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How does DeepSeek-R1 perform on USMLE?

Faray de Paiva, L.; Luijten, G.; Puladi, B.; Egger, J.

2025-02-10 health informatics
10.1101/2025.02.06.25321749 medRxiv
Show abstract

DeepSeek, a Chinese artificial intelligence company, released its first free chatbot app based on its DeepSeek-R1 model. DeepSeek provides its models, algorithms, and training details to ensure transparency and reproducibility. Their new model is trained with reinforcement learning, allowing it to learn through interactions and feedback rather than relying solely on supervised learning. Reports showcase that DeepSeeks model shows competitive performances against established large language models (LLMs) such as Anthropics Claude and OpenAIs GPT-4o on established benchmarks in language understanding, mathematics (AIME 2024) and programming (Codeforces) while trained at a fraction of the costs. Additionally, running inference shows significantly lower costs, leading to DeepSeek surpassing ChatGPT as the most downloaded free app on the American iOS App Store. This development contributed to a nearly 17% drop in Nvidias share price, resulting in the most significant one-day loss in U.S. history, amounting to nearly $600 billion. The open-source models also bring a significant shift in the healthcare system, allowing cost-efficient medical LLMs to be deployed within hospital networks. To understand its performance in the healthcare sector, we analyse the new DeepSeek-R1 model on the United States Medical Licensing Examination (USMLE) and compare it to ChatGPT.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.