The use of AI in radiology has advanced the field beyond measure. It has completely changed how doctors arrive at diagnoses. The healthcare sector was one of the first to employ and incorporate AI techniques. Therefore, the concept of AI is not new to radiology.
Healthcare and radiology, in particular, were always priorities when developing AI models. Today, radiologists are using AI models to process simple tasks without much interference from themselves. This means that radiologists could prioritize their duties and focus more on complex cases.
A task like measuring the circumference of a baby’s head while inside the womb used to be done manually. This means that the doctor only had scans and images to measure it from. Today, that task is done by an AI tool, much more accurately, by the way. This is just a small example of how AI has been changing radiology.
In this blog, we will be discussing how AI has changed radiology to accommodate better patient diagnosis. And that is an area in which AI focuses on to increase diagnosis accuracy. After all, an accurate diagnosis could determine whether a patient could live or die. AI models are able to diagnose diseases using medical images like X-rays, CT scans, MRIs, etc. with more accuracy than regular radiologists. Certain AI models have also helped in early disease detection, warning patients of diseases they could get in the future. Patients may experience better treatment outcomes if their diseases are discovered earlier.
The Modern Era of Radiology
With every revolutionary innovation, there are always the same few questions that bother us: what is it, how does it work, and why do we need it? Through this blog, you can learn how AI has transformed radiology.
What Is The Role Of AI In Radiology
Using AI algorithms, radiologists are now able to better analyze medical images like X-Rays, CT scans, MRIs, etc. These algorithms have proven themselves to be more efficient and accurate in diagnosing patients. Some of the commonly used algorithms in radiology AI models are deep learning algorithms like CNNs, RNNs, GANs, etc. Many of the simple functions that radiologists now perform, like image processing, abnormality detection, etc. can be automated using AI. By doing so, radiologists can focus on more complex cases and provide better care for their patients.
How Do The AI Models Function
For radiology-centered AI models, data scientists need to train these models. And to do that, large medical datasets prepared by medical data labeling experts are required to train the model. As the first step in the process of developing effective radiology-focused AI algorithms, this is a vital element. To guarantee that the AI model receives the data it needs to work properly, medical data labeling experts classify medical images like X-rays, CT scans, MRIs, etc. After studying all this data, they are then further trained by experts in the field to scan medical images and report their findings.
Why AI will become a necessity in Radiology
We have seen what AI is capable of doing in the radiology field. It would be unwise to not use AI to its full potential to diagnose patients. The ability to detect diseases before any symptoms show themselves is nothing short of marvelous, earlier disease detection means patients could achieve better results from their treatment. Moreover, hospitals have begun using AI models to perform simple tasks that don’t require a high level of expertise. Tasks such as preparation of reports, radiation dose optimization and other simple tasks are now being done by AI models. This means that radiologists have more time to focus on more complex cases.
Also, being able to streamline the day-to-day process helps radiologists prioritize their duties.
AI Models that Outperform Radiologists
Let us take a look at when AI models were better than radiologists at detecting certain diseases.
ChestX-ray14: In 2018, it was found that an AI model called ChestX-ray14 outperformed radiologists in detecting pneumonia on chest x-rays. The doctors were able to produce an accuracy rate of 93.8%, whereas the AI model was able to get an accuracy rate of 98.5%.
IDx-Lung: In 2019, the AI model called IDx-Lung was found to be more accurate in detecting lung cancer on low-dose CT scans. The model showed a success rate of 95.7% in accurate detection, whereas the radiologists were only able to get an 86.6% success rate.
MIAS: Short for Mammographic Image Analysis Reporting and Data System, is an AI model trained to detect breast cancer by studying mammograms. When put against each other, there was only a small difference between the results, with the AI model winning again with 96.4% and the doctors getting only 94.2%.
Viz LVO: LVO, which is short for large vessel occlusion, is a serious condition that can lead to stroke or even death. In 2021, the AI model known as Viz LVO outperformed radiologists in detecting this particular disease by 2.5% after getting an accuracy rate of 96.3%.
DeepVariant: In 2023, the AI model known as DeepVariant outperformed radiologists in detecting cancer on whole-genome sequencing data. The AI model was able to correctly identify cancer with an accuracy of 92.9%, while radiologists had an accuracy of 87.7%.
Lung Nodule Detector: Trained to detect lung nodules on CT scans, the model was put to the test against radiologists and came out victorious. The model was able to successfully detect lung nodules with an accuracy of 96.2%, while radiologists had an accuracy of 93.1%.
Medical Imaging and Diagnostics
Artificial intelligence (AI) is rapidly transforming the field of medical imaging. AI-powered tools are being used to automate tasks, improve accuracy, and provide new insights into disease. The use of AI in medical imaging has advanced significantly, giving radiologists tools for analysing and interpreting complex imaging data. By using AI models to process medical images, doctors have been able to detect diseases that are generally harder to detect in their initial stages.
The use of AI in medical image diagnostics is still in its early stages, but the potential benefits are significant. AI has the potential to improve the accuracy, efficiency, and personalization of diagnostic imaging. This could lead to earlier detection of diseases, better treatment outcomes, and lower healthcare costs.
Conclusion
In conclusion, the integration of Artificial Intelligence (AI) in radiology has undoubtedly revolutionized the field of medical imaging. Paving the way for more accurate diagnoses, streamlined workflows, and improved patient outcomes, AI will continue to advance radiology.
AI, with its ability to analyze vast amounts of data with exceptional speed and precision, has exhibited remarkable performance in radiology.
From identifying early signs of cancer to predicting disease progression and treatment responses, AI has never ceased to amaze us. It consistently demonstrates its potential to augment the expertise of radiologists and aid in clinical decision-making. It is evident that this technology has become an indispensable tool for radiologists and healthcare providers alike.
Through this blog, I hope I have enlightened you about how AI has changed radiology for the better.