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Artificial Intelligence (AI) is revolutionising industries across the globe, and healthcare is no exception. In particular, AI is playing a pivotal role in medical imaging, where its ability to analyse vast amounts of data quickly and accurately is transforming radiology. From improving diagnostic precision to streamlining workflows, AI-powered solutions are enhancing patient outcomes and redefining the future of radiology. This article explores the evolution of AI, its key applications in radiology, and how it is shaping the future of medical imaging.

What is AI?

Artificial Intelligence (AI) is the simulation of human intelligence in machines, particularly computer systems. It enables machines to process data, learn from patterns, and make decisions from the data collected.

A Brief History

AI's journey began in the 1950s, when John McCarthy led a workshop that formally introduced the concept. Since then, AI has evolved significantly, encompassing several key subfields:

• Machine Learning (ML): A subset of AI where computers use data and algorithms to mimic human learning, gradually improving performance over time.
• Deep Learning: A machine learning technique that processes vast amounts of data through neural networks (very similar to the human brain), helping AI systems refine their predictions and decision-making abilities.
• Generative AI: A branch of AI that creates new content (text, images, video, etc.) by analysing existing data. Generative AI models, identify patterns in large datasets and generate original content accordingly.

Key Applications of AI in Radiology

The use cases for AI in Medical Imaging have exploded over the past few years, particularly notable at RSNA where AI powered solutions took centre stage. With ongoing advancements, AI has the potential to enhance diagnostic precision, streamline workflows, and ultimately improving patient care.

1.  Triage and Prioritisation: AI models can quickly detect critical findings in imaging studies, allowing radiologists to prioritise urgent cases, improving workflow efficiency, and accelerating diagnoses.
2. Detection and Measurement Assistance: Advanced AI tools assist in image interpretation, highlighting abnormalities and providing precise measurements to support radiologists in making accurate diagnoses and treatment plans.
3. Seamless Workflow Integration: The "Radiology Reimagined: AI, Innovation, and Interoperability in Practice" initiative showcased the importance of integrating AI tools seamlessly into existing radiology systems, enhancing interoperability and efficiency.
4. Multimodal AI Applications: Vision-language models (VLMs) enable AI to generate radiology findings from medical images, answer visual questions, and assist with draft report generation, ultimately improving accuracy and efficiency.

The rapid advancements in AI are revolutionising radiology, bringing unprecedented improvements in diagnostic accuracy, efficiency, and patient care. By leveraging AI-powered tools, radiologists can enhance their workflows, reduce diagnostic errors, and provide faster, more reliable results. As AI continues to evolve, its integration into radiology will further enhance medical imaging, making healthcare more efficient and accessible for all. The future of AI in radiology is bright, promising continued innovation and improved outcomes for both medical professionals and patients alike.