Psychiatry includes the assessment and diagnosis of illness and disorder within a largely interpersonal communicative structure involving physicians and patients. In such contexts, AI can help to spot patterns and generate predictions, e.g. using ‘big data’ analysis via statistical learning-based models. In these ways, AI can help to automate more routine steps, improve efficiency, mitigate clinician bias, offer predictive potential, including through analysis of neuroscientific data.

This presentation is situated within the MyDigiTwin (MDT) consortium, a research project aiming to create a Digital Twin (DT), where Dutch citizens, including patients, can compare their health data (e.g., heart rate, weight, exercise) to existing big datasets. The platform will implement Artificial Intelligence (AI) models to predict a person’s risk of cardiovascular disease (CVD).

Recent advancements in artificial intelligence (AI) have demonstrated significant promise in the field of medicine. From disease diagnosis to personalized treatment plans, AI has the potential to revolutionize the healthcare industry. However, as with any emerging technology, there are questions about how to quantify the benefits and trade-offs of AI in medicine. One of the biggest challenges in assessing the benefits of AI in medicine is determining how to measure “uncertainty”. Biomedical and computer engineering define and measure uncertainty differently.