Home Events AI in Hematopathology.

AI in Hematopathology.

Date

May 06 2021

Time

1:15 pm - 2:15 pm

Speaker

  • Dr. Clinton Campbell
    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 studying human hematopoietic stem cells and medical school at McMaster University. Dr. Campbell then went on to
    attain his FRCPC in Hematological Pathology at Dalhousie University. Prior to practicing in Hamilton, Dr. Campbell held medical staff positions with the
    Nova Scotia Health Authority and University Health Network. He has held numerous peer-reviewed grants as principal investigator in cancer genomics,
    transfusion medicine and artificial intelligence applied to digital pathology.

Description

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.

Objectives

  • 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.