The use of artificial intelligence (AI) in diagnostic medical imaging is undergoing extensive evaluation. AI is increasingly helping to uncover hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets. Hence, it has shown impressive accuracy and sensitivity in identifying imaging abnormalities and promises to enhance tissue-based detection and characterization. AI has already proven that it may be a valuableally for radiologists and pathologists looking to accelerate their productivity and potentially improve their accuracy. At the beginning of an AI-assisted diagnostic imaging revolution, the medical community has to anticipate the potential unknowns of this technology to ensure effective and safe incorporation into clinical practice. Detailed assessment of AI's potential perils, in the context of its unique capabilities, is integral to establishing its role in clinical medicine, and navigating between enhanced detection and overdiagnosis will be no easy task. Hence, I became highly motivated to study AI-based machine learning and deep learning techniques to differentiate between the normalities and abnormalities of patients from medical images. Currently, I am studying (undergraduate) in the Department of Biomedical Engineering at Islamic University, Bangladesh.

Research Interest: Data Science, Data Analytics, Machine learning, Deep learning, Medical image processing, Signal processing, Computer vision, Health Informatics.