It’s fair to say that any system that is reliant on data is only as good as the data that has gone into it. In machine learning-based models, when trained on incorrect, underrepresented or biased data, the models can become biased themselves. This can exacerbate the biases that already existed in certain subsets of the data. Understanding AI bias is essential in ensuring accurate performance of machine learning models.
When it comes to AI in healthcare, the issue of data bias can lead to significant consequences. These include limiting access, inadequate care, and poor patient outcomes for underrepresented groups.
On 30th June, Zegami and SEHTA presented a taster webinar focused on the use of AI in healthcare. In this webinar, Zegami’s CEO Roger Noble discusses the issues that can arise with biased data, and how this leads to biased AI. Roger also takes you through strategies that can be employed to mitigate the effects of bias, driving better patient outcomes.