Leveraging Academia to Find the Next Product

1. “What to Build Next?”

Every builder, regardless of domain, relies on signals to decide what to build next.

If you are a mobile app developer, you monitor App Store rankings, SensorTower data, and Reddit communities to identify fast-growing categories and unmet demand. If you build web or SaaS products, Product Hunt, Indie Hackers, and X act as real-time feeds of what others are experimenting with. In more mature software markets, these signals are often enough—by the time something shows traction, it is already buildable, testable, and monetizable.

Biosensor-based wearables and Health AI are fundamentally different.

In these domains, market signals appear too late. By the time a health product shows up on charts or earns widespread attention, the most important technical decisions—sensing modality, data availability, validation pathway—have already been made years earlier. Unlike consumer software, where features can be shipped weekly, health products are constrained by biology, physics, regulation, and long validation cycles.

In this context, builders need a different kind of signal source—one that operates before markets form, before products exist, and often before users know what to ask for.

That signal source is academia.

2. Academia as an Early Signal Source

For builders in health AI, academic research labs function less like “ivory towers” and more like upstream infrastructure for future markets.

Long before a product is possible, labs are already answering the most critical questions:

• Can this physiological signal be measured at all?

• Can it be measured continuously?

• Can it be measured non-invasively?

• Can it survive real-world noise and human behavior?

These questions determine whether a product category can exist—not whether it will be popular, but whether it is technically and biologically feasible.

What matters is not whether a specific paper becomes commercialized, but whether a class of capability crosses from theoretical to practical:

• New sensing modalities

• New biological proxies

• New algorithmic primitives

• New validation frameworks

Seen this way, academia operates as upstream infrastructure. It quietly defines the boundaries of what future products can plausibly exist, often years before demand signals appear.

3. Categorization of Health AI Research

To make academic research usable as an innovation signal, it helps to categorize it not by universities or disciplines, but by the kind of product capability it unlocks.

New Physiological Signals

Some labs focus on unlocking signals that were previously inaccessible: hormones, metabolites, inflammatory markers, or stress-related biomarkers. These signals often sit closer to the root causes of health outcomes than traditional metrics like steps or heart rate.

The new signals don’t just improve existing products—they create entirely new product categories. A capability to measure something continuously often precedes any clear consumer demand for it.

New Sensing Interfaces

Other labs work on how signals are captured: sweat-based sensors, skin-like electronics, flexible materials, or minimally invasive sampling. These advances don’t change what is measured, but radically change how often and how comfortably it can be measured.

The interface innovation often determines whether a product remains niche or becomes mass-market. Frequency, friction, and comfort are as important as accuracy.

System-Level Wearables

Some research focuses on full systems rather than isolated sensors—long-term deployments, sensor fusion, power management, reliability, and real-world robustness.

This category matters because many promising signals fail not in the lab, but in daily life. Companies care deeply about this layer, because it defines whether a product survives outside controlled environments.

Health AI and Algorithmic Abstractions

Another cluster of labs works on digital biomarkers, multimodal models, and foundation-style approaches to health data. These efforts aim to turn raw signals into higher-level representations that can generalize across users, devices, and conditions.

These abstractions often compress complexity. They make it possible to build richer products without reinventing analysis pipelines from scratch—but they also threaten to commoditize certain forms of differentiation.

Clinical and Population Validation

Finally, some institutions specialize in validating technologies across large or specific populations: women’s health, pregnancy, chronic disease, or aging.

This category determines credibility, trust, and long-term defensibility. A product may work technically, but without validation, it rarely survives regulatory or clinical scrutiny.

Taken together, these categories turn academic research into a map of emerging product space, rather than a collection of isolated papers.

4. Research Frontiers Worth Watching

Using the above framework, certain research frontiers stand out to me—not because they are trendy, but because they unlock structural shifts in what can be realistically created.

Predictive Continuous Hormone Sensing

Research into non-invasive or minimally invasive, continuous hormone monitoring represents a transition from confirmatory health tracking to predictive systems. Instead of explaining what already happened, products can anticipate what is about to happen.

This changes the entire user value proposition—from reflection to decision-making.

Flexible and Skin-Integrated Electronics

Advances in materials and form factors point toward wearables that feel less like devices and more like part of the body. This matters because adoption in health is often limited not by usefulness, but by burden.

The invisibility and comfort may unlock user groups that were previously unreachable.

Foundation Models for Personal Health

Large-scale, multimodal health models trained on longitudinal wearable data suggest a future where insight generation becomes increasingly standardized. This raises hard questions for builders: what remains proprietary, and what becomes infrastructure?

The opportunity lies not just in better models, but in how insights are contextualized, delivered, and acted upon.

Summary

Academic research is not a replacement for market insight—but in emerging health categories, it is often the earliest place where the shape of future products becomes visible. Learning how to interpret it, selectively and critically, is not merely an academic exercise. It is a practical skill for anyone trying to build meaningful health technology before the market fully arrives.

A candidate list to watch for: