Active Health AI Governance Toolkit
A toolkit for turning medical AI prototypes into reviewable, explainable and governance-ready project assets.
Translating multimodal health data, AI modeling and governance requirements into interpretable, auditable and decision-oriented medical AI tools.
Demos, modeling pipelines, evaluation reports and governance documentation.
A toolkit for turning medical AI prototypes into reviewable, explainable and governance-ready project assets.
A lightweight active-health demo that estimates fall risk and explains key contributing factors using structured health inputs.
A compact data workflow for cleaning, storing and visualizing multi-source health data with clear metric definitions.
A structured template for documenting model performance, explainability, subgroup behavior, limitations and deployment risk.
The work focuses on the middle layer between raw medical data and real-world application: modeling, explanation, validation and governance-ready translation.
多源健康数据清洗、特征工程、风险建模与状态评估。
面向研发、临床和用户的分层模型解释与误差分析。
数据授权、伦理审查、隐私保护、风险矩阵与责任边界说明。
将模型输出转化为可读报告、干预建议和平台展示材料。
The site will gradually collect reusable templates, demo interfaces, sample reports and evaluation materials for medical AI projects.
Bai MedAI focuses on medical AI evaluation, active health risk modeling, explainable decision support and governance-ready project translation.
Cross-disciplinary training in communication engineering, economic law, management science and medical AI research. The core strength is translating complex multi-source data into interpretable and decision-oriented outcomes.
This portfolio is currently under active development. The first stage focuses on selected project assets, demo prototypes and governance-ready documentation templates.