I’ve spent years watching one of the most important forms of medical data go largely unmeasured.

In neurology clinics around the world, physicians still rely heavily on visual observation to assess disease progression. A neurologist asks a Parkinson’s patient to walk across the room. A tremor is graded on a scale. A hesitation in movement becomes a note in a chart. Critical treatment decisions are often based on episodic observations that can vary depending on the clinician, the environment, or even the time of day.

That reality struck me as one of the largest unstructured data problems in healthcare.

Human movement contains clinically meaningful neurological signals. Until recently, however, we lacked the technology to capture, quantify, and interpret those signals continuously and objectively.

That’s why we built Dannce.

Our mission is to transform ordinary smartphone video into clinical-grade neurological intelligence.

We’re building an AI platform that can analyze movement disorders using computer vision and machine learning, giving clinicians meaningful insight into how patients are doing between visits rather than relying on isolated snapshots from occasional office appointments.

The need for this is enormous.

Today, movement disorder care remains highly fragmented, leaving millions of patients deeply underserved. Only 9% of patients with Parkinson’s disease are able to see a movement disorder specialist¹, and even those who do are often only able to see one once or twice per year. Between visits, clinicians have almost no visibility into how symptoms are evolving, whether medications are working effectively, or when interventions are needed.

At the same time, neurological disease is becoming one of the defining healthcare challenges of our generation. Parkinson’s is the fastest-growing neurological disorder in the world, a rise researchers have described as a “Parkinson pandemic.” ² As populations age, the number of patients continues to grow while the global shortage of neurologists worsens.

We believe AI can fundamentally change that equation.

At Dannce, patients perform guided movement assessments through a smartphone application.

We’re developing AI systems that analyze those videos to detect subtle abnormalities in gait, tremor, coordination, posture, and motor function that are often difficult to quantify consistently with the human eye alone.

The result is something we think healthcare has been missing for decades: objective, longitudinal movement data.

Instead of relying solely on subjective in-clinic evaluations, physicians can begin to see how a patient is actually functioning over time, across days, weeks, and months. That longitudinal visibility has the potential to improve treatment optimization, detect deterioration earlier, and ultimately create a far more proactive neurological care model.

Technically, what we’re building sits at the intersection of computer vision, biomechanics, and clinical neuroscience.

I often describe Dannce as building a foundation model for human movement. Just as large language models learned to understand patterns in text, we’re training systems to understand patterns in motion.

Every step, turn, tremor, and gesture contains signals. Our goal is to create the infrastructure layer capable of decoding those signals into clinically meaningful insights.

And while Parkinson’s disease is an important initial focus, we see the opportunity extending far beyond a single condition.

Movement abnormalities exist across a broad range of neurological disorders, including tardive dyskinesia, dystonia, multiple sclerosis, stroke rehabilitation, and Huntington’s disease.

Over time, we believe quantitative movement analysis could become a foundational diagnostic layer across medicine.

What has been especially encouraging is the response from clinicians.

Within a short period, we have secured multiple pilot commitments tied to leading movement disorder programs and healthcare institutions. The feedback has been remarkably consistent: physicians want visibility into patient progression between visits, and they recognize that today’s tools are insufficient.

For providers, this is not simply about convenience. It’s about creating a new standard of care.

Healthcare is increasingly moving toward remote monitoring, longitudinal engagement, and value-based reimbursement. In that world, more frequent and objective neurological measurement becomes critically important. Systems that help specialists and primary care providers coordinate care around a shared understanding of disease, enabling earlier intervention, more effective therapy optimization, and fewer hospitalizations, will become essential infrastructure.

I believe we’re still in the very early innings of AI in healthcare.

Over the past two decades, healthcare digitized records, imaging, and administrative workflows. But human movement, despite being one of the richest biological data streams available, remained largely inaccessible computationally.

That is finally beginning to change.

The smartphone camera is evolving into something much more powerful than a communication device. It’s becoming a neurological sensor.

At Dannce, we believe the future neurological exam will not happen exclusively inside the clinic. It will increasingly extend into the home through more frequent, guided assessments using devices people already carry every day.

And when that future arrives, clinicians will no longer rely only on intermittent observation and subjective scoring systems to understand disease progression.

They’ll have objective movement intelligence available in real time.

That’s the future we’re building toward at Dannce.