Case Study: A pioneering health analytics company achieves improved pneumonia detection and cancer staging accuracy with Shaip

Enhancing Healthcare Predictive Models with Generative AI

Leading Pioneering Company, a leader in health analytics, needed to refine disease-condition prediction from clinical and radiology reports using generative AI on the open-source MIMIC‑CXR dataset. They faced challenges with dataset credentialing and security, inconsistent LLM prediction accuracy, complex disease-state classification, and the need for high-quality medical annotations; they engaged Shaip and used Label Studio for manual validation and annotation.

Shaip streamlined MIMIC‑CXR credentialing, developed annotation guidelines, and provided expert manual validation and correction of LLM outputs within Label Studio, while calculating performance metrics (concordance, precision, recall, F1) to drive improvements. As a result, Shaip helped improve model accuracy for pneumonia detection, boosted precision and recall, and produced a high-quality ground truth dataset for cancer TNM staging—enabling more reliable predictions and ongoing model refinement.