The biosensor industry is experiencing a profound shift. For more than a decade, the vision for digital health centred on point-of-care diagnostics: rapid tests, easy-to-use sensors, and measurements that could be taken at home or at the point of need. Today, however, a new paradigm is emerging. The future is increasingly defined by continuous sensing, wearable technologies, implantable biosensors, and artificial intelligence that can interpret physiological data in context.
At Zimmer and Peacock (ZP), this evolution mirrors our own journey. When we started in 2014, our focus was largely on point-of-care diagnostics and wearable biosensors. In 2026, while point-of-care testing remains important, it is clear that continuous physiological monitoring is becoming the foundation upon which future AI-driven healthcare systems will be built.
From Point-of-Care to Continuous Monitoring
Traditional point-of-care diagnostics provide snapshots of biology.
A blood glucose strip, for example, tells you your glucose concentration at a specific moment in time. The technology is powerful, proven, and has helped millions of people manage diabetes. Yet it only captures an instant of physiological activity.
By contrast, wearable biosensors such as Continuous Glucose Monitors (CGMs) provide a stream of data throughout the day and night. Instead of four or five measurements per day, users can collect hundreds of data points every 24 hours.
This distinction becomes critical when we consider artificial intelligence.
AI systems thrive on data. More specifically, they thrive on trends, patterns, relationships, and context. Continuous data provides exactly that.
A single glucose measurement answers the question:
What happened?
Continuous monitoring begins to answer:
Why did it happen?
and
What might happen next?
The Banana Experiment: Why Context Matters
To illustrate the limitations of point measurements, consider a simple experiment.
A healthy glucose reading was first obtained using a conventional finger-prick blood glucose meter. The reading was approximately 90 mg/dL.
Next, a piece of banana was handled immediately prior to taking another glucose measurement.
The result appeared alarming: over 400 mg/dL.
Clearly, eating a small piece of banana did not suddenly elevate blood glucose to diabetic crisis levels. Instead, residual glucose from the fruit contaminated the blood sample and produced an artificially elevated reading.
Meanwhile, a Continuous Glucose Monitor worn on the arm continued reporting stable glucose values within the normal range.
The lesson is clear.
Point measurements can be vulnerable to contamination, user error, or incomplete context. Continuous sensing provides a broader physiological picture that is often more informative and resilient.
This does not invalidate point-of-care diagnostics, but it demonstrates why continuous monitoring is becoming increasingly valuable.
What AI Can Teach the Biosensor Industry
Interestingly, some of the most important lessons about the future of biosensing are emerging from the AI community.
Researchers such as Yann LeCun have been discussing the limitations of large language models and exploring new AI architectures, including Joint Embedding Predictive Architectures (JEPA).
A central idea behind these approaches is the importance of understanding cause-and-effect relationships in the physical world.
A bouncing ball provides a useful example.
Humans intuitively understand where a ball will go next because our brains have developed internal models of physics. We understand gravity, momentum, and motion, even if we do not consciously calculate them.
AI researchers are now attempting to build systems that learn similar predictive models of reality.
The same principle applies to physiology.
Glucose levels rise and fall due to food intake, exercise, illness, stress, sleep quality, medication, and countless other variables.
Just as a ball's trajectory depends on physical forces, physiological signals depend upon biological forces.
The challenge is that biology is significantly more complex than physics.
The Missing Ingredient: Context
A glucose trace alone provides only part of the story.
Suppose glucose rises sharply after lunch.
Why did it rise?
- Was it due to a high-carbohydrate meal?
- Poor sleep the previous night?
- Reduced physical activity?
- Stress?
- Illness?
Without context, the signal is incomplete.
This is where multimodal wearable technologies become important.
Modern consumer health devices capture a growing range of physiological and behavioural signals:
- Heart rate
- Heart rate variability
- Sleep quality
- Movement and activity levels
- Recovery metrics
- Respiratory parameters
- Environmental interactions
Examples include:
- Abbott Freestyle Libre
- Dexcom CGM systems
- Oura Ring
- Whoop
- Apple Watch
- Samsung Galaxy Watch
- Fitbit
When these data streams are combined, they begin to create the context necessary for more sophisticated AI models.
The biosensor provides the effect.
The broader wearable ecosystem provides clues about the cause.
Together, they form the foundation for AI-driven physiological understanding.
Industry Giants Are Already Converging
This vision is not theoretical.
Many of the world's largest health technology companies are already moving in this direction.
Strategic relationships between glucose-monitoring companies and consumer wearable companies illustrate the trend.
Examples include collaborations and investments involving:
- Abbott and Whoop
- Dexcom and Oura
- Apple's health ecosystem
- Samsung's non-invasive glucose initiatives
- Google's health integrations
These organisations recognise that isolated data streams have limited value.
The future lies in data fusion.
Combining glucose, sleep, activity, cardiovascular measurements, and environmental information creates a more complete model of human physiology.
This multimodal approach is precisely the type of ecosystem that future AI systems will require.
Beyond Wearables: The Rise of Implantable Biosensors
Wearables are not the final destination.
Beyond wearables lies an even more powerful category of technology: implantable biosensors.
At Zimmer and Peacock, implantable sensing has been an area of active development for many years.
Research programmes have involved implantable glucose sensors in fish and, more recently, studies involving pigs.
These systems continuously monitor physiological changes over extended periods and provide valuable insight into biological processes.
The long-term vision is relatively easy to imagine:
- Extremely small implantable sensors
- Multi-year operational lifetimes
- Minimal maintenance requirements
- Continuous physiological monitoring
- AI-driven interpretation of health status
An implantable biosensor could potentially become part of a permanent human-AI interface, enabling healthcare systems to monitor health continuously rather than reactively.
While significant technical, regulatory, and safety challenges remain, the direction of travel is increasingly clear.
The Human-to-AI Biosensor Interface
Today, most healthcare remains episodic.
A patient feels unwell.
They visit a clinician.
A measurement is taken.
A diagnosis is made.
Future healthcare may look very different.
Instead, biosensors may continuously monitor physiology in the background, creating real-time digital representations of human health.
AI systems could identify trends long before symptoms emerge.
They could help explain why physiological changes occur.
They may ultimately help predict what happens next.
Wearable biosensors provide the first layer of this future.
Implantable biosensors may provide the second.
Artificial intelligence will be the mechanism that turns data into understanding.
Conclusion
The biosensor industry is transitioning from isolated measurements to continuous physiological awareness.
Point-of-care diagnostics remain valuable, but the momentum is clearly moving toward wearable and implantable technologies capable of generating rich, contextual datasets.
At the same time, advances in AI are creating new opportunities to interpret these data streams in ways that were previously impossible.
The convergence of biosensors, wearables, implantables, and AI is creating something far larger than any individual technology.
It is creating a genuine human-to-AI biointerface.
For organisations developing next-generation biosensors, wearable devices, implantable technologies, or AI-enabled healthcare platforms, this convergence represents one of the most exciting technological opportunities of the coming decade.
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