Abstract
An ever-expanding suite of cancer imaging tools is being created with the help of AI and ML. To design the best tool, it's important to include experts from other fields to determine the right use case, then test and refine the tool thoroughly before implementing it into healthcare systems. Showcasing significant advancements in the field, this interdisciplinary study. We go over the pros and downsides of using AI and ML for cancer imaging, some things to keep in mind when turning algorithms into tools for widespread use, and how to build an ecosystem that will help AI and ML expand in this field
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