AI can reduce the need for CT scans, which expose patients to higher levels of radiation than other ... [ ] imaging techniques. AI's potential in medicine has already attracted large amounts of media and public attention. However, some of the specific uses and consequences of AI in the context of health care aren't particularly well known, at least not compared to awareness of the general utility of artificial intelligence. Some of these uses may be life-saving, and in more ways than one. In recent months, a small number of companies and researchers have begun using AI for the purposes of medical imaging, harnessing machine learning algorithms in order to construct 3D models out of 2D images.

AI's potential in medicine has already attracted large amounts of media and public attention. However, some of the specific uses and consequences of AI in the context of health care aren't particularly well known, at least not compared to awareness of the general utility of artificial intelligence. Some of these uses may be life-saving, and in more ways than one. In recent months, a small number of companies and researchers have begun using AI for the purposes of medical imaging, harnessing machine learning algorithms in order to construct 3D models out of 2D images. This may seem like a fairly standard employment of AI at first glance, but what's special about this emerging use is that it's focused on avoiding the need for MRI, CT and PET scans.

AI can reduce the need for CT scans, which expose patients to higher levels of radiation than other ... [ ] imaging techniques. AI's potential in medicine has already attracted large amounts of media and public attention. However, some of the specific uses and consequences of AI in the context of health care aren't particularly well known, at least not compared to awareness of the general utility of artificial intelligence. Some of these uses may be life-saving, and in more ways than one. In recent months, a small number of companies and researchers have begun using AI for the purposes of medical imaging, harnessing machine learning algorithms in order to construct 3D models out of 2D images.

Medical imaging provides a window into the human body, allowing us to see under the skin. But to really understand what's going on inside our bodies, doctors need 3D imagery. And there's no time this would be more helpful than during surgery. Now, ImFusion, a Munich-based startup and NVIDIA Inception program member, is taking medical imaging into the next dimension. It's using AI to turn 2D ultrasound data into 3D images.

One of Medicine's greatest breakthroughs was providing a window into a patient's anatomy through medical imaging. Today, an innovative startup is taking this one step further by turning two-dimensional ultrasound data into 3D images and fusing them with CT scans or MRI images. Munich-based ImFusion wants to assist surgeons by allowing them to combine images from different modalities in real time. While CT scans and MRIs are extremely helpful in identifying and localizing pathology, such as a fracture or a tumor, they cannot be used intra-operatively. With ImFusion's AI algorithms, surgeons now have more information at their fingertips. A 3D ultrasound image obtained intraoperatively can be superimposed, in real time, onto the previously obtained CT image, effectively combining the images into a single comprehensive view.