Interview: James Zou — EMJ

Summary

James Zou | Associate Professor of Biomedical Data Science, Stanford University, California, USA Citation: EMJ. 2026;11[1]:28-31. https://doi.org/10.33590/emj/OR9BWSEN When did you first become interested in applying AI to clinical research? I’ve been working at the intersection of AI and biomedicine for about 15 years now, since my PhD. My initial work was more focused on using AI to enable biomedical discoveries. When I came to Stanford, California, USA, in 2016, that’s when I started doing more work on AI for clinical research. Your work spans clinical prediction, genomics, and large-scale AI models. When you look at the field today, what’s changed the most since you first started working in healthcare AI? I think the biggest change we’ve seen is the shift from viewing AI as a tool to viewing AI as more of an agent or co-scientist. In the past, when I first started in the field, we would begin with a specific problem in medicine or healthcare and then apply or develop an AI tool to tackle that problem. But in the last 2 years, we’ve seen the emergence of much more autonomous AI driven by language models and AI agents. Because these agents are more autonomous, they can start to come up with their own problems. They don’t have to wait for us to define the problem. They can generate hypotheses and even create their own tools. From a clinician’s perspective, for example, in dermatology, when diagnosing melanoma, if AI provides a conclusion, a clinician still has to review it. Do you think that will always be necessary, or could we step aside in the near future? Currently, it’s still necessary for human experts and clinicians to provide final judgment and oversight over these AI systems. Not necessarily for every step, but for the most important decisions. For example, the final diagnosis or treatment decision. I think that oversight is still necessary. Some studies suggest AI can make fewer mistakes than clinicians. So why do we still need to revise AI decisions? AI is still a relatively new and emerging technology. The algorithms might work very well on the specific data and hospitals they were trained on, and they could be very reliable there, but if they’re applied in the wild, in new clinics or with different patient populations, there’s still uncertainty about how the models will perform. They might work very well and be robust, but there’s also a chanc…