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Dr. Rajeev Chandra is a Senior Resident in the Department of Otolaryngology and Head and Neck Surgery at AIIMS-CAPFIMS, New Delhi, India. His professional interests include clinical research, medical imaging, Artificial Intelligence in healthcare, disease diagnosis, and technology-assisted clinical decision support. He actively contributes to interdisciplinary research integrating medicine with intelligent healthcare technologies.
Medical imaging is essential in contemporary healthcare since it facilitates early disease detection, therapy strategising, and patient surveillance. Imaging techniques including X-ray, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), Ultrasound, Positron Emission Tomography (PET), fundus imaging, and histopathological imaging produce extensive clinical data necessitating precise analysis. Traditional image analysis relies heavily on radiologists and medical experts, resulting in protracted diagnostic processes and vulnerability to discrepancies among observers. The amalgamation of Artificial Intelligence (AI), Deep Learning (DL), Computer Vision, Internet of Medical Things (IoMT), Cloud Computing, and Explainable Artificial Intelligence (XAI) has markedly enhanced automated disease identification and categorisation. Deep learning frameworks like Convolutional Neural Networks (CNN), Residual Networks (ResNet), DenseNet, U-Net, Vision Transformers (ViT), Long Short-Term Memory (LSTM), and Hybrid Deep Learning Models have exhibited exceptional efficacy in identifying cancers, cardiovascular ailments, neurological conditions, retinal disorders, pulmonary infections, and musculoskeletal irregularities. This chapter elucidates the principles, framework, deep learning methodologies, applications, obstacles, and prospective research avenues pertaining to illness detection and classification in medical imaging through deep learning.
Machine learning (ML) is increasingly being investigated for predicting disease progression and treatment response using clinical records, laboratory measurements, medical imaging, physiological signals, genomic information, and longitudinal patient observations. Conventional healthcare assessment often relies on periodic clinical evaluations and population-level treatment guidelines, which may not fully capture individual differences in disease trajectories or therapeutic outcomes. It discusses supervised learning, ensemble algorithms, deep learning, survival analysis, time-series modelling, and causal inference approaches for different clinical prediction tasks. A conceptual framework is presented that integrates data acquisition, preprocessing, feature engineering, model development, longitudinal prediction, interpretability, and clinical validation. The chapter also explores applications in oncology, cardiovascular disease, diabetes, neurological disorders, and chronic inflammatory conditions.