Deep Learning in Healthcare and Its Practical Limitations

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=Gp9WqvpI0mE


Duration: 42:32
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For slides and more information on the paper, visit https://ai.science/e/deep-learning-in-healthcare-and-its-practical-limitations--2021-01-karthik-bhaskar

Speaker: Karthik Bhaskar; Host: Jiri Stodulka

Find the recording, slides, and more info at https://ai.science/e/deep-learning-in-healthcare-and-its-practical-limitations--2021-01-karthik-bhaskar

Motivation / Abstract
Machine learning uses statistical techniques to give computer systems the ability to "learn" with incoming data and to identify patterns and make decisions with minimal human direction. Armed with such targeted analytics, doctors may be better able to assess risk, make correct diagnoses, and offer patients more effective treatments. Deep Learning has a lot of potential in Healthcare. But why don't these techniques are adopted in hospitals yet?
What are the gaps between academic research and production level code in Deep Learning and Healthcare?
How can we mitigate this production level gap in Deep Learning and Healthcare, and what are some of the tools and techniques we can deploy?

What was discussed?
* Data Augmentation
* Synthetic Data
* Pretraining
* Deep Learning as a systemic engineering
* Machine Learning Lifecycle and
* Infrastructure



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