Abstract
This bibliographic study covers Artificial Intelligence (AI)theory and its applications from the healthcare field and in particular from the discipline of pathology. This review includes basics of AI, supervised and unsupervised machine learning (ML), various supervised ML algorithms, and their applications in healthcare and pathology. Digital Pathology with Deep Machine Learning is more advantageous over traditional pathology that is based on ‘physical slide on a physical microscope’. However, various implementation challenges of cost, data quality, multi-center validation, bias, and regulatory approval issues for AI in clinical practice still remain, which are also described in this study.
Highlights
Linear RegressionLinear regression models find machine uses weak predictors (a Decision tree) that the target by finding the best-fitted “least squares are boosted, which provide a better performing regression line,” which has the smallest error sum, model (a Boosted tree)
Introduction descent andBackpropagation.[7]The main objective of this paper is to describe the history of the evolution of Artificial Intelligence over time
The method was utilized to detect and extract the ‘spectral + spatial’ characters to create an early cancer diagnosis model. It resulted in the accuracy of 96.5%, specificity of 96%, and sensitivity of 96.3% for non-precancerous lesion, precancerous lesion, and cancer groups.[35] from patients who had died at 5 years after surgery d) Digital Pathology (DP)
Summary
Linear regression models find machine uses weak predictors (a Decision tree) that the target by finding the best-fitted “least squares are boosted, which provide a better performing regression line,” which has the smallest error sum, model (a Boosted tree). This method can work with amongst the independent continuous variables unbalanced data sets but may produce overfitting.[29]. (features/the cause) and the dependent continuous variables (target/the effect). Aggarwal et al detail the pitfalls associated with this analysis.[24]
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