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AI in Healthcare
//INDUSTRIES
AI IN HEALTHCARE
Annotation in healthcare involves labeling and categorizing medical data to extract valuable insights, improve diagnosis, and enhance patient care. Annotation plays a critical role in healthcare by providing labeled data that supports clinical decision-making, research, and patient care across various medical specialties and domains.
ANNOTATION FOR IMPLEMENTING AI IN HEALTHCARE INDUSTRY
Medical Imaging
Annotation involves labeling images from various medical imaging modalities such as X-rays, MRI scans, CT scans, and ultrasound images. Radiologists annotate images to identify and delineate anatomical structures, abnormalities, lesions, tumors, and other clinically relevant features
Pathology
Annotation is used to label histopathological slides and digital pathology images for diagnosis and research purposes. Pathologists annotate tissue samples to identify different cell types, structures, and pathological changes, aiding in the diagnosis of diseases such as cancer.
Electronic Health Records (EHRs)
Annotation involves labeling clinical notes, medical records, and patient data within electronic health records (EHRs). This includes annotating patient demographics, medical history, symptoms, diagnoses, treatments, and outcomes to facilitate data analysis, decision-making, and research.
Medical Annotations
Annotation includes labeling medical concepts, terms, and entities within text documents, such as medical literature, research articles, and clinical guidelines. This helps healthcare professionals extract and analyze information from medical texts, enhance information retrieval, and support evidence-based practice.
Medical Image Segmentation
Annotation involves segmenting medical images into regions of interest, such as organs, tumors, or lesions. This enables quantitative analysis, volumetric measurements, and computer-aided diagnosis in medical imaging applications.
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//Industries
INDUSTRIES WE SERVE
RETAIL
Assisting the retail and e-commerce sectors by providing training data to optimize their in-store operations through the implementation of artificial intelligence (AI).ROBOTICS
3D object detection finds extensive application in robotics, particularly to prevent collisions with dynamic entities such as humans, animals, and other objects.AGRICULTURE
Supporting agriculture through computer vision training data involves facilitating the identification of product defects, sorting produce, managing livestock, assessing soil quality, implementing fertilizer applications, and fine-tuning genetic conditions.INSURANCE
Preparing training data to integrate AI into insurance procedures for tasks such as risk assessment, fraud detection, underwriting and minimizing human error.HEALTHCARE
Incorporating annotations and accurate labeling within AI systems is crucial for uncovering connections within genetic codesand enhancing efficiency in healthcare processes.SECURITY & SURVEILLANCE
Facilitating the integration of AI into cameras and sensors enables the detection of potential risks at workplaces, airports, and industrial sites. This involves incorporating computer vision technology into security and surveillance systems.SELF-DRIVING
Bounding boxes serve to annotate the surroundings of a vehicle, aiding in the detection of various objects including pedestrians, vehicles, traffic signs, and barriers.LOGISTICS
Logistics represents one of the growing areas of artificial intelligence application. We specialize in annotating images of goods to generate high-quality training data utilized in logistics.AUTONOMOUS FLYING
Simplifying and broadening access to AI implementations for automated or assisted flight can be achieved by leveraging image annotation conducted at the backend using training data specifically tailored for autonomous flying.