April Khademi, Ryerson University
Associate Professor
Canada Research Chair in AI for Medical Imaging
Toronto, Ontario
akhademi@torontomu.ca
Office:
(416) 979-5000 ext. 4103
Expert In
Bio/Research
When April Khademi began her PhD degree, the medical community wasn’t quite ready to embrace or adopt artificial intelligence (AI) for medical images. Physicians during this time said the field “wouldn’t go anywhere.” Then, along came IBM Watson, the AI platform designed to compete against Jeopar...
Click to Expand >>
Click to Expand >>
Bio/Research
When April Khademi began her PhD degree, the medical community wasn’t quite ready to embrace or adopt artificial intelligence (AI) for medical images. Physicians during this time said the field “wouldn’t go anywhere.” Then, along came IBM Watson, the AI platform designed to compete against Jeopardy! champions, and health-care professionals opened their minds to the power of AI. “Now, clinicians are embracing these technologies and looking forward to integrating it into their practice,” she says. “It has the potential to change the way medicine is practiced and ultimately improve the quality of care for patients.”
Her lab, the Image Analysis in Medicine Lab (IAMLAB) focuses on design, development and translation of AI tools for medical images. These innovative software tools can discover disease subgroups and describe pathology more accurately. A large focus is translation through algorithm commercialization, and uptake into clinical practices to improve turn-around-times, inter-rater agreement and accuracy of radiologists and pathologists; which improve quality of care. Dr. April Khademi, Canada Research Chair in AI for Medical Imaging, has 10+ years of academic & industrial experience in designing and commercializing AI tools for medical imaging. She partners with leading clinicians and biomedical companies from Canada.
Clinical applications come naturally to Khademi, whose experience in industry and commercialization helps make translation of AI algorithms for medical imaging a reality. Her automated MRI analysis algorithms detect new biomarkers for neurological diseases including brain cancer, dementia, stroke and neurodevelopmental disorders, and characterize breast cancers in digital pathology images – work that, up until recently, was manually done through visual assessments performed by radiologists and pathologists. The result is faster, more objective diagnoses, fewer mistakes and, overall, higher quality of care. The AI tools also provide standardized assessments that can guide therapy for more personalized therapy decisions.
Khademi brings that same industrial focus to her teaching, which relies heavily on design-based labs and practical implementation. Her teaching philosophy is encapsulated by the wise words of Albert Einstein: “Learning is experience. Everything else is just information.”
Click to Shrink <<
Her lab, the Image Analysis in Medicine Lab (IAMLAB) focuses on design, development and translation of AI tools for medical images. These innovative software tools can discover disease subgroups and describe pathology more accurately. A large focus is translation through algorithm commercialization, and uptake into clinical practices to improve turn-around-times, inter-rater agreement and accuracy of radiologists and pathologists; which improve quality of care. Dr. April Khademi, Canada Research Chair in AI for Medical Imaging, has 10+ years of academic & industrial experience in designing and commercializing AI tools for medical imaging. She partners with leading clinicians and biomedical companies from Canada.
Clinical applications come naturally to Khademi, whose experience in industry and commercialization helps make translation of AI algorithms for medical imaging a reality. Her automated MRI analysis algorithms detect new biomarkers for neurological diseases including brain cancer, dementia, stroke and neurodevelopmental disorders, and characterize breast cancers in digital pathology images – work that, up until recently, was manually done through visual assessments performed by radiologists and pathologists. The result is faster, more objective diagnoses, fewer mistakes and, overall, higher quality of care. The AI tools also provide standardized assessments that can guide therapy for more personalized therapy decisions.
Khademi brings that same industrial focus to her teaching, which relies heavily on design-based labs and practical implementation. Her teaching philosophy is encapsulated by the wise words of Albert Einstein: “Learning is experience. Everything else is just information.”
Click to Shrink <<