Chinese labs propose generative AI ‘digital twin’ paradigm for clinical trials

This Lancet Digital Health viewpoint is a good marker of how quickly cutting‑edge AI techniques are being woven into the fabric of biomedical research. The Shanghai Jiao Tong and Tsinghua team propose using generative models to design and simulate target trial emulations on top of electronic health records, arguing that AI‑powered digital twins can help overcome the cost, ethics, and data scarcity limits of traditional randomized controlled trials.([news.sjtu.edu.cn](https://news.sjtu.edu.cn/jdzh/20260105/218741.html))

For the AGI race, the key point isn’t just that AI is entering medicine—it’s that medical researchers are starting to think in terms of “world models” of patients and interventions. That mindset is very close to how alignment and safety researchers think about AI systems modeling human preferences and environments. If generative models can reliably simulate complex counterfactuals in noisy clinical settings, that’s evidence these systems are becoming powerful reasoning engines over high‑stakes domains, not just pattern recognizers.

Strategically, it also highlights China’s ambitions to lead in applying foundation models to healthcare, supported by state funding and top universities. Success here would give Chinese labs privileged access to unique medical datasets and validation loops, which could in turn feed back into more capable, medically fluent base models. The flip side is that synthetic data and opaque models raise new risks around bias, privacy leakage, and reproducibility that regulators and ethicists are only beginning to grapple with.