The Future of AI in Lab Medicine
From the AI in Medicine Student Society (AiMSSS) and the Laboratory Medicine Interest Group (LMIH).
Interested in pathology or medical microbiology? Curious about how AI will impact these fields, or how AI is already being used in medical laboratories?
Join Dr. Susan Poutanen, Dr. Phedias Diamandis, and Ameesha Paliwal for an in-person seminar talk on AI in lab medicine. This seminar is the third in a series on how AI will affect various specialties in medicine. Pizza and refreshments will be provided!
Where and when
Wednesday, April 24, 12- 2 pm
Bahen Centre for Information Technology, 40 St. George Street, Toronto, ON.
How to join
Speakers
Dr. Susan Poutanen is a Medical Microbiologist and Infectious Diseases Physician at Mount Sinai Hospital and an associate professor in the Department of Laboratory Medicine and Pathobiology and Department of Medicine at the University of Toronto. Her research interests include: the epidemiology and detection of antimicrobial resistance, the diagnosis of and preparedness for emerging and re-emerging infectious diseases, and the optimization of microbiology laboratory practices to support antimicrobial stewardship and patient care including the use of automation and artificial intelligence.
Dr. Phedias Diamandis is a Neuropathologist at The University Health Network and a Scientist at the Princess Margaret Cancer Centre in Toronto. His research focuses on using chemical biology, deep learning and mass spectrometry-based proteomics to resolve phenotypic heterogeneity in different brain and glioblastoma niches. He is an Associate Professor at the University of Toronto in the Department of Laboratory Medicine and Pathobiology.
Ameesha Paliwal is a graduate student in the Laboratory Medicine and Pathobiology program at the University of Toronto. Under the mentorship of Dr. Phedias Diamandis in the Princess Margaret Cancer Centre, Ameesha’s research focuses on using deep learning to model tissue heterogeneity in neuropathology disease systems, including recurrent brain cancer and focal epilepsy. In her work, she demonstrates that by accounting for heterogeneity, researchers are able to scrutinize cell-specific drivers of poor outcomes and support discovery of precision medicine-based therapeutic approaches.