Developing a framework for equitable ML development and deployment in oncology

The next healthcare era hinges on artificial intelligence (AI) advancements, leveraging vast clinical, imaging and operational data to streamline and augment processes. However, the data that is used to train AI solutions is biased due to the inequities that characterize our society (e.g. the social determinants of health), and health and healthcare in particular. Therefore,…

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Developing an AI-decision support tool for ED visits in oncology to augment compassion-centered care

Clinician Scientist, Sunnybrook Research Institute Many cancer patients access the emergency department (ED). However, 60% of ED visits are preventable with limited tools to stratify risk groups and personalize care plans. Ultimately, this would avoid anxiety and distress. Here, William proposes to develop an artificial intelligence (AI)-guided decision support tool to improve management and follow-up…

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Using digital models and visualization to help patients better understand prognosis and uncertainty

Radiation Oncologist, London Health Sciences Centre Understanding prognosis is important for treatment decisions, life planning, and psychological well-being in patients with metastatic disease. Physicians are increasingly using statistical and AI-based models for prognosis estimates, but these are inaccessible to patients. No models are designed specifically for patient use. How are patient interactions with prognosis models…

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