Initial implementations have delivered 35% accuracy improvement and 10% reduction in product returns SAN FRANCISCO, CA / ...

Training an AI model involves data preparation, model selection, model training, validation, and testing to ensure precision and readiness for deployment. (Jump to Section) Common challenges ...

Depending on the stage of development of the AI model, the data used falls into one of three categories: training data, test data and validation data. From personal experience, I’d say that ...

If you’re deploying or integrating AI at scale, blind spots can quietly introduce bias, security vulnerabilities or ...

Artera, the developer of multimodal artificial intelligence (MMAI)-based prognostic and predictive cancer tests, today ...

Data analytics has evolved beyond traditional forecasting and budgeting. It has become an active decision validation engine.

AI’s growth is limited by poor-quality data, not model size. Human expertise in data curation, decentralized feedback and ...

Combined Transcriptome and Circulating Tumor DNA Longitudinal Biomarker Analysis Associates With Clinical Outcomes in Advanced Solid Tumors Treated With Pembrolizumab The prognostic model demonstrated ...

This opportunistic screening service presented a range of mammogram images for each woman. We applied the model to the external validation data to evaluate discrimination performance (AUC) and ...

Retrospective validation involves feeding the AI model image data from the past, such as patient chest X-rays prior to the COVID-19 pandemic. Prospective validation, however, typically produces ...