Medical AI

Medical annotation

Label your medical data with the expert medical teams of Humans in the Loop

We have a roster of more than 50 medical specialists, including radiologists, cardiologists, ophthalmologists, dentists, etc, who have been displaced due to armed conflict. Using advanced 2D, 3D, and video annotation tools, they can deliver high-quality data for your medical AI project.


Precisely annotated data can be used to identify and highlight anomalies in images of eyes that a professional can then use to diagnose common diseases, such as diabetic retinopathy, age-related macular degeneration, cataract, retinal vein occlusion, and glaucoma.

Retinopathy segmentation pre-labelingRetinopathy segmentation post-labeling example
Radiology Brain Segmentation pre-labeling exampleRadiology Brain Segmentation post-labeling example


Computer vision can save radiologists’ time and make the entire process of diagnosing conditions more efficient. Through precise 2D annotation of DICOM imagery or 3D annotation of CT and MRI scans using advanced tools, our teams can help to segment and detect any organ or anomaly.


Ultrasound video can be annotated with bounding boxes, polygons, or full segmentation, in order to train models for locating the correct plane, identifying organs, calculating many of the standard measurements, and detecting tumors or lesions.

Ultrasound pre-labeling exampleUltrasound post-labeling example
Surgery pre-labeling exampleSurgery post-labeling example

Robotic surgery

In order to make robotic surgery safer and more efficient, AI models can be trained on images and videos with labels which identify the types of tools used, either with polygons or keypoints, as well as track their movement using interpolation across frames.


The analysis of bodily fluids like blood, or tissue samples from biopsies, can be automated at scale using labeled data. We can provide not only precise segmentation of different cells but also batch classification based on size, anomaly, structure, and other parameters.

pathology pre-labeling examplepathology post-labeling example
Teeth pre-labeling exampleTeeth post-labeling example


From teeth, cavity, and gum segmentation on Xrays and dental surgery videos, to full 3D maxillofacial segmentation of different bone structures, our expert annotators can prepare any type of data which is needed for the purposes of AI applied in dentistry.


Videos from colonoscopies and endoscopies can be analysed by AI models trained on labeled footage to classify ulcerations, polyps, and lesions. Different actions performed during the procedures, such as the removal of polyps, or the removal of tissue for biopsy, can also be detected.

Colonoscopy bounding box prelabeling exampleColonoscopy bounding box post labeling example
Cardiology pre segmentation exampleCardiology post segmentation example


Computer vision models in cardiology can be trained on segmented MRI, CT and ultrasounds of organs and tissues, allowing for the analysis of the cardiovascular structure and function. They often are used to analyse the ventricles and detect heart problems, irregularities, and anomalies.

How would you like your data annotated?

Types of annotation

Bounding Box

Interested in having a Human in the Loop label your medical dataset? 
Get in touch with our team at Humans in the Loop and a project manager will help you find the best solution to your computer vision needs!