The global AI in medical imaging market, currently valued at USD 1.75 billion, is projected to reach USD 8.56 billion by 2040, growing at a compound annual growth rate (CAGR) of 30%. This expansion is fueled by rising diagnostic volumes, a critical shortage of radiologists, and the increasing integration of artificial intelligence into telemedicine and remote healthcare services. AI technologies, particularly deep learning, convolutional neural networks, and generative adversarial networks, are transforming medical imaging by enabling faster, more accurate detection of abnormalities in X-rays, MRIs, CT scans, ultrasounds, and pathology images. These systems can identify subtle patterns in large volumes of unstructured medical data—often beyond the capability of human experts—leading to earlier and more precise diagnoses. Key drivers include the growing number of imaging procedures, the need to reduce radiologist workload, and the push for personalized and precision medicine. AI tools are also enhancing imaging quality by reducing noise and minimizing radiation exposure, especially in mammography and CT scans. Supportive government policies, strategic partnerships between healthcare providers and AI developers, and strong venture capital investment are further accelerating adoption. Technological advancements are making AI more effective and trustworthy. Explainable AI is improving transparency, helping clinicians understand how decisions are made. Federated learning allows models to be trained across multiple institutions without sharing sensitive patient data, addressing privacy concerns. Real-time AI integration is also improving surgical guidance and procedural accuracy. The competitive landscape features established medical technology leaders such as GE Healthcare, Siemens Healthineers, and Philips, alongside innovative AI-focused startups like Aidoc and Arterys. Differentiation is increasingly based on clinical validation, model explainability, and open platform adoption. Regionally, North America currently leads the market, supported by advanced healthcare infrastructure, strong R&D investment, and a favorable regulatory environment. The United States benefits from a concentration of tech companies and a high prevalence of chronic diseases, increasing demand for early diagnostic tools. However, Asia is expected to emerge as a major growth region due to rapid digital transformation, a large population, and increasing healthcare spending. Market segmentation highlights key application areas such as lung infections, brain injuries, lung cancer, cardiac conditions, bone deformities, and breast cancer. The types of images processed include X-ray, MRI, CT, and ultrasound. Despite the promise, challenges remain. High implementation costs, data security risks, regulatory hurdles, and difficulties in integrating AI systems with existing hospital IT infrastructure slow adoption. There is also a growing need for trained AI professionals and clearer regulatory frameworks. Overall, AI in medical imaging is poised for transformative growth, offering solutions to critical healthcare challenges while improving patient outcomes worldwide.