All learners complete this shared foundation before entering the specialist tracks. This phase builds a common language of AI principles, annotation fundamentals, the SCILabel platform, and the five-track programme structure — regardless of professional background.

Track 1 is compulsory for all learners and trains participants to annotate clinical text with the precision and clinical understanding that only a healthcare professional can provide. Learners annotate electronic health records, clinical notes, discharge summaries, adverse event records, and medical literature across 25 lessons, building the deepest possible NLP annotation skill set for global health-tech and pharma clients.

Track 2 is the clinical imaging core of the program and is available as an elective for learners with imaging, pathology, ophthalmology, or surgical backgrounds. Across 25 lessons, radiographers and imaging technologists lead annotation of radiology images, pathology slides, and ophthalmology scans using segmentation, bounding boxes, and diagnostic label assignment.

Track 3 is the most commercially powerful elective track. Reinforcement Learning from Human Feedback (RLHF) is how the world’s largest AI companies continuously improve their large language models. Healthcare-domain RLHF evaluators are among the highest-value annotation professionals globally. Across 20 lessons, learners are trained to evaluate, rank, and provide corrective feedback on AI-generated clinical outputs.

Track 4 positions SCILabel at the frontier of precision medicine AI. Genomic and biomedical data annotation requires deep scientific knowledge that general-purpose annotators cannot provide. Across 20 lessons, this track is purpose-built for biotechnologists, biochemists, biomedical scientists, laboratory scientists, clinical researchers, and pharmacists.