Non-Generative Artificial Intelligence (AI) in Medicine: Advancements and Applications in Supervised and Unsupervised Machine Learning.

Abstract

The use of Artificial Intelligence (AI) within pathology and healthcare has advanced extensively. We have accordingly witnessed increased adoption of various AI tools which are transforming our approach to clinical decision support, personalized medicine, predictive analytics, automation, and discovery. The familiar and more reliable AI tools that have been incorporated within healthcare thus far fall mostly under the non-generative AI domain, which includes supervised and unsupervised machine learning (ML) techniques. This review article explores how such non-generative AI methods, rooted in traditional rules-based systems, enhance diagnostic accuracy, efficiency, and consistency within medicine. Key concepts and the application of supervised learning models (i.e. classification and regression) such as decision trees, support vector machines, linear and logistic regression, K-nearest neighbor, and neural networks are explained along with the newer landscape of neural network-based non-generative foundation models. Unsupervised learning techniques including clustering, dimensionality reduction, and anomaly detection are also discussed for their role in uncovering novel disease subtypes or identifying outliers. Technical details related to the application of non-generative AI algorithms for analyzing whole slide images is also highlighted. The performance, explainability and reliability of non-generative AI models essential for clinical decision-making is also reviewed, as well as challenges related to data quality, model interpretability, and risk of data drift. An understanding of which AI-ML models to employ and which shortcomings need to be addressed is imperative to safely and efficiently leverage, integrate, and monitor these traditional AI tools in clinical practice and research.