Diagnosing blood cancers requires analyzing bone marrow cells under a microscope, a task challenged by doctor shortages and subjectivity. This project introduces a digital and AI-guided clinical platform to improve this. We will scan bone marrow samples digitally and use machine learning (AI) to help physicians assess cell morphology. Key goals include proving digital analysis is as good as traditional microscopy, ensuring AI works consistently across hospitals, and testing remote diagnosis and a national sample pool to tackle staffing issues. We will also use AI to find new indicators for how patients respond to treatment by combining cell images with genetic and clinical data. This innovation aims for faster and more objective diagnoses of blood cancer patients, better treatment planning, and a more cost-efficient healthcare system.

2026

2029