Medical

• Image processing for clinical diagnosis: AI can easily analyze medical images obtained through various imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), and PET-MRI. What makes this possible is the use of state-of-the-art deep learning algorithms.

MRI: The main limiting factor for MRI is the slow acquisition speed. To solve this issue, AI reconstructs MRI images by utilizing the united imaging intelligence (UII) and united imaging healthcare (UIH) techniques to reduce the scanning time by 80%. In fact, these techniques can accelerate AI-based image reconstruction, partial Fourier transform, parallel imaging, and compressed sensing, by merging them in a unified framework and achieving real-time MRI imaging and analysis.

CT: Since harmful radiation is used in this imaging technique, the main challenge in CT is to control the radiation dose sustained by the patient. To attain high-quality images under the low-dose condition, AI image analyzer uses deep-learning algorithms that can effectively minimize noises and enhance the signal-to-noise ratio.

PET: The PET technique suffers from harmful radiation as well as long imaging time (typically 3 min), making trouble for some patients like children and bone cancer patients who may not be able to hold their positions during the imaging process. AI can solve both of these problems by effectively reducing noise and scanning time (to lower than 1 min).

PET-MRI: The PET-MRI technique involves creating the CT images from the MRI images to achieve attenuation correction (AC) for the PET images. Using the unsupervised deep-learning algorithms, more precise PET-MRI images and more accurate AC operations can be achieved.

• AI-assisted surgical robots: Our AI system can be coupled with robotic arms to allow surgeons perform precision surgery. This approach enhances their visual ability and minimizes invasive incisions and stitch wounds, which is impossible with a completely manual approach. Although critical decisions are still made by surgeons, some common surgical procedures can be completed using AI-powered surgical robots including prostate surgery, head and neck surgery as well as gynecologic surgery.

• Precision medicine: AIEX software can exclusively predict the most appropriate treatment procedures for individual patients according to their history and current treatment.
• Transcribing medical documents: Using natural language processing algorithms, machines can understand and classify clinical and research documents, generate reports (e.g. radiology or MRI examinations reports), and write prescriptions. Clinical protocol documents are usually over several thousand pages, making modifications a laborious and time-consuming task. Here’s where NLP comes to the rescue, it can handle massive data, find conflicts, and revise docum.

• Robotic process automation (RPA): Computer vision can easily scan most documents, and extract useful information automatically and quickly to be processed in transactional systems.