AI-Enabled Health Data

In the realm of healthcare, the integration of patient-reported data into clinical practice and AI modelling presents complex challenges. Ethical data collection is paramount, requiring stringent adherence to patient privacy and consent, amidst diverse and often sensitive personal health information. Coherent data management is equally critical, demanding sophisticated systems that can process and store heterogeneous data efficiently while maintaining quality and interoperability with existing healthcare infrastructure. Further, translating this data into effective clinical support tools necessitates a user-friendly design that provides actionable insights for healthcare professionals, a task that must be balanced with the ethical development and deployment of AI models. These models, while promising for analysing vast datasets, face challenges in ensuring clinical relevance, accuracy, and the mitigation of inherent biases. Addressing these multifaceted challenges is essential for harnessing the full potential of patient-reported data in enhancing healthcare delivery and patient outcomes.

Among the clinical conditions, migraine represents one of the biggest opportunities for impact. Migraine is a chronic neurological disease that characterises by recurrent episodes of headache associated with nausea and sensitivity to light or sound. Migraines are very prevalent and affect 1 in 7 Europeans. They can be debilitating and costly, with an estimated cost of €125 billion in Europe each year due to lost productivity and healthcare. There are currently no cures for migraine and existing treatments provide only symptomatic relief.