By Jenni Le, Principal, Venture Investors Health Fund

A patient suspected of sleep apnea used to spend the night in a lab, hooked up to sensors, trying to sleep in an unfamiliar environment, often producing a poor representation of a typical night.

Today, that same patient can sleep in their own bed, wearing a ring that monitors their sleep while a platform like Ensodata captures waveform data across multiple nights. Instead of a single snapshot, clinicians receive a richer dataset—meaningful trends that better reflect how that patient is actually doing.

That shift from static information to longitudinal insight is one of the clearest signals that diagnostics are entering a new era.

Diagnostics have always been central to precision medicine. But the conversation is shifting. It’s no longer just about detecting something earlier—it’s about whether that information meaningfully changes what happens next.

For us as investors, that distinction matters. A diagnostic doesn’t create value simply because it produces a result. It creates value when it informs an intervention that improves outcomes.

AI is everywhere in diagnostics right now. But is it actually improving care or just generating more data?

• What makes a diagnostic truly valuable?
• Is earlier detection always better?
• Why do so many promising tools stall at reimbursement?
• And as continuous monitoring becomes the norm, are we empowering patients or creating a generation of the “worried well”?

These are the questions shaping Precision Medicine 2.0.

Below are a few of the shifts we’re seeing at Venture Investors Health Fund, and the standards we believe diagnostics must meet in this next era of precision medicine.

Where AI Meaningfully Improves Diagnostics (and Where It Doesn’t)

In essence, AI is about pattern recognition in datasets that are too difficult for a human to manually parse through. AI will only meaningfully improve diagnostics if the interpretation of a result actually informs some type of intervention that can move the needle on outcomes.

It’s absolutely not about replacing doctors. It’s about helping clinicians triage what’s important to pay attention to and when. Clinicians should always have the final say. These tools are meant to aid in diagnosis, not make the final call.

What tends to be overhyped is AI replacing clinical judgment. Any “black box” diagnostic test that claims to use AI needs to be able to explain what it’s doing. Without that clear explanation, adoption will be challenging. And the tool needs to provide real value. An AI diagnostic for something that, in the vast majority of cases, can be determined by a simple blood test isn’t going to succeed.

Precision medicine isn’t about adding complexity. It’s about adding clarity.

Diagnostics That Change What Happens Next

The most powerful diagnostic tools don’t just detect something—they change downstream decision-making. A strong example is EarliPoint, whose tool aids clinicians in the diagnosis of children suspected of having autism.

While the waitlists to obtain an autism diagnosis are heartbreakingly long, another significant pain point is measuring whether treatments are actually working. EarliPoint allows clinicians to repeatedly measure whether a child is responding to therapy. The output is a report showing a child’s verbal and non-verbal skills, helping treatment providers understand where that child may need additional attention.

This is, in essence, a tool that helps clinicians tailor therapy to a child’s specific needs. That’s what precision medicine should do.

Reimbursement: The Constraint That Can Sink Innovation

There are many constraints holding diagnostic innovation back: clinical adoption, workflow integration, reimbursement. But in practice, reimbursement can sink a tool if there is no clear path for the clinician to get paid for using it.

A company needs to be able to perform an economic analysis that shows upside for the clinic or health system. It is incredibly difficult to sell efficiency gains alone. In diligence conversations, efficiency is almost never high on the list of “must haves” unless there is clear, demonstrable economic evidence. Clinical value and economic value must go hand in hand.

Actionable Insight vs. Additional Data

From an investor’s perspective, the key question is whether a diagnostic meaningfully changes how a patient will be treated. Does it provide actionable insight?

A common example is early detection of Alzheimer’s risk. If someone learns in their 20s that they are likely to show signs much later in life, what meaningful, evidence-based intervention can they take to change that outcome? Or does that information simply create unnecessary worry?

Not every new data point advances precision medicine. Some may simply add anxiety.

​From Snapshots to Movies

We are leaving a world where snapshots dominate and moving toward watching movies.

In other words, we are headed toward a future where continuous monitoring opens up richer views into our individual biology. Whether through wearables, smart implants, or increasingly affordable biomarker tests, patients will be empowered with more data about their bodies than ever before. That’s incredible, and a little scary.

We need to balance this shift with evidence, rather than driving people toward becoming the “worried well.” Too much data can lead to unnecessary stress.

​Defining Precision Medicine 2.0

Precision medicine 2.0 provides continuous biological data to manage patients in real time and longitudinally.

It matters because we’ve never had this level of compute power to allow clinicians the tools to intervene earlier (perhaps even before significant symptoms emerge) and to provide healthcare in a way that focuses on keeping patients well before things fall off the rails.

The opportunity now isn’t just better detection. It’s better intervention, informed by data that truly changes care.

Stay tuned for more insights on Precision Medicine from the VI team.