Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods

Research output: Contribution to journalReview articlepeer-review

83 Scopus citations


Increased interest in the opportunities provided by artificial intelligence and machine learning has spawned a new field of health-care research. The new tools under development are targeting many aspects of medical practice, including changes to the practice of pathology and laboratory medicine. Optimal design in these powerful tools requires cross-disciplinary literacy, including basic knowledge and understanding of critical concepts that have traditionally been unfamiliar to pathologists and laboratorians. This review provides definitions and basic knowledge of machine learning categories (supervised, unsupervised, and reinforcement learning), introduces the underlying concept of the bias-variance trade-off as an important foundation in supervised machine learning, and discusses approaches to the supervised machine learning study design along with an overview and description of common supervised machine learning algorithms (linear regression, logistic regression, Naive Bayes, k-nearest neighbor, support vector machine, random forest, convolutional neural networks).

Original languageEnglish (US)
JournalAcademic Pathology
StatePublished - Jan 1 2019


  • algorithms
  • artificial intelligence
  • convolutional neural network
  • deep learning
  • k-nearest neighbor
  • machine learning
  • random forest
  • supervised learning
  • supervised methods
  • support vector machine
  • unsupervised learning

ASJC Scopus subject areas

  • Pathology and Forensic Medicine


Dive into the research topics of 'Artificial Intelligence and Machine Learning in Pathology: The Present Landscape of Supervised Methods'. Together they form a unique fingerprint.

Cite this