AI and Health

This lecture explores the transformative role of Artificial Intelligence (AI) in healthcare, covering foundational AI methodologies, practical applications, technical challenges, and causal inference frameworks. The lecture begins by outlining AI’s core healthcare applications—including disease prediction/diagnosis (e.g., cancer, diabetes), personalized treatment, drug discovery, medical imaging, and remote monitoring—before diving into Machine Learning (ML) subsets: Supervised Learning (with labeled data for regression/classification), Unsupervised Learning (pattern detection in unlabeled data), and Reinforcement Learning (agent-based reward maximization). The lecture also distinguishes association vs. causation, highlighting Randomized Controlled Trials (RCTs) as the gold standard for causal inference and Mendelian Randomization (MR) as an alternative for unfeasible RCTs. Finally, it presents the Sequential Diagnosis Benchmark and MAI Diagnostic Orchestrator (MAIDrO), which, paired with OpenAI’s o3 model, achieves 80% diagnostic accuracy (four times higher than generalist physicians) and 20% lower costs. Overall, the lecture underscores AI’s potential to enhance healthcare efficiency and personalization while addressing critical challenges in interpretability, generalization, and causal validation. Gaetan Bakalli，法国里昂商学院定量经济学与金融系及医疗创新、技术与社会研究所助理教授，同时担任法国里昂高等师范学院治理、不平等与冲突经济研究中心研究员，在统计学、计量经济学与金融交叉领域深耕多年，教学与研究经验丰富。他专注于统计学习、金融市场预测、医疗数据分析及惯性传感器信号处理等方向，擅长将定量分析方法应用于金融市场、医疗健康等实际场景，既具备深厚的理论功底，又拥有金融行业实操经验。教学中，覆盖本科至研究生多个阶段，授课形式包含线上与线下，课程内容兼顾理论深度与实践应用，涵盖金融计量、数字金融工具、统计学等多个领域，且熟练运用 Python、R、SQL 等工具辅助教学，助力学生提升定量分析与数据应用能力。他的学术成果丰硕，多篇论文发表于国际顶级及高影响力期刊，同时积极参与学术服务，担任国际会议组织者与期刊评审，还指导多名硕士、博士研究生开展前沿课题研究。