Part 1 of 3: The Clinician's AI Dilemma: Big Tech vs. Big Heart

I've spent the last year designing, creating and testing software solutions to bring AI to clinical practitioners. Engineering robust, safe software is never easy; creating software that respects the depth and nuance of clinical training, a sensitive ear and a caring heart is at least one order of magnitude more complex.

The honest truth? So far, all these experiments have failed to deliver. The ideas were great, but 20 years in the profession will give you appropriately high standards. Creating software that we can trust to replicate skills it took us years to develop might be about as non-trivial a task as building up a human mind and body cell by individual cell.

All current AI solutions, even the premium tier, have some pretty glaring problems. Initially, they look great on the surface. But like any relationship, it takes time to see the truth. In healthcare, those details matter and like you, I am getting tired of the simplistic, money-grabbing takes.

The AI is improving all the time. But I haven't yet seen it successfully close in on this gap - if anyone is telling you otherwise, please send them to me. I'll see how well their offerings can withstand the full assault of clinically-informed, industry-grade software testing.

Wish them luck; they're going to need it.

Does this struggle resonate? Have you felt the gap between AI's current capabilities and the deep requirements of clinical practice? I would love to hear from you.

Tomorrow, in Part 2, I'll share the human-centric solution I've discovered after these frustrating dead ends – a simpler, more elegant, and powerful path forward.

#ArtificialIntelligence #DigitalHealth #Wellbeing #ThoughtLeadership #HumanCentredAI