Dr. Clinton Campbell
Dr. Clinton Campbell is a hematopathologist at Hamilton Health Sciences and Assistant Professor of Medicine at McMaster University. Dr. Campbell completed his doctoral training in the laboratory of Dr. Mick Bhatia studying human hematopoietic stem cells, and medical school at the McMaster Michael G DeGroote School of Medicine. Dr. Campbell then went on to attain his FRCPC in Hematological Pathology at Dalhousie University. He has held numerous peer-reviewed grants as principal investigator in cancer genomics, transfusion medicine and artificial intelligence. Dr. Campbell’s research group focuses on using machine learning to 1) automate workflows in diagnostic medicine; 2) develop new representations of the information in pathology and 3) link this information with other large healthcare datasets to redefine paradigms in health and disease. Dr. Campbell’s research program is conducted in collaboration with Dr. Hamid Tizhoosh and KIMIA Lab at the University of Waterloo
We are in an era where the availability and scale of data is unprecedented. It is beyond the capability of humans to meaningfully analyze such enormous datasets using traditional approaches. This is particularly relevant to healthcare, where increasingly large and complex patient datasets are set to redefine the practice of medicine. The future of diagnostic medicine will entail extracting information from these datasets using artificial intelligence (AI). Consequently, the specialty of pathology will evolve from that of a diagnostic consultant to one of an information specialist. Careful application of machine learning algorithms in diagnostic medicine will yield new insights into human health and disease by enabling computational pathology, learning to better patient care. This presentation will discuss how machine learning is set to redefine the specialty of pathology.
- Overview of how machine learning can Support and automate pathology workflows.
- Overview of how machine learning can make new representations of information to improve diagnostics.
- Overview of how machine learning can enable computational pathology by linking information between healthcare datasets to redefine paradigms in health and disease.