With approximately 2 billion chest X-ray examinations conducted globally each year, the demand for radiological interpretation far surpasses the available expertise, particularly in resource-constrained regions. Recent advancements in artificial intelligence and computer vision present promising solutions for automated chest X-ray analysis. Nevertheless, integrating AI-driven diagnostics into clinical practice encounters several challenges, including data-centric issues, implementation barriers, deployment complexities, and the need for trustworthy AI. This dissertation focuses on the data-centric aspect, making significant contributions through enhanced data collection, the creation of novel datasets, algorithm development, privacy-preserving collaborative learning, and modelling for low-resolution data. It offers practical methodologies for embedding AI into chest radiology workflows, with a particular emphasis on addressing underserved conditions and healthcare settings with limited data availability. Furthermore, this work illustrates how tailored AI solutions can democratize access to high-quality radiological care while balancing privacy considerations and operational constraints across diverse environments.

Silicosis is an irreversible lung disease caused by silica dust exposure in industrial settings. Early detection is crucial, but automatic diagnostic methods are hindered by limited data availability. We propose SHIELD - a self-supervised, Silicosis-focused Hierarchical Imaging framework for early occupational Lung disease Diagnosis. Our method leverages a multi-resolution jigsaw puzzle pretext task on CXR images to extract and preserve features for lung region analysis. By employing a pyramidal strategy to generate pretrained models at various resolutions, followed by fine-tuning and a two-level ensembling across diverse deep learning architectures, SHIELD achieves enhanced diagnostic accuracy. We validate our approach on a publicly collected CXR dataset of 3044 samples from public health centers in India. SHIELD achieves 72% accuracy, demonstrating up to 20% improvement over baseline approaches. This work advances medical image analysis and supports UN Sustainable Development Goal 3 by providing cost-effective early screening in resource-limited settings.

According to theWorld Health Organization, Pneumoconiosis affects millions of workers globally, with an estimated 260,000 deaths annually. The burden of Pneumoconiosis is particularly high in low-income countries, where occupational safety standards are often inadequate, and the prevalence of the disease is increasing rapidly. The reduced availability of expert medical care in rural areas, where these diseases are more prevalent, further adds to the delayed screening and unfavourable outcomes of the disease. This paper aims to highlight the urgent need for early screening and detection of Pneumoconiosis, given its significant impact on affected individuals, their families, and societies as a whole. With the help of low-cost machine learning models, early screening, detection, and prevention of Pneumoconiosis can help reduce healthcare costs, particularly in low-income countries. In this direction, this research focuses on designing AI solutions for detecting different kinds of Pneumoconiosis from chest X-ray data. This will contribute to the Sustainable Development Goal 3 of ensuring healthy lives and promoting well-being for all at all ages, and present the framework for data collection and algorithm for detecting Pneumoconiosis for early screening. The baseline results show that the existing algorithms are unable to address this challenge. Therefore, it is our assertion that this research will improve state-of-the-art algorithms of segmentation, semantic segmentation, and classification not only for this disease but in general medical image analysis literature.