Artificial intelligence in diagnostic software: validation, safety, and lifecycle challenges in Europe.
TL;DR
A comprehensive introduction to the application of machine learning in routine clinical practice and biomedical research, including biomarker discovery is provided and the most widely used machine learning models are introduced together with their key characteristics, strengths, and limitations.
Abstract
Artificial intelligence, particularly machine learning and deep learning, is a rapidly evolving field that is increasingly permeating all areas of modern society, including public healthcare. In the present work, we provide a comprehensive introduction to the application of machine learning in routine clinical practice and biomedical research, including biomarker discovery. The most widely used machine learning models are introduced together with their key characteristics, strengths, and limitations. Furthermore, the fundamental principles governing the interpretation of model outputs and communication with regulatory authorities are discussed and illustrated using both real-world and synthetic datasets through modeling in the Python programming environment. Particular emphasis is placed on maintaining data quality and ensuring robust validation procedures in the context of dynamic, continuously updated AI systems intended for diagnostic software registered as medical devices (SaMDs). The importance of data governance, external validation, interpretability, and clinical oversight is highlighted as a prerequisite for safe and effective implementation of AI technologies in healthcare - throughout both the development and commercialization phases. Finally, current trends in medical artificial intelligence are reviewed, together with their potential benefits and associated risks for patients. The presented overview aims to facilitate a deeper understanding of the opportunities and challenges associated with the integration of AI-driven solutions into modern healthcare systems.