Top 5 Radiology Technology Trends to watch out for in 2020

In the last few years, technological advances in combination with innovative thinking have led to changes in the radiology industry, which has provided both radiologists and patients with major advantages. These solutions enable radiologists to deliver services of higher quality more quickly than ever before, while also helping to prevent industry burnout in the workplace. 

It can be a challenge to determine which technologies to add to your diagnostic toolbox. Radiologists should educate themselves on what is available to take full advantage of these and then equip themselves with the solutions that will give them the maximum value. As a natural consequence, these radiologists will be able to compete better in terms of price and value, while offering a better level of service to patients than to those who do not follow these new approaches.

Below are industry technology trends to watch out for in 2020 as we look towards a new decade.

• Hyper-automation 

In order to automate tasks, we make use of the technology called hyper-automation. By applying modern capabilities, such as machine learning ( ML) and artificial intelligence ( AI), procedures that typically require humans can now be finished via automatization. This technology can be used to automate various instruments, which is important because a single instrument can not substitute a human being in the correct manner. 

It is important to know that the implementation of a combination of tools, including robotic process automation (RPA), intelligent business management software (iBPMS), and AI, is required for successful hyper-automation. Inevitably, the aim is to increase AI-driven choices.

• Artificial Intelligence and Machine Learning 

In radiology, the uses of AI and ML are breaking barriers and offering major improvements in overall productivity, amount, and quality. This involves improving diagnostic tests, enabling ongoing training, and ensuring that adequate safety measures are in place.

• AI assistance for diagnostics enhancement
• AI will allow for constant training
• AI safety is paramount

• Automatic Reallocation 

Although there are a few AI-assisted workflow optimization techniques that are going through the testing stage, quite a few more are still being created. In order for these systems to be advantageous, however, they must be based on the amount of work of the radiologist and be capable of the following: 

• Automatically scheduling and organizing examinations using various variables such as time of day, department, and specialty. 
• Automatically balancing workloads by matching the workload of the radiology exam with reading capacity.
• Escalate and assign studies automatically based on the radiologist’s availability, location, sub-specialties, and time of day. 

• Cross-discipline Collaboration Within Departments 

The industry has become increasingly dependent on collaborating with other specialists in order to provide optimal care, giving radiologists the potential to take the lead in cross-disciplinary workflow management. Radiologists will have no problem applying integrated diagnostics using digital technology since radiology is one of the most IT-knowledgeable disciplines within the healthcare industry. Now that pathologists are trying to move towards digitally reviewing images, a more disciplined process is on for the purpose of cross-collaboration.

• Mammography Advances 

There are two key technological advances on the show floor, which include breast ultrasound and software for breast density, . There have also been several new studies presented at various places regarding mammography.

The automated breast ultrasound system, which it recently acquired with its U-Systems purchase, was highlighted by GE Healthcare. It is the first automated ultrasound system that is FDA-cleared to screen women who have dense breast tissue. The system is clinically demonstrated to boost cancer detection accuracy in women with dense breast tissue by 30 percent when used along with mammography. It decreases the fluctuation of the interoperator inherent in hand-held ultrasound systems.