Osgoode PhD candidate teams up with Sick Kids to explore AI in health outcomes
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,024 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,004 | 0,011 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,051 | 0,009 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».