Case Studies
Real‑world success stories of healthcare AI projects powered by SCILabel
Case Study 1
Radiology AI Startup — Medical Image Annotation
Client Challenge
A healthtech startup building an AI-assisted chest X‑ray screening tool needed 12,000 annotated chest radiographs with bounding boxes around abnormalities (nodules, consolidation, effusion) — labelled by qualified imaging professionals, not general-purpose crowd workers. Their internal team of two radiologists could not scale to meet their model-training timeline.
SCILabel Solution
- Data Collection: SCILabel sourced 12,000 de-identified chest X‑rays from partner hospitals via revenue-sharing agreements.
- Annotation: A team of eight Track 2 (Medical Imaging) annotators — all qualified radiographers and imaging technologists — completed bounding-box and classification annotation across five abnormality categories.
- Quality Assurance: Multi-tier QA: peer review, QA review, and PM spot-check on a 10% random sample. Inter-annotator agreement scored at 0.89 (Cohen's Kappa).
Results
- 12,000 annotated radiographs delivered in 18 working days.
- QA pass rate: 96.4% first-submission approval.
- Client model accuracy improved from 78% to 91% after retraining on the SCILabel dataset.
Case Study 2
Pharmaceutical Company — Clinical NLP & Medical Coding
Client Challenge
A multinational pharmaceutical company needed 50,000 de-identified adverse-event reports annotated with ICD‑10 codes, drug-event relationships, and severity classifications to train its pharmacovigilance NLP pipeline. Regulatory compliance (HIPAA, GDPR) was non-negotiable.
SCILabel Solution
- Compliance First: All data handled under signed NDA and DPA, HIPAA-aligned workflows, and automatic PII stripping before annotation.
- Annotation: A 15-person Track 1 (Medical NLP) team — pharmacists, clinical researchers, and health informatics specialists — annotated reports with ICD‑10, relationship tags, and severity labels.
- Quality Assurance: Gold-standard benchmark tasks embedded in 5% of assignments for passive accuracy monitoring.
Results
- 50,000 reports annotated in 6 weeks with a 97.1% QA pass rate.
- Client reduced manual pharmacovigilance review time by 60% after deploying the trained model.
- Engagement extended to a monthly retainer contract for ongoing adverse-event data annotation.
Case Study 3
Government Health Agency — RLHF Evaluation for Clinical Chatbot
Client Challenge
A national health agency developing an AI-powered symptom-triage chatbot needed expert clinical evaluation of its model's responses across 8,000 patient-query scenarios — assessing accuracy, safety, and appropriateness of recommended next steps. General-purpose evaluators were unsuitable for safety-critical health advice.
SCILabel Solution
- Evaluation: Track 3 (RLHF) evaluators — medical doctors, nurses, and clinical researchers — scored 8,000 AI responses using SCILabel's side‑by‑side comparison interface across four criteria: Accuracy, Relevance, Safety, and Clarity.
- Safety Testing: Red-teaming: a separate team of Track 5 (AI Safety) specialists ran adversarial queries to identify failure modes.
- Output: Preference dataset exported in reward-model-ready format for RLHF fine-tuning.
Results
- 8,000 evaluations + 1,200 adversarial red-team scenarios completed in 4 weeks.
- 23 critical safety failures identified and remediated before public deployment.
- Client achieved regulatory sign-off for pilot deployment in three regions.