Intelligent Image Diagnosis

Artificial intelligence can support radiologists and pathologists as they use medical imaging to diagnose a wide variety of conditions. AI may also be able to help train radiologists on both normal and abnormal presentations of various organs and body systems so as to more easily identify related disease states and conditions. Main AI methods include image segmentation, feature extraction, quantitative analysis, comparative analysis, etc. AI is increasingly helping to reveal hidden insights into clinical decision-making, connect patients with resources for self-management, and extract meaning from previously inaccessible, unstructured data assets.

Functional Scenarios

nomain-Intelligent-Image-pic1Identifying Abnormalities

Identify cardiovascular abnormalities by detecting left atrial enlargement, aortic valve analysis, carina angle measurement, pulmonary artery diameter measurement, and monitoring changes in blood flow. AI tools can be used to automate measurement tasks as mentioned above. Identify hard-to-see fractures, dislocations, or soft tissue injuries using artificial intelligence. Automating the detection of abnormalities in commonly-ordered imaging tests, as well as automatic target delineation and adaptive radiotherapy.

nomain-Intelligent-Image-pic2Diseases Diagnosis

Algorithms may be able to streamline this process in identifying degenerative neurological diseases. Automating this procedure with machine learning would facilitate research and assist in the development of a promising imaging biomarker. AI-powered medical imaging can be used in preventive screening for common cancers. For patients with established cancers, AI could support the detection of malignancies that have spread. A performant algorithm could potentially identify extranodal extension for diagnoses that do not usually proceed to surgery, potentially enabling better treatment stratification in this population. AI could be useful for head and neck cancers, prostate cancer, colorectal cancers, and cervical cancer.

Component Modules



Intelligent medical imaging equipment, including medical rapid imaging method, image quality enhancement and intelligent chemical workflow.



Intelligent medical image processing and analysis method, mainly introduces the most widely used imaging omics and deep learning algorithm at the current stage.



The combination of intelligent medical image and natural language text processing, mainly solves the problem of defective labeled database in the field of medical image.


AI has the potential to change medical practice in myriad ways. Medical imaging data are the richest and most complex sources of information about patients. The work of medical image analysis is tedious and repetitive. AI medical imaging has the ability to improve the efficiency of image analysis by quickly and accurately labeling specific abnormal structures, so as to free more time for radiologists. For patients, AI medical imaging will help them complete health examination more quickly, including X-ray, B-ultrasound, MRI, etc., and can obtain more reliable diagnosis results. Compared with the traditional mode, AI film reading can greatly improve the efficiency, reduce the omission of small lesions, and improve the accuracy rate. Moreover, through the preliminary AI screening and diagnosis not only can guarantee the higher diagnosis quality, but also bring a significant cost reduction.

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