Osgoode PhD candidate teams up with Sick Kids to explore AI in health outcomes
Bibliographic record
Abstract
Osgoode Hall Law School PhD candidate Ian Stedman has been awarded a prestigious Fellowship in Artificial Intelligence Law and Ethics with the Hospital for Sick Children’s Centre for Computational Medicine.\nStarting this fall, Stedman will explore the role that Artificial Intelligence (AI) can play in improving health outcomes, particularly for patients and families with rare diseases.\nThe work is especially important to Stedman, a lawyer and 37-year-old married father of two who was diagnosed five years ago with a rare disease called Muckle Wells Syndrome. His delayed diagnosis resulted in progressive nerve damage that led to permanent hearing loss, which also inspired him to become an advocate for people with rare diseases.\n“The revolutionary use of AI technology in health care is allowing people with rare and undiagnosed genetic diseases to benefit from the application of machine learning to analyze large genomic data sets,” Stedman said. “This SickKids analysis will identify possible disease-causing genes that, in turn, will lead to quicker diagnoses and, in many cases, drive research into appropriate therapies.”\nThe one-year, $40,000 Fellowship in Artificial Intelligence Law and Ethics will allow Stedman to apply his expertise in law, ethics and governance while working on-site at SickKids.\nHe will work with computer scientists, engineers, bioinformaticians, genetic counsellors and clinicians in order to directly address concerns that arise in relation to the Centre for Computational Medicine’s innovative work. He will also work with the SickKids’ research ethics board on refining institutional ethics review processes so that they are responsive to the unique needs of AI-driven research.\n“This is an amazing opportunity to contribute to the removal of barriers that limit the use of AI in both the research and clinical settings and to inform broader policy directions at SickKids,” he said.\nThe SickKids Fellowship is one of a number of prestigious external awards (including the Ontario Graduate Scholarship, the Joseph-Armand Bombardier CGS Doctoral Scholarship and the Centre for International Governance Innovation Doctoral Scholar) that students in Osgoode Hall Law School’s Graduate Program in Law have won in the past year.\nStedman’s doctoral work at Osgoode focuses on governance issues related to the principles of transparency and accountability, and the laws, policies and customs that enforce those principles.\nSeeing the emerging concerns about AI as a good fit for his research, he also collaborated with IP Osgoode (Osgoode Hall Law School’s Intellectual Property Law and Technology program) to organize an Social Sciences and Humanities Research Council of Canada funded conference in early 2018 called “Bracing for Impact – The Artificial Intelligence Challenge: A Road Map for AI Policy in Canada.” The conference examined specific topics within AI and furthered interest in the role governments ought to play, if any, in the use and regulation of artificial intelligence in both the public and private sectors.\nVisit http://ianstedman.blog.yorku.ca to learn more about Stedman’s research and https://ccm.sickkids.ca to find out more about the work being done at the Centre for Computational Medicine.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.051 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".