Artificial Intelligence based Healthcare Systems

Submission Deadline: 30 March 2023 Submit to Special Issue

Guest Editors

Dr. Mujeeb Ur Rehman, Riphah International University, Pakistan.
Dr. Rab Nawaz, University of Glasgow, UK.
Dr. Rehmat Ullah, Queen's University Belfast, UK.


The early diagnosis of a disease is vital in medical science and would be a prerequisite for prophylactic treatments. A timely diagnosis followed by treatment and precaution can save human lives. Testing the feasibility of disease biomarkers and providing novel artificial intelligence-based treatments are essential. In this regard, Computer-Aided Diagnosis (CAD), also called Complementary Medicine Technique (CMT), supported by Artificial Intelligence (AI), appears to be reliable, indispensable, robust, and accurate. CAD/CMT is gaining significant popularity as a diagnostic tool, especially for diseases where the mainstream diagnosis is painful and less precise. Further, proliferated utilization of CAD/CMT has been observed because of its reliability and accuracy. AI has gained popularity for solving numerous real-world problems in the last few years. With the help of machine learning (ML) and Deep Learning (DL), AI can be used for diagnosis, prognosis, monitoring, and administration of treatment to enhance patients' health outcomes. Furthermore, AI has aided medical practitioners in lowering diagnostic errors and increasing precision. In exchange, it has saved the human body's most critical organs and reduced burdens on hospitals. Early detection of diseases such as breast cancer, diabetic's disease, Liver and lungs diseases, viral diseases, Alzheimer's disease, and cardiovascular disorders has been made possible through AI. Due to accuracy and reliability, many researchers have focused on illness diagnosis using AI-based diagnostic techniques. We invite academics to submit original research articles and review articles that examine novel AI, machine learning, and deep learning-based medical diagnosis and prevention systems.


Artificial Intelligence; Computer-Aided Diagnosis; Complementary Medicine Technique; Machine Learning; Deep Learning; Signal processing; Biomedical signal analysis

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