Our Founder, Eyad Al-Musa, on the hidden cost of misdiagnosis.
In my years advising healthcare leaders across systems, regions, and regulatory environments, one theme keeps resurfacing: delayed or misdiagnosis is still one of the most expensive — and least solved — problems in medicine.
Not because we lack data, but because most MedTech systems that are currently used are too monolithic and unwieldy for doctors to find the right and relevant information. Most systems lack reasoning — the ability to connect that data meaningfully, transparently, and in real time.
Breakthroughs in AI, interoperability, and digitized medical evidence have created the foundation for a new era of clinical reasoning. Success now depends on building AI that is:
- Reliable: Deterministic, evidence based and consistent
- Intuitive: fits naturally into how clinicians work
- Explainable: Grounded in evidence that is trustworthy and verifiable
- Seamless: Integrates with existing systems and clinical workflows.
Misdiagnosis: A Systemic, Expensive Blind Spot
Here’s what the data tells us:
- Over 795,000 people suffer serious harm or death each year due to diagnostic errors in the U.S. alone
- Diagnostic errors contribute to over $100 billion in annual costs in the U.S. — from litigation, redundant procedures, prolonged hospital stays, and inefficiencies
- The OECD estimates 17.5% of total healthcare spend in developed nations is attributable to mis-, over-, or under-diagnosis. Halving diagnostic errors could reduce national spending by 8%.
- For many conditions, the financial impact is measurable: In a recent study published by Wiley Periodicals LLC on behalf of American Headache Society, shows that patients with migraine with a history of misdiagnosis tend to incur significantly higher inpatient costs and make more frequent use of healthcare services compared to those with accurate diagnoses.
These aren’t edge cases. They’re the result of systemic design gaps; where healthcare systems present fragmented data but fail to support the reasoning needed to make sense of it.
Why Existing Tools Fall Short
Having spent over a decade designing and executing digital health strategies for providers, payers, and regulators, I can say with confidence: the bottleneck is no longer data access, it’s how we support clinicians in reasoning through it.
The real challenge isn’t access to information; it’s how current tools fall short in helping clinicians synthesize EHRs, labs, imaging, and notes into a coherent, explainable picture of the patient. This is further compounded by the fact that non-clinical data such as SDoH and wearables is ignored thus significantly reducing the opportunity of improving the accuracy in diagnoses and tailored treatment plans.
Many solutions — ambient scribes, symptom checkers, even LLMs — focus on surface-level utility. What’s missing is a platform that offers:
- Transparent reasoning
- Context-aware diagnosis support
- Traceable outputs clinicians can trust
What is Centyent building
Centyent is not another medical chatbot or voice assistant. It’s a clinical reasoning engine built using Neuro-symbolic AI concepts, designed to:
Build Multi-Dimensional Problem Lists
Understand not just what the patient presents, but how their symptoms, history, and context interact to elevate risk, confound clarity, or mask serious issues.
Offer Fully Explainable Outputs
Centyent’s AI Platform does not make “guesses.” Every recommendation comes with a traceable reasoning path and evidence-backed support.
Embed Directly Into Existing Workflows
From EHRs to clinical portals, Centyent is designed to integrate, not replace. The aim is to reduce diagnostic delays.
Learn From Every Interaction
Centyent’s AI platform continuously adapts based on real clinical feedback, improving with each use and aligned with evolving standards of care.
Why This Is a Business Opportunity
For investors and health system leaders, this is not just a clinical innovation, it’s a scalable business lever.
Recapture Hidden Value: Up to 15% of diagnoses are estimated to be flawed — correcting even a fraction of that gives access to huge financial upside.
Mitigate Risk: Transparent reasoning supports litigation protection, audit trails, and regulatory compliance.
Align With Value-Based Care: Improving diagnostic accuracy directly improves downstream outcomes, reimbursement alignment, and patient trust.
High Barrier to Entry: Neuro-symbolic AI tuned for healthcare is a deep-tech space. This isn’t an app, it’s a platform.
If you’re building, funding, or scaling healthcare transformation, let’s talk.
We’re open to early partnerships ahead of our launch in 2026 to co-develop with forward-thinking institutions and clinicians and care teams who believe in trusted, explainable clinical reasoning.
