Harnessing AI for Equitable Healthcare
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Artificial intelligence is generating enormous excitement for its potential to predict health risks, speed diagnoses, and guide medical decision making. At the same time, the algorithms that make AI so powerful carry an inherent danger of bias, as some models are known to return lower accuracy results on risk assessment or even exclude certain populations from clinical studies. Lack of diversity in the input data, analytical limitations, and clinical assumptions can all compound existing disparities. Finding ways to scale AI innovations rapidly, while ensuring responsible deployment, is essential to mitigate algorithmic biases, maintain the human aspect of medicine, and secure guardrails so that disruptive AI platforms foster the most equitable and patient-centric healthcare system possible. (Presented by Takeda)
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