What are the barriers to algorithm adoption among physicians and healthcare facilities and how can they be overcome?
We all know the progress and speed with which Artificial Intelligence (AI) is entering different sectors. Thanks especially to deep neural networks such as Machine Learning and Deep Learning, algorithms to support healthcare practice, i.e., public health, are being commercialised.
Despite many steps forward, only a few AI-based tools have actually been implemented in the various healthcare systems.
Lino Mari, Head of Technology at Healthware International, looked at the main reasons for this – transparency, data quality, and the ability of doctors and patients to rely on algorithms.
The quality of the data source and thus of the data itself is one of the main concerns of those in healthcare environments dealing with technology. Indeed, it is not always possible to establish the quality of the data and access to the algorithm source code.
In addition, there are still not enough published studies to directly support algorithms that have been tested in silico and may not reflect clinical practice.
How do you establish data quality? In the tech world it is often said: garbage in, garbage out.
