How Does AI Reshape Radiology & Medical Imaging?

 Except for the geopolitical discussions, if we consider the hottest topic these days, then it has to be AI. Being in mass discussion since last decade, today AI has stepped into every industry, be it film or healthcare. With a global market size of $19.27 billion, the upcoming market is going to boom at a record pace. And the key player in this market is Canada, which is expected to grow at a CAGR of 43.1% by 2028. Talking about the number of AI-driven healthcare organizations, Canada ranks at the top with 22 leading companies, whereas in the USA and India, the number stands at 10 and 8 respectively. In a nutshell, whether it’s a developed nation or in the developing stage, AI healthcare is the next big business in the future. 

But What’s the Big Deal in Incorporating AI in Healthcare?

The answer lies in the way AI has been transforming the same. Whether you are there in healthcare for a simple test or have to undergo a massive surgery, AI just simplifies your experience without consuming much time. The largest contribution of AI has been made in the field of radiology, which is also known as the prime speciality department when it comes to medical emergencies.

How Does AI Shape Radiology?

The primary duty of any radiologist in the healthcare sector is to diagnose the patients, create and interpret medical images, take care of the patients, and make effective diagnostic decisions. With the incorporation of AI, all these processes get simplified and efficient. It’s not like earlier the radiologists were inefficient, but now they have become more productive. How?

More Accurate Diagnosis

Radiology softwares are driven by AI that incorporates a wide range of algorithms coupled with deep learning and big data analytics. These technologies analyse vast amounts of medical imaging data throughout the web and respective authorized platforms with incredible speed and precision. If there is a slight abnormal action that may escape human observation, the radiology softwares detect those and report it to the authorised specialists to prevent any misfortune. Thanks to the continuous learning process from the vast amount of data available, AI-driven radiology softwares can improve diagnostic outcomes over time, reducing the chances of any casualties and providing more reliable interpretations of medical images.

Integration of Predictive Analytics & Optimized Workflow

Due to their wide command of the vast amounts of data available, radiology softwares can analyse historical data and imaging results of respective patients, and based on those, can predict disease progression, treatment outcomes, and potential complications. Moreover, these radiology softwares streamline the workflow and increase efficiency, by prioritising urgent cases by flagging critical findings for immediate attention. These reduce turnaround times and improve patient care. The integrated algorithms automate routine tasks such as image analysis and report generation, which enables radiologists to focus more on critical patients. The radiology softwares send the respective reports on the cloud of the authorized platforms so that authorised people such as doctors, nurses, and radiologists can access the same for later findings.

What Is the Role Of AI in Medical Imaging?

While earlier and even now in some healthcare, radiologists interpret medical images such as X-rays, CT scans, and MRIs to make a constructive decision, with AI, this has become quite easy. To interpret any medical image, artificial intelligence dives through 5 processes, i.e., object detection, object processing, segmentation, identification and classification. 

The first step of artificial intelligence in medical imaging is to acquire images from the available databases. These images are captured with real-time image-capturing tools like MRI, CT-Scan, Radiology, etc. Next, the algorithms process the images to make it more evident to identify shape, colour, and texture by reducing noises and distortions. However, most of the time, the whole image is not required. So in segmentation, artificial intelligence in medical imaging crops the important area and eliminates the rest. Then in identification, AI again clarifies the cropped image for better visibility and identifies the focused features in the image. In classification, artificial intelligence in medical imaging refines all the extracted features by selecting and classifying methods using deep learning techniques such as Decision Tree, Naïve Bayes, Logistic Regression, etc. These methods read 70% of the data to categorise the disease, whereas the rest 30% is used to predict the disease.

Conclusion

When it comes to medical imaging and radiology, the role of AI is revolutionary. AI has simplified processes so much that today we just can’t think of a day without artificial intelligence. From diagnosing to optimising workflow, AI has just harnessed the game. Among the top AI healthcare providers, MLHealth 360 is a revolutionary organization that is the first in its class to incorporate the Scaida platform, which in itself is a huge success of AI. Scaida being completely cloud-based is quite inexpensive, leading to more affordable treatments in MLHealth 360. Its capabilities make diagnostics faster, more reliable, and accessible to more patients globally.