Apparently, the earlier title for this article was not strong enough! When a inclusivity, racism, and equality fanatic writes an article you get this opinion piece! Some of his statements are outright laughable!
"... But as someone deeply involved in applying AI to health care, I found one element grating: Highlighting inclusivity and equality as things to be “encouraged” is not the way forward, especially with something as important as health care. ...
Inclusivity and equality must be built into the DNA of a product from day one, and it does not happen by simply checking a politically correct box. ...
For example, it was only in 1993 that the United States mandated adequate inclusion of women in clinical trials for medicines.
And systemic racism can still be seen in revised U.S. guidelines which now, as a result of Covid-19, recommend that women under age 50 skip annual mammograms even though breast cancer diagnoses are at their highest for Black, Hispanic and Asian women in their 40s. ...
There are troubling indications that significant disparities exist in the performance of AI-enabled health care solutions ...
Unfortunately, bias exists everywhere in the world and permeates the datasets used to develop and test AI products. ...
The lack of diverse data to develop and train AI products is currently the primary inhibitor to making substantive progress in this area. ..."
Inclusivity and equality must be built into the DNA of a product from day one, and it does not happen by simply checking a politically correct box. ...
For example, it was only in 1993 that the United States mandated adequate inclusion of women in clinical trials for medicines.
And systemic racism can still be seen in revised U.S. guidelines which now, as a result of Covid-19, recommend that women under age 50 skip annual mammograms even though breast cancer diagnoses are at their highest for Black, Hispanic and Asian women in their 40s. ...
There are troubling indications that significant disparities exist in the performance of AI-enabled health care solutions ...
Unfortunately, bias exists everywhere in the world and permeates the datasets used to develop and test AI products. ...
The lack of diverse data to develop and train AI products is currently the primary inhibitor to making substantive progress in this area. ..."
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