Learn how to evaluate AI systems in healthcare using specialized metrics and frameworks that address clinical validity, FDA regulatory requirements, bias detection, safety assessment, and practical implementation strategies. This comprehensive guide provides insights into designing robust evaluation pipelines for health AI applications.
Learn how to apply statistical methods for robust evaluation of models, including power analysis, mixed-effects models, bootstrap confidence intervals, multiple comparison corrections, and effect size calculations. This guide provides practical algorithms and Python code snippets to help researchers ensure their evaluations are statistically sound and meaningful.
Learn about various methods for evaluating large language models (LLMs), including automatic metrics like BLEU and ROUGE, the LLM-as-judge paradigm, human-in-the-loop strategies, and specialized approaches for health-related applications. This comprehensive guide also covers best practices for benchmark design, red teaming, and scaling evaluations.
This post provides a detailed overview of human evaluation and psychometrics in the context of AI systems, covering key concepts, reliability metrics, scale design, and practical implementation strategies. It includes algorithms and code snippets to help practitioners design robust evaluation frameworks.