AI is making healthcare smarter. AI is changing radiology.
The promise of artificial intelligence (AI) has captured the interest and imagination of almost every industry, and healthcare is no exception. Radiology is a field that has already been impacted by AI innovation. In fact, about a third of radiologists now use AI in their practice, according to a recent study by the ACR Data Science Institute.
Here are three ways in which AI is changing the radiology we know.
1.Marking abnormalities and prioritizing workflows
A radiologist combines knowledge, experience, and good instincts in the process of reading medical images, often analyzing different details of the image to draw conclusions. Artificial intelligence is far from being able to replicate this ability, but AI has proven to be able to detect some potential problems in medical images. For example, artificial intelligence tools such as MammoScreen and CMTriage from CureMetrix can assess the likelihood of malignancy on mammograms.
2. Sorting out emergencies
During the Covid crisis, different regions were flooded with patients and needed help with triage — especially in the early months of the pandemic, as health workers were still learning how to diagnose and treat the disease.
Hospitals and clinics overflowing with patients with Covid symptoms needed to be able to quickly identify which patients needed immediate medical attention, and imaging — especially chest X-rays — proved to be one good method of recognizing alarming symptoms.
This need has inspired medical start-up Qure.ai to change the purpose of its Artificial Intelligence-based chest X-ray machine to look for signs of Covid, enabling multidisciplinary doctors around the world to better manage cases. This is a fantastic example of how AI can be used to help medical professionals in emergency situations.
3. enabling easy sharing of images between professionals and patients
Have you ever been asked by a new doctor to bring along your previous medical images? More and more radiology departments are posting images online to take advantage of new technological innovations, allowing radiologists to easily share images with other healthcare providers and patients. This requires the adoption of a cloud computing platform, a sector that is growing rapidly as businesses across all sectors seek to become more interoperable.
Some radiology practices, especially smaller ones, may be reluctant to move to a new platform because of concerns about cost or the effort required to adapt everyone to the new system. However, cloud platforms are essentially the critical infrastructure needed to use AI effectively, so a strong business case needs to be made for investing in such a move, especially as AI will continue to become more pervasive.
