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Explain yourself, machine. Producing simple text descriptions for AI interpretability
We describe a feature, give a location, and then synthesise a conclusion. For example: There is an irregular mass with microcalcification in the upper outer quadrant of the breast. Findings are consistent with malignancy. You don’t need to understand the words I used here, but the point is that the features (irregular mass, microcalcification) are consistent with the diagnosis (breast cancer, malignancy). A doctor reading this report already sees internal consistency, and that reassures them that the report isn’t wrong. An common example of a wrong report could be: Irregular mass or microcalcification. No ...
Source: The Health Care Blog - December 12, 2019 Category: Consumer Health News Authors: Christina Liu Tags: Artificial Intelligence Health Tech AI Luke Oakden-Rayner machine learning Radiology Source Type: blogs

The LITFL Review 155
Welcome to the 154th LITFL Review. Your regular and reliable source for the highest highlights, sneakiest sneak peeks and loudest shout-outs from the webbed world of emergency medicine and critical care. Each week the LITFL team casts the spotlight on the blogosphere’s best and brightest and deliver a bite-sized chuck of FOAM. The Most Fair Dinkum Ripper Beaut of the Week Cricoid pressure/force continues to be a contentious point amongst critical care practitioners. Where did it come from? The Bottom Line review and critique the original paper by Sellick. [SO] Insight into the mind of Scott Weingart. How the master...
Source: Life in the Fast Lane - November 10, 2014 Category: Emergency Medicine Authors: Anand Swaminathan Tags: LITFL review LITFL R/V Source Type: blogs