The Limitations of AI

AI is transforming healthcare, but it has clear limitations. One of the biggest limitations? Data quality and standardization.

In a recent discussion with Mark Mayeaux, CEO/Founder of Equiptrack, a leader in the medical equipment data and informatics space, the AI discussion came up. Knowing what we do from our internal tech team, his comments put a bow on it, “AI cannot process or parse complex, critical, non-formatted medical equipment data.”

In medical equipment inventories and appraisals, data collection in a facility is full of variables. Patient care dynamics, variations of manufacturers, models, serial numbers, conditions and more create the need for expertise and experience to drive data quality. Without this, AI may produce results that on the surface look precise, but are far from
reliable. Think of trusting the value of your enterprise to a search engine – the internet casts a wide variety of results as we all know. We’ve seen these results in action…

For hospital leadership, this is a critical reminder: AI should support decision-making, not drive it blindly. The real value comes when technology is paired with experienced professionals who understand how to collect, standardize, and interpret the data in context.

In capital planning, asset management, and budgeting, accuracy isn’t just important – it’s non-negotiable.