Review of AI in Medical and Health Sciences is a scholarly journal focused on comprehensive reviews of artificial intelligence and emerging technologies across medical sciences, healthcare, biomedical research, diagnostics, clinical practice, medical education, and public health. It promotes critical evaluation of AI applications, innovations, challenges, etc.
Published on the Behalf of FARI
Review of AI in Medical and Health Sciences is a scholarly journal focused on comprehensive reviews of artificial intelligence and emerging technologies across medical sciences, healthcare, biomedical research, diagnostics, clinical practice, medical education, and public health. It promotes critical evaluation of AI applications, innovations, challenges, and future directions.
Review of AI in Medical and Health Sciences aims to provide a scholarly platform for critical and comprehensive reviews of the rapidly evolving role of artificial intelligence in medical and health sciences. The journal covers AI applications in clinical medicine, diagnostics, medical imaging, biomedical research, drug discovery, healthcare systems, public health, medical education, digital health, and patient care. It welcomes reviews that examine emerging technologies, practical applications, opportunities, limitations, ethical considerations, and future developments to advance understanding and responsible use of AI in healthcare and medical research.
Volume 1, Issue 1
Pratibha Kumari, Kamini Kumari
August 2026: 1(1):1–8
Background: Cardiovascular diseases remain a major global health burden, and the increasing volume of electrocardiographic, imaging, electronic health record, laboratory, and wearable data has created opportunities for artificial intelligence (AI) in clinical...
Pankaj Kumar, Krishnendu Sen
August 2026: 1(1):9–16
Chest radiography remains one of the most widely used imaging modalities for the initial evaluation of pulmonary diseases, including pneumonia, pulmonary tuberculosis, and other lung abnormalities. However, interpretation of chest radiographs can be challengin...
Sankar Sahu
September 2026: 1(1):17–21
Background: Type 2 diabetes mellitus (T2DM) is a major public health concern associated with substantial long-term morbidity. Early identification of individuals at increased risk may support timely screening, preventive interventions, and improved glycaemic m...
Avinash Chaubey
September 2026: 1(1):22–26
Cardiovascular diseases (CVDs) remain a major global health burden, creating a continuing need for accessible and reliable methods of early risk identification. Conventional cardiovascular risk assessment primarily relies on demographic characteristics, medica...
Ravish Kumar, Sonali Sekhar
September 2026: 1(1):27–31
Background: Generative artificial intelligence (AI) is increasingly used in health sciences education; however, its reliability, evidence quality, and educational usefulness require systematic evaluation.Objective: To compare the factual accuracy, completeness...
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