AI algorithms offer numerous benefits in medical imaging, significantly enhancing the field's capabilities. They improve diagnostic accuracy by detecting subtle patterns in medical images that may be missed by human radiologists, leading to more precise diagnoses12. Additionally, AI accelerates the analysis process, reducing the time required to diagnose conditions and enabling faster treatment34. This efficiency is particularly beneficial in high-stakes scenarios, such as cancer detection, where timely intervention can save lives3. Furthermore, AI facilitates personalized medicine by tailoring treatment plans to individual patients based on their unique medical images and characteristics15. This technology also increases accessibility to high-quality diagnostic services in remote or underserved areas, where specialized radiologists may not be available14.

AI's prowess in disease detection spans multiple medical fields. In oncology, AI algorithms excel at identifying cancerous lesions in mammograms, ultrasounds, and MRI scans, often with higher accuracy than human radiologists12. For cardiovascular diseases, AI can detect blockages and abnormalities in arteries through CT scans and other imaging techniques, enhancing early diagnosis and treatment planning34. Neurological conditions, such as Alzheimer's disease, are also being diagnosed more effectively by analyzing brain scans with AI, which can identify early signs that might be overlooked by the human eye56. This technology's ability to process vast amounts of imaging data quickly and accurately is transforming the landscape of disease detection and patient care78.

Despite its transformative potential, AI in medical imaging faces several significant challenges. High-quality data is paramount; AI algorithms require extensive and diverse datasets to produce accurate results, but these can be difficult to obtain due to privacy concerns and the need for standardized data formats12. The complexity and opacity of AI models, often referred to as "black boxes," make it challenging for clinicians to interpret how these algorithms reach their conclusions, potentially hindering trust and adoption34. Additionally, the regulatory landscape for AI in healthcare is still evolving, necessitating robust frameworks to ensure the safe and effective deployment of AI technologies in clinical settings52. Addressing these challenges is crucial for the continued advancement and integration of AI in medical imaging.

The future of AI in medical imaging is poised for significant advancements, driven by integration with electronic health records (EHRs) and the development of multimodal imaging techniques. By combining AI-powered imaging with EHRs, healthcare providers can achieve a more comprehensive view of patient health, facilitating better-informed clinical decisions and personalized treatment plans12. Additionally, AI's ability to analyze multiple types of medical images, such as X-rays and MRI scans, will enhance diagnostic accuracy and provide a holistic understanding of patient conditions13. Researchers are also focusing on explainable AI, which aims to make AI algorithms' decision-making processes transparent and understandable, thereby increasing trust and adoption among clinicians45.

The application of AI in pediatric imaging is still in its nascent stages, primarily due to the unique challenges associated with pediatric patients. Unlike adults, children exhibit a wide range of body sizes, growth patterns, and disease manifestations, making it difficult to apply adult-focused AI algorithms directly to pediatric cases12. Additionally, the lack of child-specific datasets and the smaller market share for pediatric AI tools have hindered development3. Despite these challenges, AI holds significant promise for improving pediatric radiology by enhancing diagnostic accuracy, speeding up patient care, and providing predictive analytics for early disease detection3. To ensure the safe and effective use of AI in pediatric imaging, it is crucial to design, train, and validate algorithms specifically for children, and to advocate for regulatory changes that mandate pediatric applicability labeling on AI tools23.