Satvik is an incoming Medical Physics and Imaging Informatics PhD student at the University of Pennsylvania and works as an AI Scientist with RAD-AID International. He has been working around AI and radiology since 2019, including global health deployments and LLM benchmarking work.

The conversation focuses less on “AI is amazing” and more on where the evaluation of radiology AI still feels pretty shaky.

A few topics covered:

• Why high-accuracy numbers do not always translate into clinical usefulness  

• How data leakage can inflate model performance  

• Why multiple-choice benchmarks are a weak way to evaluate medical LLMs  

• What happens when 20+ models are tested against an internally annotated clinical dataset  

• Why fine-tuned models are not always the obvious winner  

• The difference between real agentic AI and vendor-flavoured workflow automation  

• Lessons from RAD-AID’s AI work in Botswana and India  

• Why smaller/local open-source models may make more sense in some clinical environments  

One of Satvik’s stronger points is that prompt engineering should be treated more like a scientific method than a shortcut. That feels like a more useful framing than a lot of what gets thrown around right now.

Episode link:  \[https://youtu.be/PEp6GElgPYQ\](https://youtu.be/PEp6GElgPYQ)

AI in Radiology: Benchmarking LLMs, Agentic Hype, and Imaging Informatics | Satvik Tripathi