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Deploying AI for Critical Care: Learning to Make a ...
Deploying AI for Critical Care: Learning to Make a Difference
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Video Summary
In this talk, the speaker discusses the challenges and considerations when implementing machine learning models in clinical care. Although there is a growing number of machine learning models being developed, very few are actually being implemented clinically. The speaker highlights several barriers to implementation, including data privacy and security, data quality and availability, lack of standardization, ethical considerations, model interpretability, and adoption and integration challenges. To address these challenges, the speaker presents a framework for successful implementation, which involves exploring the problem, establishing a multidisciplinary team, designing and testing the machine learning model, and implementing and evaluating the system. The speaker also emphasizes the importance of ensuring that the model adds value to clinical practice, understanding the workflow and usability of the model, and measuring usage and outcomes. The talk concludes by suggesting that accurate machine learning models that prompt the right actions at the right time have the potential to improve patient outcomes.
Asset Subtitle
Professional Development and Education, 2023
Asset Caption
Type: year in review | Year in Review: Anesthesiology (SessionID 2000001)
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Content Type
Presentation
Knowledge Area
Professional Development and Education
Membership Level
Professional
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Tag
Innovation
Year
2023
Keywords
machine learning models
clinical care
implementation challenges
data privacy and security
improve patient outcomes
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