Introduction

Healthcare AI is no longer only about prediction. A new generation of large language models and multimodal clinical assistants now generate text, summarise records, answer patient questions and support clinical reasoning. For these systems, the most valuable human contribution is no longer drawing boxes on images — it is judging the quality, safety and honesty of what the model produces, and using that judgement to shape the model’s behaviour. The Clinical AI Evaluation & RLHF Specialist Program trains learners to do exactly this.

Reinforcement Learning from Human Feedback (RLHF) and related techniques sit at the heart of modern AI alignment. Behind every well-behaved clinical assistant is a team of skilled human evaluators writing rubrics, grading responses, ranking outputs, building preference data and red-teaming the model for unsafe behaviour. This program demystifies that pipeline and turns learners into rigorous, employable evaluation specialists who understand both the craft and the clinical stakes.

Delivered by Shevs Connect Institute (SCI), the program assumes completion of the HEALTHCARE AI TRAINER & DATA ANNOTATION PROGRAM and builds on that foundation with a deep focus on generative AI, evaluation methodology, preference data and AI safety in healthcare contexts. Graduates are prepared to contribute to RLHF and model-evaluation projects on SCILabel and with global AI data partners.

This specialist program is designed to be taken after the foundation program above. The foundation course establishes the core concepts and working practices that this program builds upon.

Learning Outcomes

On successful completion of this program, graduates will be able to:

• Explain how modern clinical AI systems and large language models work at a level sufficient to evaluate them.
• Describe the full RLHF pipeline (supervised fine-tuning, reward modelling, policy optimisation) and the human’s role at each stage.
• Design clear, operational evaluation rubrics for helpfulness, harmlessness and honesty in clinical settings.
• Grade individual model responses consistently against a guideline.
• Produce high-quality pairwise and multi-response preference data, including rationale capture.
• Recognise and mitigate annotation artifacts such as length bias, sycophancy and reward hacking.
• Measure inter-rater reliability for subjective evaluation tasks and improve calibration.
• Conduct structured red-teaming of clinical AI and score harms by severity.
• Test models for bias, fairness and equity across patient populations.
• Plan and run an end-to-end evaluation campaign and report results to inform model decisions.

Course Features

• Six in-depth modules totalling 60 structured lessons plus 5 major hands-on assignments.
• Practical exercises grading and ranking real model outputs against rubrics.
• Hands-on red-teaming labs with a severity-scoring framework for clinical harms.
• Calibration sessions and inter-rater reliability analysis that mirror professional RLHF teams.
• Coverage of current methods including RLHF, RLAIF, constitutional approaches and DPO.
• A portfolio-ready capstone: a complete evaluation campaign with a written report.
• Direct pathway into evaluation and RLHF projects on SCILabel and with partners.
• A Certificate of Completion in Clinical AI Evaluation & RLHF from Shevs Connect Institute.