Scientific Rigor Every PAMLEE™ HUB algorithm undergoes extensive validation using retrospective clinical datasets, prospective clinical trials, and real-world healthcare system deployments. We publish our findings in peer-reviewed medical journals and present at major oncology and radiology conferences.

Performance Metrics We evaluate our AI models using clinically relevant metrics including sensitivity, specificity, positive predictive value, negative predictive value, and area under the ROC curve (AUC). Our validation studies compare PAMLEE™ HUB performance against current standard-of-care diagnostic methods and expert human readers.

Continuous Improvement AI model development is an ongoing process. We continuously refine PAMLEE™ HUB algorithms based on new clinical data, emerging research, and feedback from healthcare professionals using the system in real-world settings. This commitment to continuous improvement ensures our technology remains at the cutting edge.