Over the years, the integration of various currently available imaging modalities has increased the complexity of clinical decision making for the radiologists. Moreover, the shortage of healthcare workforce, especially in developing countries, has necessitated the use of automated radiology workflows, which is further possible using AI. Hence, the integration of AI in the field of imaging is highly important since it holds the potential to assist the radiologists in carrying out accurate diagnosis and facilitates disease detection.
AI-based solutions are utilized for a range of applications ranging from image detection to diagnosis and decision support, image acquisition, reporting and communication, triage, image analysis, and predictive analysis and risk assessment, among others. AI algorithms help in identifying patterns in medical images as they are conceptualized to study vast number of diagnostic studies and images, thus aiding in detection of abnormalities.
On March 4, 2021, DiA Imaging Analysis announced its collaboration with Royal Phillips to provide hiqh-quality ultrasound imaging with artificial intelligence (AI)-based image quantification. DiA Imaging is a leading organization known for providing AI-powered solution for ultrasound. Royal Philips is a leading brand name known for its technology solution in the healthcare arena.
According to the market intelligence published by BIS Research, the global AI-enabled imaging modalities market is expected to reach $2.64 billion by 2030. The market is expected to witness a growth of 23.32% during the forecast period, 2020-2030.
The market growth can be attributed to factors including increasing need for personalized and standardized patient-centric care, the number of accurate and rapid diagnosis enabled by artificial intelligence, increasing workload of radiologists, and the ever-evolving hardware technologies and data quality.
In addition, there are certain opportunities presently that can further boost the market growth, including increasing standardization of radiology practices, increasing voluminous medical imaging data requiring analysis, gradual decline in imaging reimbursement forcing facilities for accelerated and accurate performance, and the increasing participation from various organization in the robotics and imaging industry. Moreover, AI-based solutions are used for improving the quality of image. These solutions also help in reducing the MRI timings, and in the reduction of radiation dose. Owing to these applications, AI has been rapidly employed for improving patient care and in reducing the incurred costs in medical imaging procedures. Thus, numerous established as well as emerging companies are venturing into the AI space to earn huge profits and utilize AI to the maximum potential.
