Collect. Annotate. Validate. Build Better Healthcare AI
Diagnostic models struggle when labels miss subtle findings or differ between reviewers. We collect, annotate, and validate medical images, keypoints, and clinical documents through a multi-stage QA process, so your model trains on data that is consistent, reliable, and ready for clinical review.
Label quality sets the ceiling for model performance
In healthcare, trust in an AI model begins with trust in its data. A lesion boundary, a tooth margin, or a joint keypoint can shift by a few pixels and change what a model learns. X-ray, MRI, and CT images differ in contrast, resolution, and artefacts, and clinical text carries terminology that general annotators often misread.
Inconsistent labels by inexperienced annotators lead to false negatives, unstable segmentation, and extra validation cycles before your model reaches review. Data that does not reflect the cases your model will meet in practice has a similar effect.
At Qualitas Global, we support the full data pipeline: collecting the data your model needs, annotating it against your guidelines and class definitions, and validating every batch before delivery. Domain-specific people are trained to annotate on your guidelines before production begins, so labels stay consistent across reviewers, batches, and project phases. Clinically trained reviewers support projects where terminology and imaging knowledge matter, and every batch passes multi-stage quality checks before it reaches you.
Dental AI depends on accurate labels for intraoral periapical (IOPAR) and bitewing radiographs. IOPAR images show a tooth from crown to root along with the surrounding bone, while bitewing images show the crowns of the upper and lower teeth together. Our medical image annotation services use bounding boxes, polygons, and segmentation masks to mark teeth, restorations, cavities, bone levels, and other findings your model needs to detect.
Medical data transcription converts clinical speech and recorded content into accurate, structured text for training healthcare language and speech models. Transcribers follow your style guide for medical terminology, abbreviations, drug names, and speaker labels. The result is human-annotated data that supports speech recognition, clinical documentation tools, and medical NLP services.
Medical generative AI needs expert human input to stay accurate and safe. We support the data behind these models with prompt and response creation, output review, ranking, and quality evaluation against your clinical guidelines. Reviewers flag factual errors, unsafe statements, and unsupported claims so your team can improve model reliability before deployment.
AI liver diagnosis models learn from carefully labeled liver scans. Our medical data annotation team outlines the liver, marks lesions and abnormal regions, and classifies findings according to your labeling protocol. Segmentation and detection labels give your model consistent examples for training and validation.
Medical imaging analysis starts with precise labels across X-ray, MRI, and CT scan studies. We deliver medical image labeling for classification, segmentation, detection, and localization, including anatomical structures, lesions, and abnormalities. Annotation guidelines are agreed with you in advance and applied consistently across every study in the dataset.
Medical document annotation structures the records that clinical AI depends on, including reports, forms, prescriptions, and lab documents. Annotators label fields, tables, entities, and document sections, and apply OCR and medical text extraction where documents are scanned or handwritten. The output feeds information extraction, coding support, and medical NLP models.
Dermatology AI needs labeled skin images that capture variation in lesion type, size, color, and skin tone. We annotate lesions and affected regions with polygons and segmentation masks, and classify images according to your taxonomy. Consistent labeling across lighting and capture conditions helps models generalize beyond controlled datasets.
Pathology annotation turns microscopic tissue images into training data for diagnostic AI. Annotators outline regions of interest, mark cells and structures, and apply the classes your protocol defines. Pixel-level segmentation and detailed region labels support models that analyze tissue at high resolution.
Full body keypoint annotation marks joints and body landmarks across the whole figure. These labels train pose estimation models used in movement analysis, posture assessment, and health monitoring applications. We annotate images and videos with a consistent skeleton definition, handling occlusion and unusual poses according to your guidelines.
Face landmark annotation places precise points on features such as the eyes, eyebrows, nose, mouth, and jawline. These labels support facial analysis, expression recognition, and medical applications that depend on facial structure. Our team follows a fixed landmark schema so every image is labeled to the same standard.
Feet keypoint annotation marks the ankle, heel, arch, toes and other landmarks that matter for gait and posture analysis. It supports models that assess foot structure and movement from images and video. Annotators work to a defined landmark set and review ambiguous frames to maintain consistency.
Eye keypoint annotation places landmarks on the eyelids, corners, iris and pupil. These labels train models for gaze estimation, eye tracking and ophthalmic applications. Precise placement and a fixed landmark schema keep results reliable across varied lighting and image quality.
Hand keypoint annotation marks each joint and fingertip so models can understand hand shape, pose, and movement. It supports gesture recognition, rehabilitation tools, and clinical applications that track hand function. Annotators handle overlapping fingers and partial views using clear rules agreed with your team.
In-plane annotation labels structures and measurements within the plane of an image, helping models interpret orientation, position, and alignment. We apply your annotation rules to landmarks, outlines, and reference points so that labels remain consistent across the dataset. Quality checks confirm that each annotation follows the defined plane and view.
Eye annotation covers the labeling of eye images for detection, segmentation, and classification. Annotators outline structures and mark findings according to your protocol, supporting ophthalmic AI and vision research. Clear class definitions and review steps keep labels accurate across large image sets.
Shoulder annotation marks bones, joints, and landmarks in shoulder images to train models for musculoskeletal analysis. Labels may include segmentation of anatomical regions and key point placement for alignment and range-of-motion assessment. Annotators follow your protocol, so every study is labeled the same way.
Ear lobe annotation places precise landmarks and outlines on the ear lobe and surrounding ear structure. These labels support biometric, anatomical, and healthcare models that rely on ear shape and position. Each image is labeled to a defined schema and reviewed for placement accuracy.
Teeth and gum annotation supports dental AI with detailed labeling of individual teeth, gum lines, and surrounding tissue. We use polygon and segmentation annotation to separate teeth, restorations, and gum regions, and classify conditions according to your guidelines. Accurate dental labels help models detect problems earlier and measure changes more reliably.
Explore our data collection services.
Domain-trained expert annotators and daily throughput tracking against SLA targets.
A rigorous multi-level quality assurance process that ensures a 97%+ quality benchmark, validated on every project.
A risk-free 2-day pilot before full production, and team deployment within 24 business hours of sign-off. You see our work before you commit to volume.
Every engagement is tailored to your goals, with annotation guidelines, QA, and delivery cadence adapted to your project.
Pay-per-verified-productive-hour billing. You are not paying for our learning curve or mistakes, our internal rework, or idle bench time.
ISO/IEC 27001:2022, ISO 9001:2015, TISAX Level 3, HIPAA, and GDPR compliance. Biometric access, 24*7 CCTV monitoring, and policies and procedures that prohibit cameras and unauthorised storage devices on the floor.
100% in-house delivery. No outsourcing, no work-from-home access to client data, no exceptions.
Medical data annotation is the structured labeling of data so a machine-learning or analytics system can learn from examples or be evaluated against defined criteria. For healthcare, healthtech & biological, the work begins with a written label taxonomy, clear inclusion and exclusion rules, and representative sample data. The exact scope depends on the model objective and the client's acceptance criteria.
Depending on the approved scope, healthcare, healthtech & biological projects may involve medical images, clinical documents, healthcare text, biological imagery, or other health-related datasets only when explicitly approved and within qualified scope. The team should define each label, the unit being annotated, how to treat uncertain cases, and whether the task is frame-level, object-level, document-level, or text-level. These examples describe possible project requirements, not a guarantee that every modality or use case is currently offered.
Output formats and tooling should be confirmed during discovery rather than assumed from a general industry page. The project brief should specify the required schema, field names, class IDs, metadata, export structure, and any client platform access. Qualitas Global should confirm compatibility with the requested tools before the pilot begins; no specific format or third-party platform is promised here.
A quality plan should define the annotation guideline, reviewer responsibilities, sampling or review coverage, error categories, escalation rules, and acceptance criteria before production. Multi-level quality checks are a stated part of the intended service approach, but the exact workflow and quality SLA need internal confirmation. Disagreements should be documented, corrected, and reflected in updated guidance where the same issue could recur.
Data access, storage, transfer, retention, deletion, and physical work arrangements should be agreed before any sensitive files are shared. The project owner should confirm the applicable security controls and whether any certification or regulatory commitment applies to the specific engagement. Do not assume a particular ISO standard, HIPAA status, GDPR status, or other certification without current internal evidence.
Pricing should be scoped around the task definition, data complexity, annotation guideline, expected review effort, language or domain requirements, and the delivery arrangement. The stated commercial model is based on productive hours, with scope and acceptance criteria agreed in advance. A quote should clarify what counts as productive work, how rework is treated, and which activities are included, without relying on an unverified public price.
Yes, the recommended first step is to discuss a representative sample and agree on a pilot scope before committing a larger dataset. The pilot can test the label definitions, edge-case handling, reviewer workflow, output structure, and acceptance criteria. Start dates, sample size, turnaround, staffing, and scale-up capacity should be confirmed by the project team after reviewing the data and requirements.
Common challenges include Specialist interpretation, subtle visual differences, de-identification needs, label ambiguity, and the need to distinguish annotation from clinical diagnosis. A clear guideline should show positive and negative examples, explain ambiguous cases, and define when annotators should escalate rather than guess. To get started, share the intended model or business use, data types, approximate volume, required outputs, security constraints, and a sample if permitted. Qualitas Global can then confirm fit and propose a pilot.
We audit, correct and validate auto-annotated aerial data against your guidelines before it reaches your training pipeline.
Whether you need precisely annotated training data, real-world data collection, a dedicated in-house team, or other staffing solutions, Qualitas Global delivers all three under one roof. Backed by over a decade of experience, we back every engagement with a risk-free 2-day pilot before you commit.
