HeAIth was born from a personal experience: successfully using AI to diagnose my own skin condition, avoiding months of NHS waiting and saving over £200 in private dermatology fees.

Like many people, I faced a frustrating medical situation. I had a persistent skin condition but couldn't get a timely NHS dermatology appointment. The wait times were months long, and private consultation fees were prohibitively expensive. As a software engineer researching AI applications, I applied the same diagnostic flow that now powers HeAIth to my own condition.

It worked. I identified the condition, researched the recommended treatment approach, and applied an over-the-counter solution. The condition cleared up completely, saving me time and money.

But then it hit me: this process could be refined and optimized into a powerful diagnostic system that could democratize healthcare for millions—not just in the UK, but worldwide. Our NHS doctors are working incredibly hard under immense strain, while patients with straightforward conditions wait months for care that could be addressed quickly.

I set to work immediately. Within a week, I had a working system. But as I tested it, I realized numerous issues—accuracy limitations, workflow friction, cost inefficiencies. So I started building a more advanced, accurate MVP specifically designed to integrate smoothly into doctor workflows while drastically cutting costs.

That's HeAIth today: a clinical decision support tool built to help hardworking NHS clinicians see more patients efficiently, provide faster reassurance to those who don't need complex treatment, and ensure those who do need urgent care get it sooner. Not replacing doctors—supporting them at the critical first moment of patient assessment.

I'm a software engineer, not a clinician. That's precisely why I'm assembling a Clinical Advisory Board of practicing GPs and frontline clinicians to guide every aspect of HeAIth's development.