Limits of AI in medical imaging for clinical decision making
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From the Human AI Collaboration: A Dynamic Frontier Conference of November 1, 2017; Daniel Rubin, associate professor of biomedical data science, radiology, medicine (biomedical informatics research), and ophthalmology (courtesy), Stanford University looks at…
1. There is a lot of variation between people and their diseases, making “precision medicine” and “precision health” necessary.
2. There are vast amounts of data that can be used to improve clinical decision making.
3. It is necessary to integrate different types of data (e.g. clinical notes) and deal with longitudinal data.
4. AI methods that use integrated data can provide clinical decision support in different types of clinical decision scenarios.
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