Bibliographic record
Abstract
Topics that will be discussed are: What does "agentic AI" mean in a healthcare context, and how does it differ from traditional decision-support tools? What ethical tensions arise when AI agents begin to make or recommend autonomous decisions in patient care? What safeguards should be in place to prevent algorithmic bias or patient harm in remote care settings—especially when patients and providers are not co-located? When agentic AI goes wrong, who’s responsible, and how do we ensure accountability in cross-border virtual care? How can health systems build trust in agentic AI when the algorithms themselves may not be fully explainable—even by their creators? How are organizations operationalizing AI ethics beyond principles and into practice—especially when the tech moves faster than policy? Speakers Shanil Ebrahim is a Partner and the National Life Sciences and Healthcare Consulting Leader at Deloitte Canada. He advises clients across healthcare, pharmaceuticals, and retail pharmacy on complex strategy transformations, with a focus on AI and data-driven innovation. Shanil also leads Deloitte Canada's cross-industry AI strategy, helping organizations harness AI to drive growth, personalization, and operational excellence through tech-enabled, citizen and customer-centric solutions. Dr. Dimitrios Kalogeropoulos is a digital health pioneer and a committed advocate for the ethical and responsible use of AI in healthcare. He serves as CEO of the Global Health & Digital Innovation Foundation and as Health Executive in Residence at UCL’s Global Business School for Health. Dr. Kalogeropoulos has advised leading organizations, including the WHO, and has played a key role in shaping global policy initiatives to improve healthcare accessibility and drive sustainable, innovative solutions worldwide. Moderator Sarah Harper, MA, MBA drives digital transformation across the healthcare ecosystem and beyond. With deep expertise engaging with learners of all ages and levels, she blends clinical insight, systems thinking, and user-centered design to turn bold ideas into practical, equitable solutions. Dedicated to helping others, Sarah makes digital care smarter—and more human. Sarah leads AI, Analytics, and Automation initiatives at Mayo Clinic Health System, and holds the academic rank of Assistant Professor of Healthcare Administration. She's the co-host of Tech It to the Limit, a podcast blending wit and wisdom to explore digital health’s messiest challenges. Sarah also serves as an Advisor to Mayo Clinic Platform, supporting solution developers and health systems in tech implementation and evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".