Transform healthcare solutions through accurate and comprehensive medical data annotations, ensuring enhanced diagnostic capabilities and improved patient outcomes.
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At Dodeed AI, we specialize in high-quality data annotations tailored for medical and healthcare applications. Our expert annotations drive advancements in diagnostic accuracy, treatment planning, and patient care, ultimately contributing to a healthier future
We provide high-quality data annotations that enable AI systems to analyze medical images and detect abnormalities with unparalleled accuracy.
This allows healthcare professionals to make faster, more informed diagnostic decisions, leading to earlier treatments and improved patient outcomes.
Our annotations fuel predictive healthcare models by enabling AI to forecast patient health trends, identifying potential risks before they escalate.
With our data, healthcare providers can deliver proactive, personalized care that improves long-term patient wellness and reduces medical costs.
Through precise annotations, our data helps power AI-driven telemedicine platforms, ensuring accurate diagnoses and treatment recommendations during virtual consultations.
This allows healthcare providers to offer seamless, high-quality care remotely, improving access to health services for all.
Our expert annotations empower AI to analyze complex biological and chemical data, significantly speeding up the drug discovery process.
By improving the accuracy of AI models, we help pharmaceutical companies identify potential treatments faster, reducing time to market and cutting development costs.
Our data annotation services help streamline the organization of electronic health records (EHRs), enabling AI to efficiently sort and manage vast amounts of patient data.
This ensures healthcare providers can access critical information quickly, improving patient care and operational efficiency.
We adapt quickly to your evolving project needs. Whether you're launching a proof of concept or scaling to millions of annotations, our agile workflow ensures speed, flexibility, and consistent quality — without the bottlenecks
Your data is your asset — and we treat it that way. We follow strict data privacy protocols, secure infrastructure practices, and industry-grade compliance standards to ensure your information stays protected at every step.
From emerging startups to global enterprises, we offer scalable pricing models that align with your budget and goals. You get top-tier annotation quality — without the enterprise-only price tag.
Our data annotation solutions enhance healthcare AI systems with precise labeling of medical images and texts. This accuracy empowers healthcare professionals to make informed decisions, improving patient outcomes and streamlining care.
Orthopedic data annotation focuses on labeling medical imaging like X-rays, MRIs, and CT scans to train AI systems in identifying bone fractures, joint conditions, and musculoskeletal disorders.
These annotations help improve diagnostic accuracy and assist orthopedic professionals in clinical decision-making.
This process involves annotating dental X-rays, intraoral images, and 3D scans to highlight features like tooth structure, cavities, gum conditions, and alignment issues.
These labeled datasets help train AI models for accurate diagnostics, treatment planning, and the development of intelligent dental tools. It plays a vital role in modernizing dental care through precision-driven, AI-assisted solutions.
This annotation process labels radiology images to identify organs, tissues, and abnormalities. Semantic segmentation classifies each pixel by region, while instance segmentation distinguishes between individual structures like multiple tumors.
It helps AI models deliver precise diagnostics and supports radiologists in clinical decisions.
Precise labeling of cells and tissues in microscopic images helps AI detect diseases and support diagnosis.
These annotations enable accurate analysis for applications like cancer detection and pathology research, improving healthcare outcomes. Trusted experts ensure high-quality, reliable annotations tailored to complex medical imaging needs.
Medical text and report annotations involve tagging clinical notes, patient records, and diagnostic reports to extract key information.
This structured data helps AI systems understand medical language, enabling better decision support, automated documentation, and enhanced patient care.
Our efficient data annotation process guarantees quality at every stage. We prepare and clean datasets, apply precise labeling through skilled annotators, and conduct thorough quality checks. Finally, we deliver annotated datasets ready for AI model training and deployment.
Requirement Analysis is a crucial step where we collaborate closely with clients to fully understand their project goals and annotation needs. This phase allows us to define the types of annotations (e.g., bounding boxes, polygons, 3D cuboids) and quality benchmarks that align with their AI model objectives. We assess the data, develop detailed guidelines, and establish clear workflows to ensure that our annotations meet the highest standards.
By conducting thorough requirement analysis, we deliver tailored, accurate data labeling solutions that accelerate AI training and deployment.
In the Annotation Process, we leverage our expert annotators and advanced tools to label data with precision and accuracy. Depending on the project requirements, we apply various annotation techniques such as bounding boxes, semantic segmentation, or 3D cuboids. Our team follows the established guidelines to ensure consistency across all annotations, while adhering to quality standards.
Throughout the process, we maintain a seamless workflow, ensuring that the labeled data is ready for immediate use in AI model training and development.
In the Quality Assurance phase, we conduct thorough evaluations to guarantee the accuracy and uniformity of annotations. Our team uses both manual inspections and automated tools to catch any errors or inconsistencies. By implementing multi-layered reviews, we uphold stringent quality standards, ensuring all annotations align with project specifications.
This meticulous process is vital for delivering high-quality data that enhances the effectiveness and reliability of AI model training.
The Feedback Loop is an integral part of our process, where we continuously refine and improve annotations based on client feedback and quality assessments. By analyzing the results of quality checks and incorporating insights from clients, we adjust guidelines and workflows to enhance accuracy and efficiency.
This iterative approach allows us to respond quickly to evolving project needs, ensuring the final annotated data consistently meets the highest standards for AI model development.
In the Finalization and Delivery stage, we ensure all annotations are polished and meet the agreed-upon standards. After passing thorough quality checks and revisions, the annotated data is formatted and packaged according to client specifications. We prioritize timely delivery, providing the final dataset ready for immediate use in AI model training and deployment.
This phase ensures the project is completed efficiently, with accurate and high-quality data that drives optimal AI performance.
Our clients share how Intellisane AI’s precise and reliable annotation services boosted their AI projects, showcasing our commitment to quality and trust.
Intellisane AI played a key role in helping us reach 97% accuracy in automating foundation layout detection. Their deep understanding of spatial data and labeling precision brought measurable improvements to our AI pipeline. The team was communicative, detail-oriented, and delivered everything ahead of schedule.S. RagavanSr. ML Engineer
Our fashion AI model required pixel-level segmentation across 72 garment categories—and Intellisane AI handled it flawlessly. They quickly adapted to our complex annotation guidelines and delivered consistent, high-quality labels at scale. Their domain focus, speed, and attention to visual detail were exactly what we needed.Valerio ColamatteoSr. AI Scientist & Team Lead
For our ADAS project, Intellisane AI delivered precise vehicle annotations across diverse traffic scenes, including multiple object classes and occlusion scenarios. Their expertise in automotive data workflows and quality-first mindset helped us pass all validation checks, with timely delivery and professional communication throughout.Raphael LopezCo-Founder & CTO
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