Emma Shtivelman is a molecular and cell biologist with extensive experience in cancer biology. Emma received her PhD from the Weizmann Institute of Science, where she identified the fused transcript between BCR and ABL in chronic myeloid leukemia (CML), the first demonstration of an oncogenic gene product derived from a chromosomal translocation. As a postdoctoral fellow at the University of California, San Francisco (UCSF), she characterized the molecular consequences of chromosomal alterations in Burkitt’s lymphoma, and studied the pathogenesis of neuroblastoma. She worked at Systemix, establishing a novel SCID-hu metastasis model to enable the in vivo analysis of human tumor metastasis. She continued her research in signal transduction pathways in cancer and its metastatic progression at the UCSF Cancer Center and biotech before joining Cancer Commons. As a Chief Scientist, she works directly with cancer patients, identifying the most promising clinical trials for each and every patient, and helping them find the best experts and treatments.
Artificial intelligence holds the promise to revolutionize health care and directly impact the clinical trials process via expediting the recruitment of eligible clinical trial patients. The systems currently developed with the use of AI/ML enable higher patient enrollment rates and better trial assignment, contribute to reducing the cost of recruitment via additional/more efficient medical tests, and facilitate analysis and integration of operational data from historical cases. This session focuses on various applications of AI in clinical trial design and patient selection.