The Place of Artificial Intelligence Based Biomedical Signal Processing and Its Impact on Medical Diagnostic Systems

Abstract

Today, artificial intelligence (AI) and data analytics have gained a significant position in the field of engineering for solving complex problems, especially in the medical domain. In particular, machine learning techniques such as artificial neural networks (ANN), Bayesian learning, and clustering algorithms have demonstrated remarkable results in classification and prediction tasks. These methods are increasingly applied in biomedical applications, enhancing the accuracy of diagnostic systems and enabling the effective analysis of large and complex datasets. As a result, biomedical data can now be accessed and processed more efficiently.Biological signals such as motor imagery (MI), electroencephalography (EEG), electrocorticography (ECoG), and electrocardiography (ECG), which play a critical role in clinical decision making, are now analyzed using machine learning algorithms. This integration enables more accurate and rapid decision making processes, particularly in areas such as brain signal classification, epileptic seizure detection, cardiac arrhythmia identification, and early breast cancer diagnosis — all of which are vital for human health.In addition, AI based approaches are being utilized in security and biometric identification systems, which are critical components of healthcare services, achieving high levels of accuracy. In light of these advancements, AI powered classification and prediction systems continue to make a profound impact in both the medical and engineering fields.This study reviews various biomedical signal processing studies conducted using AI based machine learning techniques. The selected studies were examined in terms of the materials used, methodologies applied, domains from which the data were obtained, and their reported performance metrics. The scope was limited to academic research published over the past 15 years. Furthermore, the practical use of these systems in clinical diagnosis is also discussed.