Artificial intelligence and machine learning overview in pathology & laboratory medicine: A general review of data...

Area of Science:

• Pathology and Laboratory Medicine
• Data Science
• Medical Informatics

Background:

• Machine learning (ML) is increasingly vital in medical applications.
• Pathologists and laboratory professionals often lack familiarity with ML tools.
• There is a need to prepare the medical community for ML integration.

Purpose of the Study:

• To provide an overview of key machine learning concepts for medical professionals.
• To bridge the knowledge gap regarding ML in pathology and laboratory medicine.
• To serve as a reference for those new to ML or needing a refresher.

Main Methods:

• Review of fundamental machine learning concepts.
• Explanation of data types and preprocessing methods.
• Description of supervised and unsupervised learning algorithms.
• Inclusion of a comprehensive glossary of ML terms.

Main Results:

• The review covers essential ML elements including data concepts, preprocessing, and study design.
• Common supervised and unsupervised learning algorithms are detailed.
• Key ML terminology is defined within a glossary.

Conclusions:

• This review offers a broad overview of ML concepts and algorithms relevant to pathology and laboratory medicine.
• It serves as an updated reference to aid professionals in understanding and adopting ML.
• The objective is to facilitate the integration of ML into medical practice.