Challenges for AI solutions in healthcare

Why are the expectations for AI solutions in healthcare sky high, but only few of them received authorisation and even fewer are used in the clinical setting? The implementation of AI in clinical practice is extremely challenging. In this fifth blog in a series of AI we discuss the 8 biggest challenges and recommendations in data organisation and integration of solutions into an existing workflow.

AI Challenge: Data

The most crucial and challenging step for developing and training AI solutions is having large amounts of compatible high-quality data that guarantees privacy and security. For healthcare specifically this comes with several challenges. Currently, there are still countless clinical processes, patient data and patient reports that have not been digitalised. Those that are digitialised, often are unintegrated. We have defined the 8 biggest challenges in implementing AI in clinical setting:

1. Quality of the data

One of the risks of applying AI to clinical issues specifically is discrimination and introduction of bias. The quality of algorithms highly depends on the quality of the population it is trained and tested on, which needs to be representative. Issues will rise to any sub-population under-represented in the training data, as the AI model performs better on the group used to train it.

 

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