collected in big city hospitals in the U.S., Europe, or other developed countries.

These places are not like small towns or villages. In rural areas, hospitals may not have enough facilities or doctors. People might speak local languages, and the health problems can be very different from those in big cities.

So, the AI tools often don’t work as well in those places because they don’t understand the full picture.

A fever in one place might mean something completely different in another place. When health technology doesn’t consider these differences, it can make more mistakes in finding out what’s wrong.

People can get the wrong treatment, feel worse, and start to lose trust in the healthcare system.

The Flawed Model of Uniform AI

Artificial Intelligence (AI) can do amazing things in healthcare.

It can find tumours very quickly, warn doctors about dangerous test results, and even guess when a disease might spread, before it starts.

But it can only do all this if it knows what to look for and where to look.

The problem is that most AI systems are not trained using local or diverse data.

They mostly learn from the same types of symptoms, medical codes, and patient stories written in English.

That works okay until someone from a village or small town explains their sickness differently, or has an illness that is common in their area but not in Western countries.

AI may give the wrong advice. It’s like giving a child a book with only lions and polar bears, then asking them to find a rabbit. They wouldn’t understand.

They would miss learning about so many other animals in the world.

In the same way, if AI only learns from Western healthcare, it won’t understand the real-life health problems of millions of people living in other places.

Smarter Tech Starts with Local Voices

Things are starting to change. New healthcare tools are being made to care for people as individuals. They don’t follow a one-size-fits-all rule. Instead, they understand that everyone is different and give care that matches their needs.

In India, researchers are teaching AI to understand local languages because not all patients talk about chest pain in English, or even in Hindi.

They use their dialects, and the AI needs to understand them to give the right help.

In some parts of Africa, people are using mobile health tools specially made to find local diseases like malaria and tuberculosis. These tools give quick help in places where there aren’t many doctors around.

New health tools are being made that mix medical information with real-time location data.

This means a symptom isn’t just checked against global facts, it’s also looked at based on local diseases, weather, and health problems in that area.

These aren’t just small changes. They show a big new idea: instead of treating people like numbers, we start treating them like real human beings.

The Real Breakthrough? Healthcare is about real people. It depends on where you live, what you eat, the air around you, and even the stories your family shares.

Technology can help us understand all this better, but only if we first listen and learn from people.

Many believe the future of healthcare is just more technology, smarter machines, faster computers, and more information.

But maybe the real improvement comes from something else: understanding people better.

The future of healthcare isn’t just about faster computers or smarter machines; it’s about creating technology that truly understands people. Real progress happens when technology cares about local needs and listens to real-life stories.

The same sickness can feel very different depending on where you live. A fever in a remote village is not like a fever in a big city. AI in healthcare must learn to see these differences and respond with care that truly fits.

As we build new healthcare tools, here’s an important question to think about: Are we creating solutions for everyone, or only for the people who already have good healthcare?

In the future, should we focus more on AI in hospitals or on understanding real human needs? Tell me your thoughts in the comments, I’d love to hear them!