The global artificial intelligence in medical imaging market size is estimated to be worth over $14,200 million by 2032, increasing from $762.84 million in 2022.
The global AI in medical imaging market is expected to expand at the double digit CAGR of 33.1% during the forecast period 2023 to 2032, according to a report done by Precedence Research.
One of the most promising areas of health and medical innovation is the use of AI in medical imaging.
According to a NIH report, machine learning, a subset of AI, also known as the traditional AI, was applied on diagnostic imaging starting in the 1980’s within fields like radiology and oncology.
Though AI plays a significant role and is evolving in medical imaging research today, implementing AI in medical imaging research faces challenges.
These challenges can lie in organizing and pre-processing data from different institutions, hindering widespread predictive modeling.
While breakthroughs like "deep patient" offer a method for general-purpose patient representation, adapting it to diverse clinical settings remains complex, according to the NIH report.
Authors of the report stress that image data sharing poses policy and infrastructure challenges, addressing HIPAA compliance and inadequate systems.
Achieving true AI intelligence comparable to human decision-making requires addressing not only data and algorithm challenges but also ethical regulations, acknowledging the unique life experiences and philosophies involved in medical imaging decisions.
While significant leaps are being made in this space in addressing its challenges, increases in the market are a result. These increases include:
AI in medical imaging has revolutionized medicine, enhancing diagnostic accuracy and treatment efficiency, according to the report.
Utilizing advanced deep-learning algorithms, it accelerates image interpretation, aiding doctors in swift decision-making during CT scans or X-rays.
AI's efforts prove crucial in monitoring subtle changes, particularly in conditions like brain tumors and cancers, contributing to precision medicine.
This technology lifts the workload on medical professionals, automating analysis and addressing the global shortage of healthcare personnel.
Overall, AI in medical imaging significantly improves patient outcomes through faster diagnosis, individualized therapies, and precise interventions, especially in critical care scenarios like strokes, where it expedites diagnosis and saves valuable