For a long time, we thought the hardest part of building AI in healthcare was the model.
Turns out, it isn’t. It’s data. Always data.
Good healthcare data is rare. Even when it exists, accessing it is slow, restricted, and wrapped in layers of approvals. Privacy laws, internal policies, ethical concerns – all of them make sense, but together they create a real bottleneck. We’ve seen promising ideas stall simply because teams couldn’t get permission to use the data they needed.
That frustration is what eventually pushed us to take synthetic data seriously. Not as a replacement for real patient data – but as a way to keep moving forward when everything else was blocked.
What Synthetic Data Actually Means
Synthetic data is not copied from real patient records. That’s an important distinction.
It’s generated.
Usually, this happens by learning the structure of existing datasets – things like distributions, relationships, and patterns – then recreating those characteristics artificially. Sometimes it’s done with simple statistical methods. Other times, more advanced approaches like GANs or VAEs are involved.
The idea isn’t to recreate reality perfectly. The idea is to create something useful enough for experimentation, testing, and learning – without exposing anyone’s personal information.
When it works well, models trained on synthetic data behave surprisingly close to how they behave with real data.
Why Healthcare Keeps Running into the Same Data Problems
Healthcare data science has a few recurring issues that almost everyone runs into sooner or later:
- Data is highly sensitive and tightly regulated
- Certain populations and rare conditions are barely represented
- Labelling clinical data takes time and expert effort
- Data is scattered across systems and institutions
None of this is new. And none of it is easy to fix.
Synthetic data doesn’t magically solve these problems. But what it does offer is room to experiment. It allows teams to test ideas, validate assumptions, and stress models without constantly worrying about compliance at every step.
Sometimes, that breathing room is all you need.
Where Synthetic Data Has Been Genuinely Useful
From what we’ve seen, synthetic data tends to work best in specific situations:
- Early-stage model development, when real data isn’t accessible yet
- Medical imaging tasks, especially where examples are limited
- Testing EHR systems, dashboards, and alert logic
- Sharing datasets across teams without legal friction
- Addressing bias by generating underrepresented cases
It’s particularly valuable when the goal is learning, iteration, or validation – not direct clinical deployment.
Why Teams Keep Coming Back to It
There are some very practical reasons teams adopt synthetic data:
- No direct patient identifiers
- Lower cost compared to licensed datasets
- Easy to customize for specific scenarios
- Scales well for large experiments
- Fewer compliance hurdles
For startups and research teams, this often makes the difference between building something and staying stuck in approval loops.
The Part That Often Gets Ignored: Limitations
Synthetic data isn’t harmless just because it’s synthetic.
If the generation process is weak, important clinical patterns can be lost. Models trained only on synthetic data may also struggle when exposed to real-world complexity. That’s not a theoretical risk – it happens.
This is why validation against real data is non-negotiable. And why synthetic data should support real-world evidence, not replace it – especially in high-impact healthcare applications.
Another issue is standards. Right now, there’s no universal agreement on how to measure synthetic data quality. That makes evaluation inconsistent and sometimes subjective.
Where Things Seem to Be Headed
Despite the challenges, adoption of synthetic data in healthcare is clearly increasing. Not because it’s trendy – but because it’s practical. Regulators are also starting to acknowledge its role, particularly for privacy-safe research and testing. As generation methods improve, synthetic data is likely to support areas like federated learning, low-resource healthcare AI, and faster experimentation cycles. `It won’t replace real data. And it doesn’t need to.
Final Thoughts
Synthetic data isn’t a shortcut. And it isn’t a silver bullet.
But in a field where access to real data is often the biggest obstacle, it has become an essential tool. For anyone working in healthcare AI, learning how to generate, evaluate, and responsibly use synthetic data is no longer optional.
It’s part of the work now.
In that sense, synthetic data isn’t just a workaround.
It’s momentum.
Responsible AI starts with responsible data – and synthetic data helps close that gap.
