Healthineering builds AI and wearable-driven tools that turn continuous, everyday signal into earlier cardiovascular risk detection — founded by a practicing cardiologist, grounded in published clinical research.
Most cardiovascular risk is assessed episodically — an annual check-up, a single ECG, a lipid panel every few years. The disease doesn't work that way, and increasingly, neither does the data available to track it.
The shift toward continuous, AI-supported cardiovascular care is a global one — but the UAE and wider GCC are moving with unusual speed and capital behind it, making the region an exceptional base from which to build and validate before scaling internationally.
The UAE's National Strategy for Artificial Intelligence 2031 has been backed by more than $2 billion in public AI investment over the past decade — a policy and capital environment few markets can match for validating clinical AI at speed.
We use the term Transformational AI-Care to describe the shift our work is built around: from reactive, episodic cardiology to continuous, predictive, clinician-guided care — powered by AI that is designed with clinical practice in mind from the outset, not retrofitted onto it. This is where we believe the category, and Healthineering's advantage within it, is heading.
Led by a practising, published cardiologist with formal AI training from Stanford and Harvard — a rare combination that shapes every product decision from the inside, not from the outside looking in.
Headquartered and operated from the UAE, built from day one for the regulatory, linguistic and cultural realities of GCC health systems — with the architecture to scale to global markets once validated.
A transparent, staged validation roadmap rather than premature claims — held to the same evidentiary standard as our founder's own peer-reviewed publication record.
The next decade of cardiology will be defined less by new hardware and more by how intelligently we use the signal we already collect — from the echo probe to the wrist.
Wearable ECG, PPG and activity data are rich enough to support earlier, more individualised risk detection than most current workflows use. The opportunity is extracting clinically meaningful signal from continuous, everyday data — not just episodic snapshots.
Combining wearable-derived signals with clinical history and imaging findings to move cardiology from episodic, reactive evaluation toward personalised, continuous risk profiling — surfacing deterioration earlier, before symptoms present.
Decision-support tools should sit naturally alongside the clinician, not replace clinical judgement. Every model we build is designed to be trusted and used by the physicians who rely on it — not just benchmarked in a paper.
We're early. Rather than overstate where we are, here's our actual roadmap — the same evidentiary bar our founder's own published cardiology research was held to.
Every concept starts from a documented clinical gap — including our founder's own peer-reviewed research on drug-induced QTc prolongation — plus a structured review of existing published datasets and prior art.
Defining a small-scale, single-site pilot protocol with clear endpoints, in collaboration with clinical partners — the step where a concept starts generating real, prospective data rather than modelled data.
Expanding validated pilots across multiple sites and patient populations — the bar any tool needs to clear before it belongs anywhere near clinical decision-making.
Early-stage concepts, not shipped products — shown here as design direction, not availability.
Continuous wearable-ECG detection of drug-induced QTc prolongation in patients on interacting medications — extending our founder's own published research into a real-time, always-on model.
A dynamic risk model combining wearable-derived signals (activity, HRV, sleep) with periodic echo findings to flag decompensation days before symptoms present.
Device-agnostic middleware that normalises signal quality across consumer wearable brands, so continuous monitoring doesn't depend on which device a patient happens to own.
Perspectives from the intersection of clinical cardiology and applied AI — the questions we believe matter most in translating research into responsible clinical practice.
A single annual ECG catches almost nothing compared to a month of continuous rhythm data. The clinical case for always-on monitoring is stronger than the tooling to support it — that gap is the opportunity.
The limiting factor in wearable cardiology isn't model accuracy — it's motion artifact, poor skin contact, and inconsistent sensor placement. The unglamorous engineering problem is the one worth solving first.
An AI cardiology tool designed without its clearance pathway in mind from day one is a demo, not a product. Clinical validation and regulatory strategy have to be designed alongside the model, not after it.
Rapid private healthcare investment and high consumer wearable adoption make the UAE and wider GCC an unusually fertile — and underexplored — environment for piloting AI-driven cardiology tools.
Healthineering is led by Nico Monadian, MD — cardiology-trained with subspecialty training at Erasmus Medical Centre, a peer-reviewed published researcher, and formally trained in AI in healthcare at Stanford and Harvard.
We are engaging with clinical pilot partners, research collaborators, and institutional investors who share our commitment to rigorous, clinically-grounded AI development.