Artificial Intelligence to optimize radiology workflows to reduce the turnaround time

Artificial intelligence has rapidly helped radiologists reduce their workload. Artificial Intelligence (AI) has also helped in reducing waiting time for the reports and scans of different diagnoses. Experts in radiology are working on identifying opportunities for machine learning and neural networks to maximize time and optimize the radiology domain’s workload. However, the radiology domain has now warmed up to the concept of artificial intelligence. Identifying opportunities to reduce ineffective radiology workflow strategies through artificial intelligence (AI) integration is a significant challenge. 

Artificial intelligence has helped the radiology domain in several aspects. These aspects are explained below:

• Optimization of workflow and detection: As AI helps in detection, it has made it easier to prioritize patients and get a diagnosis sooner.
• Acquisition of the image: AI helps acquire and take over all the challenges in obtaining high-quality images using modality and imaging protocols. 
• Reporting: The lengthy and strenuous task of making reports, which is very prone to errors, is taken care of using AI precision. 
• Registrations and monitoring: Keeping track and checking various consecutive scans’ progress is very efficiently managed by AI. It can also keep track of registrations. 

The use of the flagging tool has helped prioritize tasks and assist in more efficient functioning in the healthcare sector. Time is efficiently managed, and accurate results are obtained using AI. Artificial intelligence presents flags from machine learning algorithms to help the radiology domain reduce image wait time and turnaround times.