Global Journal of Multidisciplinary and Applied Sciences

Advancements in Biomedical Engineering: AI Applications in Early Disease Detection

Areeba Siddiqui

Assistant Professor, Department of Biomedical Engineering, National University of Sciences and Technology (NUST), Islamabad, Pakistan

Muhammad Hasan Raza

Lecturer, Department of Mechatronics & Biomedical Systems, University of Engineering and Technology (UET), Lahore, Pakistan

Keywords: Artificial intelligence, early disease detection, biomedical engineering, deep learning, medical imaging

---

Abstract

 Artificial intelligence (AI) has rapidly advanced biomedical engineering by enabling earlier, faster, and more accurate disease detection across imaging, biosignals, laboratory diagnostics, and real-world patient monitoring. This article reviews key AI applications in early detection, focusing on engineered pipelines that integrate sensors, data preprocessing, machine learning (ML) and deep learning (DL) models, and clinical decision support. We discuss AI-enabled screening in radiology (mammography, chest X-ray, CT), ophthalmology (retinal imaging), cardiology (ECG-based risk prediction), and critical care (sepsis early warning). Beyond algorithm performance, we highlight biomedical engineering priorities: data quality, device calibration, model generalizability, interpretability, cybersecurity, workflow integration, and regulatory/ethical requirements. We conclude that AI’s greatest impact will come from robust, clinically validated systems designed for deployment constraints—especially in resource-limited settings—supported by strong governance and continuous monitoring of safety, bias, and outcomes.