Overview

Imagine a world where medical diagnoses are not only quicker but also more accurate, leading to better patient outcomes. Thanks to the integration of Artificial Intelligence (AI) in radiology, that future is near. As a medical doctor with an interest in radiology, I will help guide you through the relationship between radiologists and AI, highlighting the profound positive impact it is having on the diagnostic process.  

This article will explore how AI is reshaping the field of radiology. Radiology plays a crucial role in modern medicine by using imaging techniques like X-rays, CT scans, MRIs, and ultrasound to diagnose and treat various injuries and diseases. However, with increasing demands and challenges within the field, AI offers significant advancements by integrating machine learning and deep learning techniques. AI enhances diagnostic accuracy by efficiently identifying and differentiating structures in images, identifying subtle abnormalities that might be overlooked by human eyes alone. Furthermore, AI optimises workflow by automating routine tasks, enabling radiologists to dedicate more time to patient care. Through AI's capabilities in image analysis and data integration, radiologists can provide faster and more accurate diagnoses, leading to improved treatment outcomes and patient care.

The role of radiology in modern medicine

Radiologists are medical doctors specialising in diagnosing and treating injuries and diseases using medical imaging. Since the inception of the X-ray in 1895, radiology has been at the forefront of adopting breakthrough technologies in physics and engineering. Various imaging techniques, such as X-rays, CT scans, MRI, and ultrasound, are used to visualise the internal structures of the human body and diagnose conditions like broken bones, cancers, and other diseases. The resulting images form the foundation for accurate diagnoses and prompt treatments. Radiology provides essential diagnostic information to various departments, making it a crucial component in the medical field. It plays a vital role in the diagnostic process across all medical specialities.

The medical imaging workflow involves identifying the patient who needs imaging, preparing the equipment and the patient, analysing the results, and then sharing the findings with the patient. As the demand for imaging studies rises alongside an ageing and growing population, the radiology profession faces various challenges. Radiologists must adapt to maintain their output while continuing to improve the quality and efficiency of their practice. 

How AI is revolutionising radiology

AI refers to the capabilities and functions of computers that allow them to engage in human-like thought processes and mimic human intelligence through learning and adapting.¹ Given that most of the data in imaging is digital, AI systems can be developed to aid in analysis. 

In the context of medical imaging, we focus on two branches of AI: machine learning and deep learning. Machine learning focuses on teaching a computer to learn from previous examples until it is able to recognise patterns. Deep learning involves computer programmes that mimic how the brain works by forming neural networks. In the same way, our brains have neurons that communicate with each other to process information, and deep learning programmes have artificial neurons, making neural networks, that work together to solve problems. 

The concept of AI in radiology has been in development since the 20th century. The development of computer-aided diagnosis (CAD) began in the early 80s as a potential smart assistant for doctors.² In the last 20 years, radiologists have improved CAD tools using machine learning to combine various data and make more diagnoses accurate. This integration makes radiology services more efficient by using AI to assist in the workflow.³

Application of AI in radiology

Radiologists rely on a wealth of expertise from a lifetime of interpreting images to make educated decisions. However, the amount of data they process over their career is trivial compared to the amount a computer can be trained on whilst maintaining perfect recall. AI improves diagnostic accuracy and speed through various processes:

Image enhancement 

AI improves the clarity and detail of medical images. This is achieved by removing ‘noise’, similar to editing a photo to remove blurriness, and increasing spatial resolution, similar to zooming into a photo without losing clarity. This results in sharper images that reveal finer details in tissues and organs.

Image segmentation and classification

Image segmentation identifies and isolates specific regions of interest, such as tumours, organs, or blood vessels, from the rest of the image. These regions of interest can be classified into normal or abnormal findings.⁴ AI reduces the time spent on insignificant details by rapidly recognising changes from previous medical images.

Radiomics

Radiomics integrates the data from imaging, disease and genetics. It involves the extraction of additional data from the images, so-called ‘hidden data’, that is not readily apparent to the human eye, such as shape, texture and intensity. This data can be applied to medical decision-making using AI.

Workflow optimisation

As described above, the workflow of medical imaging involves many steps. AI can control intelligent choices of protocols and calibrations, select optimal imaging techniques for targeted imaging, adapt to individual variations in anatomy and patient needs, and avoid technologist errors. This results in higher throughput and improved image quality. 

AI case studies in medicine

There is an increasing body of research across various medical specialities that support the accuracy and efficiency of detecting diseases. As the quantity and quality of this research grows, the confidence to apply these techniques more frequently will become common. 

Brain scans

• Early detections and differentiation of strokes which are a medical emergency when the blood supply to part of the brain is cut off⁵
• Early detection of neurodegenerative disorders are conditions that gradually damage and kill nerve cells in the brain and nervous system e.g Alzheimer’s and Parkinson’s disease⁶

Heart scans

• Coronary artery disease is where the heart blood vessels become narrowed or blocked⁷
• Vascular abnormalities whereby vessels can become narrow, twisted, blocked or have weak points which can bulge or burst⁸

How AI enhances the level of care provided

Most importantly, AI will be a tool that not only assists radiologists but also enhances the level of patient care. By efficiently handling repetitive and routine tasks, AI allows radiologists to focus on more valuable aspects of patient care. Empowering radiologists with AI tools enables them to provide significantly more timely and detailed information to the healthcare team, improving patient outcomes. This results in more face-to-face interactions to guide imaging decisions and more effective communication of results, emphasising our role as doctors serving our patients. Ultimately, this will lead to earlier disease detection, better treatment options, and improved patient outcomes.

Summary

AI is significantly enhancing diagnostic accuracy and speed in radiology, addressing the growing demand for medical imaging by integrating machine learning and deep learning technologies. These advancements allow for precise identification of features within images, and the extraction of ‘hidden data’, leading to more accurate diagnoses and optimised workflows. AI not only aids radiologists by handling routine tasks but also improves patient care by enabling earlier disease detection leading to better treatment outcomes. As AI continues to evolve, staying informed about these technological advancements is crucial. The transformative potential of AI in healthcare is vast, promising a future where medical professionals can deliver more efficient and higher-quality care.