Conversational Health Assistants & AI Triage
Building AI that listens, understands, and guides patients safely
Industry Challenge
Conversational AI for healthcare — symptom checkers, triage chatbots, medication advisors, mental health companions — holds enormous promise for extending care access. But health dialogue AI carries unique risk: incorrect or unsafe responses can directly harm patients. These models need both high-quality training data (real clinical dialogue) and rigorous expert evaluation of their outputs before deployment.
How SCILabel Serves This Industry
Data Collection
We source consent-collected health dialogue data: symptom triage conversations, patient intake records, telehealth consultation transcripts, and structured health questionnaire response datasets from clinical and community health partners. Multilingual coverage across English, Swahili, French, and other African and global languages.
Data Annotation & Labeling
Our clinical workforce annotates health dialogue with intent labels (symptom reporting, medication query, appointment request, emergency signal), entity tags (symptom, body part, severity, duration), urgency classification (non-urgent / semi-urgent / urgent / emergency), and appropriate-response labeling for RLHF preference collection.
Data & Model Evaluation
Clinical evaluators score chatbot responses across five criteria: Clinical Accuracy, Safety (would this response harm the patient?), Appropriateness, Completeness, and Empathy. Red-teamers probe for unsafe responses to high-risk queries (chest pain, suicidal ideation, paediatric emergencies). Every response flagged as clinically unsafe is escalated for immediate remediation.
Annotation Types & Formats
- Intent classification: symptom reporting, triage, medication query, escalation
- Entity annotation: symptoms, anatomy, severity, duration, onset
- Urgency labeling: non-urgent, semi-urgent, urgent, emergency
- Safe/unsafe response labeling for RLHF training
- Red-teaming: adversarial health queries and failure-mode documentation
Specialist Workforce Tracks
Track 1 (Medical NLP) and Track 3 (RLHF): Medical Doctors, Nurses, Clinical Officers, Pharmacists.