Overview of Transformation in Radiology Through Artificial Intelligence

Analytics Insight provides an overview of transformation in radiology through AI

The scope of Artificial Intelligence is not only limited to certain industries and household chores but in all directions. The global market size of AI is increasing at an increasing rate due to digital transformation and globalization. The world has experienced how AI is transforming the healthcare industry, especially in the COVID-19 pandemic. It is known that radiology includes diagnosing as well as treating diseases or injuries through X-rays, Computed Tomography (CT), Magnetic Resonance Imaging (MRI), nuclear medicine, ultrasound, and many more. Radiologists get continuous exposure to radioactive rays that are very harmful to health. Analytics Insight provides an overview of the transformation in radiology through the implementation of Artificial Intelligence in recent years.

Radiologists are concerned whether Artificial Intelligence machines will take over their job opportunities in the nearby future. But it is a myth because the implementation of Artificial Intelligence will optimize the workflow through quantitative radiology by protecting the health of fellow radiologists. Artificial Intelligence is helping radiologists in different ways— detecting early-stage of cancer, auto-segmentation of organs in 3D models, NLP for reports, and many more. AI has raised a significant value to radiologists through accurate pieces of advice or insights regarding patients and their health issues.

• TOP 5 USE CASES OF ARTIFICIAL INTELLIGENCE IN MEDICAL IMAGING
• HOW TELEMEDICINE AND AI CAN BENEFIT HEALTHCARE PROVIDERS?
• CAN IOMT PROTECT HEALTHCARE SYSTEMS FROM CYBERATTACKS IN 2021?

Artificial Intelligence and machine learning algorithms are transforming the regular work of radiologists through appropriate communication, coordination, screening examinations like mammography, colonography, and chest CT, standardize reports as well as immediate alerts for critical patients. It is used for inventory and equipment management and maintenance efficiently and effectively. The AI models have started acting as assistants to senior radiologists in analyzing the medical records and real-time data of patients and detect any deterioration or improvement in a particular disease or injury. The Artificial Intelligence algorithms are here to guide radiologists in redefining their purpose and perceive critical data that are impossible for them to notice through naked eyes. Radiologists need to work long shifts daily which makes them tired and distracted. Artificial Intelligence is particularly helpful in this situation to make them notice certain issues in a patient's body as well as complete their mundane tasks.

Yes, when radiologists across the world are set to utilize smart functionalities of Artificial Intelligence for the utmost care of patients, there are certain challenges faced by them at the same time. One of the major drawbacks of algorithms is these machines do not have sufficient medical knowledge as per a radiologist or physician. Sometimes it is difficult for AI machines to understand the proper workflow in the radiology department. Secondly, these Artificial Intelligence machines are trained with historical medical data of different kinds. But, radiologists find unique and new types of symptoms of a disease that are not registered in the historical data. Thus, it is difficult to solve diseases and predict the curing process on the basis of one modality. Thirdly, AI machines work differently at different hospitals ie. the quality of outcomes may decline or improve, depending on the medical records and systems of hospitals. Hence, radiologists in different hospitals and clinics need to update the training dataset with their existing medical records respectively.

Artificial Intelligence has created a massive impact on radiomics which is a new field in radiology in recent years. Radiomics deals with the extraction of a high number of different features such as size, shape, and texture from medical images of patients. These features include spatial information on pixel or voxel distribution and patterns as well as provide support for the diagnosis of brain, heart, liver, prostate, adrenal gland, pituitary gland, and lung. The blend of Artificial Intelligence with radionics provides a smarter capability of managing enormous datasets efficiently than the traditional systems. It gives computational power in radiology and encourages radiologists to adopt radiomics and AI for better practice.

That being said, it is essential to incorporate Artificial Intelligence in radiology to transform the workflow efficiently, despite having a few barriers. It is useful for gaining trust and loyalty from existing and potential patients for a better cure as soon as possible.