Wednesday 28 September 2022

Can AI/ML Help Solve Underrepresentation In Clinical Trials?


To assess medicines for safety and efficacy, it is important that clinical trials are representative of biological sex. Too often, there is an underrepresentation of women in clinical trial subject populations. This leads to erroneous data since women differ in presentation, clinical manifestations, and outcomes in comparison to men.2 For example, many medications metabolize more slowly in women and other medications are more active in men. Clinical manifestations may differ because of the unknown pathophysiology for many disorders, and the drug target identification can differ between the sexes for the same disease. Consequently, since details about clinical trial participants often remains underreported in medical journals, the physician deciding on the optimal treatment for a female patient may not be choosing the most appropriate intervention. Alternatively, the side effects of a medication can be different between men and women.3


Where these biases occur, they undermine the potential that AI presents to translate, scale, and accelerate drug development insights. In addition, if the base algorithm or the training data for an ML model contains some form of bias, it is extremely likely that the resulting models will perpetuate that bias when recommending subject data sets and for interpreting trial data sets.



Sandle, T.(2022) Can AI/ML Help Solve Underrepresentation In Clinical Trials?, Clinical Leader, 26th April 2022:


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Sandle, T.(2022) Can AI/ML Help Solve Underrepresentation In Clinical Trials?, Life Science Leader, 1st July 2022:


Posted by Dr. Tim Sandle, Pharmaceutical Microbiology Resources (

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