Published Paper Details:

Computer vision has revolutionized the field of automated medical screening by enabling the efficient analysis of medical images for early disease detection and diagnosis. With the integration of deep learning techniques, especially convolutional neural networks (CNNs) and transformer-based models, computer vision systems can now accurately interpret complex patterns in radiology, ophthalmology, dermatology, and pathology. These systems enhance diagnostic precision, reduce human error, and offer scalable solutions, particularly in underserved and remote areas. Despite significant advancements, challenges such as limited access to diverse, annotated datasets, data privacy concerns, algorithmic bias, and lack of clinical validation persist. Moreover, ensuring model interpretability and seamless integration into existing healthcare workflows remains a critical hurdle. Recent developments in multimodal learning, federated learning, explainable AI, and mobile-based screening tools are addressing these limitations, paving the way for more equitable and transparent healthcare delivery. Performance evaluation using robust metrics and regulatory validation is vital to ensure safety and effectiveness in clinical practice. This review outlines the applications, challenges, and future directions of computer vision in medical screening, emphasizing the need for interdisciplinary collaboration and ethical considerations. Ultimately, computer vision has the potential to transform medical diagnostics by making healthcare more proactive, accessible, and data-driven.