Healthcare today is broken.
Today, diagnosis depends on memory, intuition, and whatever doctor is available at that moment.
Some clinicians diagnose brilliantly. Others miss critical signs. Many are exhausted, rushed, or unreachable.
Billions lack access to expert reasoning, and millions are harmed not because medicine lacks answers —
but because access to those answers is uneven, unpredictable, and fragile.
Diagnosis shouldn’t depend on geography, guessing, or a doctor’s energy at 3AM.
It should be systematic. Explainable. Reliably consistent. Universal.
Our Vision
We are building a Structured Medical Reasoning Engine — not a chatbot, not a symptom checker, note a black box LLM, but a system that represents clinical logic in a transparent, auditable, continuously improving framework.
A system that works alongside clinicians now — and scales expertise to the world over time.
The engine will:
guide patients to understand symptoms clearly
suggest likely pathways of investigation
surface risk patterns and red flags
recommend appropriate urgency and care level
evolve into clinically validated differential generation & reasoning support
operate under medical supervision, regulation, and evidence
We are removing diagnosis as a bottleneck and letting clinicians focus on treatment and care.
Our system becomes the first-pass cognitive layer — fast, consistent, scalable.
The Roadmap
Phase 1 — Symptom Reasoning + Education (Now)
A structured tool that helps people understand symptoms, risk factors, and when to seek care.
Insight, not diagnosis. Safe, accessible, and trust-building.
Phase 2 — Clinical Support Layer
Differential suggestions, reasoning chains, and test recommendations with human oversight and validation.
Phase 3 — Diagnostic Co-Pilot for Clinicians
A second set of eyes. A cognitive safety net.
Something to challenge assumptions, catch misses, reduce mental overload.
Phase 4 — Scalable Diagnostic Automation
With evidence, regulation, and real-world outcomes —
the system handles appropriate cases autonomously.
Care multiplies instead of bottlenecking.
Why
Diagnostic error is massive, does not scale but is solvable
400,000–800,000 serious misdiagnosis-related harms per year in the U.S. alone (Johns Hopkins, BMJ Quality & Safety, AHRQ).
~1 in 3 malpractice dollars are paid for diagnostic error.
In primary care, ~5–12% of cases are misdiagnosed; in emergency departments it’s even higher for certain presentations (stroke, sepsis, aortic dissection, etc.).
Autopsy studies still show 10–20% of deaths have a major missed diagnosis. That hasn’t budged in 70 years.
Access is catastrophic in most of the world — One clinician can treat dozens. Our system can serve millions.
4–5 billion people lack access to basic surgical/medical expertise, let alone good diagnostic reasoning (Lancet Commission on Global Surgery & WHO).
In low-income countries, the average doctor-to-population ratio is <1 per 10,000 in many regions. Even where doctors exist, they’re often undertrained in complex reasoning.
Current “AI doctor” solutions are not fixing this
ChatGPT-style tools are impressive at conversation but fail badly on edge cases, can’t reliably show chain-of-thought that holds up in court, and are banned or heavily restricted in most regulated settings.
Traditional symptom checkers (Isabel, Ada, Babylon (bye), etc.) have been around for 15–20 years and still have single-digit adoption by physicians because they’re shallow, annoying, or wrong too often.
Clinicians are burning out on the cognitive load — they are the custodians of care, we will exten their reach globally, consistently, 24/7
Primary-care doctors make 500–1,000 clinical decisions per week.
Cognitive overload is a top driver of burnout. A tool that reliably takes the first structured pass at history-taking and differential generation would be welcomed, not feared.
The regulatory path is now open for exactly this kind of system
FDA and EMA have cleared dozens of “clinical decision support” SaMD products in the last 5 years.
Transparent, rule-based or knowledge-graph systems (the opposite of black-box LLMs) are dramatically easier to get through 510(k) or De Novo pathways.
A structured, explainable, continuously validated reasoning engine is the only credible way to:
Reduce diagnostic error at scale
Bring high-quality reasoning to underserved populations
Survive regulation and liability
Actually get adopted by doctors instead of being another toy
Turn healthcare equity into reality, not theory.
The industry knows this.
No one has built it properly — yet.
We will.