AI in healthcare does not fail because of weak models, but because implementation does not align with requirements. Whenever an AI tool is first introduced, and when an organisation sees a demonstration of it, the feeling is one of excitement. Testing reveals amazing initial results, but the focus is rarely on the end goal that is to be achieved. The process is generally ‘in progress’, until they day comes when the technology meets the reality of everyday work. And when this happens, the output starts becoming inconsistent. It does not meet the requirements it was brought in for. This is where trust drops, before the value of the tech is fully proven.

Compared to other industries, healthcare has generally been slower and more cautious in adopting AI. This is a field that deals with human lives, and one where there is very little tolerance for error. However, AI has made its way into healthcare spaces across the world. The American Medical Association (AMA) reported in 2025 that that 66% of physicians said they were already using AI, an increase from 38% in 2023, and 57% say their burden has been reduced through AI automation. While this is important, it also means that healthcare organisations are now under pressure to move from pilots to disciplined implementation.