Introduction to Artificial Intelligence and Machine Learning in Pathology and Medicine: Generative and Nongenerative...

Area of Science:

• Medical Informatics
• Artificial Intelligence
• Machine Learning

Background:

• Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields with significant implications for healthcare.
• Pathology and medicine are increasingly integrating AI and ML for enhanced diagnostic capabilities and operational efficiency.
• Understanding AI and ML is crucial for healthcare professionals navigating an AI-enabled future.

Purpose of the Study:

• To introduce a 7-part review series on AI and ML in pathology and medicine.
• To provide a foundational understanding of AI and ML terminology and core concepts.
• To serve as a primer for subsequent articles detailing statistics, regulations, bias, ethics, and ML-Ops.

Main Methods:

• Literature review of AI and ML applications in medicine.
• Compilation of a comprehensive dictionary of fundamental AI-ML terminology.
• Overview of generative and non-generative AI domains.

Main Results:

• The introductory review establishes a baseline understanding of AI and ML for a broad audience.
• It defines key terms and categorizes AI-ML domains, including traditional and generative approaches.
• The manuscript sets the stage for in-depth discussions in the subsequent six review articles.

Conclusions:

• This introductory review is essential for healthcare professionals seeking to understand AI and ML's role in medicine.
• It equips readers with the necessary knowledge to engage with advanced topics on AI-ML in healthcare.
• The series aims to prepare the medical community for the transformative impact of AI and ML.