Artificial neural networks in medical diagnosis

TLDR

Clinical specialists have access to extensive data—from symptoms to biochemical and imaging outputs—that must be evaluated and assigned to specific pathologies, and adaptive learning algorithms can integrate diverse data into categorized outputs. The study aims to streamline routine diagnostics and prevent misdiagnosis by employing artificial intelligence methods, particularly artificial neural networks. The authors review the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis, illustrating their use through selected examples.

Abstract

An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected examples.