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.
This dashboard provides comprehensive analysis of dataset citations within the NEMAR ecosystem, revealing collaboration patterns, research trends, and the impact of open neuroscience data sharing on the research community.
Learn how to transform your Hugo static site into an interactive dashboard powerhouse using Chart.js, structured JSON data, and modern web development practices.
This is a data visualization dashboard for exploring the Healthy Brain Network EEG dataset. Features age and sex distributions, task availability metrics, mental health correlations, and per-release analysis across 11 dataset releases with over 3,600 participants.
Learn how to seamlessly integrate Mermaid diagram functionality into your Hugo website using partials, enabling beautiful flowcharts, sequences, and technical diagrams directly in markdown.
Using 1024-electrode ultrahigh-density electrocorticography (μECoG) data as ground truth, this study demonstrates that cortical activity from a small 3×3 cm patch projects broadly across the entire scalp surface, not just to nearby EEG electrodes. By applying ICA decomposition and forward-projecting through a high-definition head model, we show that scalp EEG channels reflect complex mixtures of distributed cortical sources rather than primarily local activity. These findings challenge conventional channel-level EEG interpretation approaches and underscore the critical importance of source-level analysis methods for accurate understanding of brain electrical activity.
This post explores Python’s built-in venv module, highlighting its advantages for research computing while comparing it with Conda to help researchers choose the right tool for their workflows.