Introduction
AI-for-HealthCare
Note: when choosing a medical imaging problem to be solved by machine learning, it is tempting to assume that automated detection of certain conditions would be the most valuable thing to solve. However, this is not usually the case. Quite often detecting if a condition is present is not so difficult for a human observer who is already looking for such a condition. Things that bring most value usually lie in the area of productivity increase. Helping prioritize the more important exams, helping focus the attention of a human reader on small things or speed up tedious tasks usually is much more valuable. Therefore it is important to understand the clinical use case that the algorithm will be used well and think of end-user value first.
50 Surprising Statistics Every Healthcare Stakeholder Must Know
Inspiration from Weina
2D medical Imaging
- FDA cleared AI medical products that are related to radiology and other imaging domains
- The Cancer Imaging Archive
- TigerLily: Finding drug interactions in silico with the Graph
- TigerLily: Finding drug interactions in silico with the Graph
- How it fits into medicine and the healthcare system.
- How to use AI to solve 2D Imaging problems
- How to take AI from the bench to bedside to be used by doctors to improve patient lives