November 23, 2022
Within the past decade, rapid technological advancements have happened in both medical imaging and artificial intelligence, causing the two to converge in recent years. Major advances in computing power coupled with enhanced access to data has accelerated AI research in the context of radiology. In this article, we dive deep into the applications of AI in radiology, discuss its benefits, and provide a market overview.
According to a recent study, there is a significant workforce shortage of radiologists: 1.9 radiologists per million people in low-income and 97.9 in high-income countries, respectively. UK researchers asked expert clinicians to classify more than 3,600 images of hip fractures. The study showed that clinicians recognized only 77.5% of images accurately, while the machine learning system achieved 92% accuracy. In a nutshell, the ever-growing demand for radiology coupled with increasingly more accurate AI-based radiology systems makes AI a saving grace for healthcare worldwide.
of radiologists use AI in their clinical practices
American College of Radiology
plan to purchase AI tools in the near future
American College of Radiology
AI in the global radiology market CAGR from 2021 to 2030
Vision Research Reports
The number of use cases of artificial intelligence software in clinical data science and radiology practice is growing. Let’s discuss the top 10 applications of AI in radiology:
Early detection
Improved prioritization
Improved accuracy
Optimized radiology dosing
Reduced radiation exposure
Enhanced image quality
Improved satisfaction
Faster diagnosis
Improved access to care
Improved reporting
AI in radiology
AI can more reliably detect diseases at early stages, preventing complications and dramatically improving patient outcomes
Most current AI applications in radiology provide estimates of how likely a certain patient is to have complications based on radiological imaging. For example, an AI system concludes that a breast lesion of a certain patient has a 10% chance of being malignant. A radiologist could opt for a biopsy, but the AI system may not understand the severity of the problem and deem a 10% chance of cancer insignificant for conducting a biopsy.
It is critical for developers and medical professionals to work in close collaboration. Insights from medical professionals can improve the performance of AI-based solutions.
AI has become synonymous with machine learning (ML), which encompasses many techniques used to create predictive models, including deep learning models. Today, the most popular way of mimicking human decision-making is artificial neural networks (ANN), which is a certain type of very flexible deep learning model.
ANN is inspired by the design of biological neural networks. In other words, this model tries to mimic the network of neurons that comprise the human brain so that it can solve problems similar to the way a human would.
Currently, the convolutional neural network (CNN), which is a class of ANN, is the most popular model for analyzing visual data, making it the most important component of AI in radiology.
While input from field experts is always critical to the development of AI solutions in virtually any industry, medical imaging calls for radiologists to have a much more decisive and significant role in the development of AI-based radiology solutions. Engineers and data scientists lack an understanding of essential radiological and anatomical concepts, which is crucial to creating AI systems applicable in radiology. On top of that, radiology is a very nuanced medical field, where therapeutic methods tend to change every few years. It’s highly advisable to involve radiologists in every phase of development.
Healthcare providers should get key decision-makers and executive leadership on board with AI implementation. According to the latest report by Deloitte, healthcare providers who succeeded in AI implementation put leaders at the core of AI transformation. These leaders should clearly communicate with the radiology department on how new tools will impact their daily workflows.
In a recent study, radiologists received chest X-rays and diagnostic advice from human experts and from AI systems. The radiologists rated AI advice as lower quality, which highlights the persistent 'AI black box' problem. In healthcare, where decisions can directly influence one's well-being, it is paramount for radiologists to understand the underlying decision-making of AI systems. Most AI radiology tools use deep learning models, and system interpretability and transparency become crucial. While deciphering deep learning models is a universal task across many AI applications, developers are responsible for minimizing bias and making models as explainable as possible. It comes down to continuous algorithm validation, dataset audit, evaluation of performance, standardized documentation, and routine bias assessment.
AI-based radiology tools are already used in clinical practice every day, and their role will increase moving forward. The validation of existing AI applications and development of new ones for solving common radiological problems requires effective collaboration between radiologists, data scientists, and engineers.
AI will make the next two decades transformational for healthcare and radiology in particular. Our professional AI consultants can help you implement AI-based technology into your radiology practice.