Artificial Intelligence is continuously revolutionising the medical domain. Since the 70s, many researchers have taken an interest in applying Artificial Intelligence in life sciences. Moreover, the modern AI aims to provide solutions for the practical healthcare problems. Emerging technologies such as deep learning have made a deep impact in healthcare, specifically when using AI in radiology.

Radiology is the area of medical science that uses radiation to generate medical imaging like X-ray, CT scans, Ultrasound and MRI images to discover abnormalities and tumours. AI algorithms can automatically detect complex anomalous patterns from the image data and assist patients in diagnosis.

According to the American Department of Radiology, AI adoption in radiology surged from 0 to 30% from 2015 to 2020.

Scarcity of radiologists

Radiologists who efficiently observe and cross-check patient scans are in short supply worldwide. Hence, researchers are studying the possibilities and potential of AI to support the current professionals and reduce the workload.

According to a report published in the Lancet Oncology Journal, AI could help lighten the burden on radiologists, and AI-integrated screening managed to detect immense likeable symptoms of cancer as well. Another report published by MIT and Harvard Medical School researchers revealed that AI could affect the diagnoses done by doctors, and trust issues may arise when we opt for human professionals. Regarding this, Indian doctors opined that technologies like AI were already in use but with some amount of human intervention.

Pros and Cons

Since radiology generates a massive amount of clinical image data, Radiologists will be expected to spend a significant amount of time sorting out these images, writing analysis and arriving at a final diagnosis. However, these medical images can also be processed and observed through computer vision, which can quickly and accurately predict diseases.

Benefits

Artificial Intelligence in Radiology brings many benefits for Radiologists, like ease in their work. A few of them include:

• Accurate classifications- the deep learning-integrated specialised Computer Vision algorithms will be able to differentiate even the minute abnormalities and provide them with a precise classification comparable to that of humans and sometimes even better.
• Enhanced Analysis- Deep learning architectures like U-Net focus on the automated segmentation of medical images and the usual classification process. This segmentation helps to improve image analysis and assists the practising radiologists. Such models also suggest that radiologists have another opinion regarding analysis and boosting confidence during their diagnosis. They can detect even minute anomalies that are not obvious to the human eye.
• 3D model generation- 3D modelling benefits from AI as these models can segment medical images with accuracy and fuse several segments, fed to third rendering software for reproduction. These models are also helpful for the additional analysis of radiologists.
• Immediate results- Besides assuring an accurate result, AI models perform tasks with the appropriate hardware within a few seconds. This will boost the radiology practices and reduce stress on the practitioner.

Challenges

Though AI integration in radiology benefits radiologists, there are also some concerns.

• Low standardisation- As per a presentation of SWOT analysis for AI in medical imaging by Martin-Valdivia and Luna, they show the lack of standardisation as one of the significant challenges and weaknesses of AI when it comes to radiology. The lack of standardised benchmarks hinders the comparison or authentication of the performance of any model. Hence, without proper validation, it is difficult to decide if a model is ready to perform or not.
• Risk with explainability- Model explainability is a serious concern. The interpretability of an AI algorithm is critical in clinical data science. Deep learning algorithms implement neural network architecture and process data sets with a wide range of neurons. Hence, it is difficult for humans to understand such complexity. Such a lack of reasoning triggers questions on the reliability of AI models. A slight mistake can cause adverse effects, making interpretability more significant in clinical practices.
• Privacy concerns- As medical researchers access a patient’s data for training AI models, it becomes a privacy concern. This may not be appropriate for the people who value their privacy and, therefore, hinders practical implementation. Hence, ethics are vital to ensure that the privacy of patients’ data is secured while using AI. 

• Photo by National Cancer Institute on Unsplash
• https://www.v7labs.com/blog/ai-in-radiology#:~:text=Radiology%20is%20the%20field%20of,an%20assistive%20diagnosis%20for%20patients
• https://www.thehindu.com/sci-tech/technology/todays-cache-ai-versus-human-radiologists-hollywood-writers-call-for-streaming-giant-regulation-apple-boosts-iphone-15-production-in-india/article67208576.ece