In November of 2022 I attended the Women in Machine Learning workshop in New Orleans. At this workshop I presented a poster summarising my current work in the detection of voice disease using artificial intelligence. This work used two different methods to classify patients with voice diseases from healthy controls from recordings of the patient’s speech. The two methods were 75.5% and 69.8% accurate when classifying new data. In order to simulate a real environment these new speech recordings were overlayed with backgroud noise and then put into the classifier again. The accuracy of the classifiers dropped to 66.0% and 62.3% respectively. By adding backgroud noise to the recordings the accuracy of the classfiers decreased and were more likely to classify healthy patients as having a voice disease.