Authors: Doke Shubham Somnath, Prof. N. G. Rokade, Prof. M. C. Kolse, Prof. S. K. Said
Abstract: Healthcare systems face a shortage of trained doctors and specialists, heavy hospital workloads, rapidly growing volumes of medical data, diagnostic delays and unequal access to quality care, particularly in rural areas. Artificial Intelligence (AI), including machine learning, deep learning, computer vision and natural language processing, is increasingly used to learn patterns from medical data and support clinicians in diagnosis, treatment planning and patient monitoring. This paper reviews AI in healthcare through a three-stage collect–predict–care pipeline. In the collection stage, medical images such as X-ray, CT, MRI, retinal and skin photographs, laboratory results, electronic health records and wearable readings are gathered and prepared. In the prediction stage, convolutional neural networks analyse images, machine learning models estimate patient risk from records and language models interpret clinical notes. In the care stage, results are presented to doctors as decision support, and the doctor makes the final diagnosis and treatment decision. The review surveys landmark studies for each stage, proposes a layered clinical decision support architecture, and discusses challenges of privacy, bias, explainability, infrastructure cost and regulation. It concludes that AI can make healthcare faster, more accurate and more accessible when it is validated in real hospitals and always operates under clinical supervision.
DOI: https://doi.org/10.5281/zenodo.24220253
