The use of digital twins (DTs) has proliferated across various fields and industries, with a
recent surge in the healthcare sector. The concept of digital twin for health (DT4H) holds …

Medical digital twins, which are potentially vital for personalized medicine, have become a
recent focus in medical research. Here we present an overview of the state of the art in …

We are rapidly approaching a future in which cancer patient digital twins will reach their
potential to predict cancer prevention, diagnosis, and treatment in individual patients. This …

Despite the remarkable advances in cancer diagnosis, treatment, and management over the
past decade, malignant tumors remain a major public health problem. Further progress in …

The pace of technological innovation over the past three decades has transformed the field
of radiotherapy into one of the most technologically intense disciplines in medicine …

Triple-negative breast cancer (TNBC) is persistently refractory to therapy, and methods to
improve targeting and evaluation of responses to therapy in this disease are needed. Here …

Mechanistic learning refers to the synergistic combination of mechanistic mathematical
modeling and data-driven machine or deep learning. This emerging field finds increasing …

We develop a methodology to create data-driven predictive digital twins for optimal risk-
aware clinical decision-making. We illustrate the methodology as an enabler for an …

Radiopharmaceutical therapy (RPT) is a rapidly develo** field of nuclear medicine, with
several RPTs already well established in the treatment of several different types of cancers …

The impact of artificial intelligence (AI) will be felt not only in the arena of patient care and
deliverable therapies but will also be uniquely disruptive in medical education and …