Bài viết & Nghiên cứu

一张血涂片中包含数千个细胞。有经验的检验师需要数十分钟才能完成形态学分析，而深度学习模型可以在秒内完成，精度相当甚至更优。这不是取代——而是赋能。

当城市的每个角落都安装了传感器，当AI系统能实时分析数百万人的行为模式——我们正在建设更聪明的城市，还是更精密的监控系统？这是智慧城市研究者必须正视的核心问题。

Predicting how an individual patient will respond to immunotherapy. Identifying autoimmune disease subtypes from genomic data. Designing vaccines that work across viral mutations. These are not hypothetical futures — they are active research areas where AI is already demonstrating measurable impact.

一个能识别上千种中草药的计算机视觉模型，一个能从症状描述中自动辨别证候的NLP系统——这不是科幻，而是正在发生的事。作为一名同时研究AI与中医的学者，我想分享这一交叉领域的最新进展与深层挑战。

What if multiple hospitals could collaboratively train a cancer detection model without ever sharing a single patient record? Federated Learning makes this possible — and its implications for healthcare AI are profound. But the real-world challenges are equally significant.

Most career advice tells you to pick a lane. I picked seven. This is what happened — and what I would tell my younger self about navigating a genuinely interdisciplinary academic career in a developing research ecosystem.