Artificial Intelligence (AI): A Dynamic Rebar for Radiology

By CIOReview | Tuesday, April 4, 2017

The application of radiology is growing in tandem with scope for medical treatment. Though medical practitioners apply various techniques to understand the severity of any disease, radiology has been at the forefront of medical imaging techniques in terms of veracity and precision that could subsequently lead to better medical decision making. Radiologists apply their experience and training to analyze computed tomography (CT) scans, Magnetic Resonance Imaging (MRI), and X-rays. They examine the medical images and determine the stage of ailments.

Mobility and Cloud are also increasing the usage of radiology

As mobility and cloud have become fundamental elements in several medical applications, real-time transmission of radiology reports is no longer a fairy tale. Radiologists regardless of their geographical location collaborate with emergency department at a hospital and immediately report their findings after examining medical images. As CT, MRI, ultrasound, and nuclear medicine scans are directly transferable, medical data transfer no more remains a conundrum. The ease of transfer aligns with the growing dependence on radiology reports that give accurate picture of patient’s medical condition. Cloud technology facilitates the storage of the medical imaging reports and solves the conventional problems that arise from the storage and discovery. With the ability to access key medical data from any mobile device, enhanced patient care is achieved through seamless patient data access.

Artificial Intelligence: The next big thing in healthcare

The growing volume of radiology reports creates a challenge for radiologists as the analysis of radiology reports is cumbersome process and involves significant investment of time and effort to infer them accurately. Radiologists are not unaware to the concept of Artificial Intelligence (AI) as they are already well versed with techniques such as CT scan which involves a combination of X-rays and computing algorithms to photograph human body. AI can simplify report analysis as scanned medical reports can be searched for basic patterns. Radiologists can arrive at primary conclusions at a faster rate with rule engine. The application of AI can also reduce the errors that arise from examination with the human eye. Examination of medical images can be performed quickly if AI is integrated with big data, thereby facilitating data discovery.

But, are the Radiologists Willing to Embrace AI?

The fraternity of radiologists is paranoid of AI as it thinks that the application of AI in radiology examinations would gradually shrink the human role and threaten their livelihood. However, they should ponder over few recent developments. A group of radiologists conducted a collective study and adopted the concept of Swarm AI. Using the concept of collective intelligence, the group successfully deduced skeletal defectiveness. The collective intelligence not only helped to speed up the examination process but also increased the accuracy. Of late, IBM has been offering its Watson platform—an AI powerhouse—in areas that range from spotting cancer patterns to decoding genome sequence of drug resistant tumors. IBM is also facilitating evidence based cancer care and forging partnerships with medical institutions and enterprises in the same pursuit. Startups are contributing significantly to AI and Enlitic can be the best example for it. Enlitic interprets a medical image in milliseconds and equals the work of 10,000 radiologists in terms of speed. To sum up, AI’s analytical role is set to grow in the upcoming days.

Role of Radiologists in future

For radiologists, the best recourse in this scenario is to understand AI’s scope of application. Radiologists should not become insecure over the growing influence of AI; instead, they should perceive AI as supplementary to their skill set. With the assistance of AI-based applications, fraternity of radiologists can expedite their work and solve more cases as AI can save their time and cost in the referential work. At the end of the day, radiologists should explore ways to apply AI for their own benefit.