Hooman H. Rashidi|Nam K. Tran|Elham Vali Betts|Lydia Pleotis Howell|Ralph Green
Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods
University of California, Davis|University of California, Davis|University of California, Davis|University of California, Davis|University of California, Davis
increased interest opportunities provided artificial intelligence machine learning spawned new field healthcare research new tools development targeting many aspects medical practice including changes practice pathology laboratory medicine optimal design powerful tools requires crossdisciplinary literacy including basic knowledge understanding critical concepts traditionally unfamiliar pathologists laboratorians review provides definitions basic knowledge machine learning categories supervised unsupervised reinforcement learning introduces underlying concept biasvariance tradeoff important foundation supervised machine learning discusses approaches supervised machine learning study design along overview description common supervised machine learning algorithms linear regression logistic regression naive bayes knearest neighbor support vector machine random forest convolutional neural networks