– For cancer patients, prognosis and drug selection using biomarkers with multimodal machine learning is revolutionizing personalized medicine. By integrating genomic, proteomic, imaging, and other biomarker data, multimodal machine learning provides highly accurate predictions of disease outcomes and tailors treatments to each patient’s unique profile. This approach not only improves the effectiveness of therapies but also reduces unnecessary side effects and costs associated with traditional trial-and-error methods of treatment.

– Long-term monitoring of dementia through biomarkers for early detection is an emerging and promising field that aims to identify and track the biological changes associated with dementia, often well before clinical symptoms become apparent. Dementia, including Alzheimer’s disease (AD) and other neurodegenerative disorders, typically develops over many years. Biomarkers offer a way to detect these changes early, providing opportunities for earlier intervention, better disease management, and potential disease-modifying treatments.