2 citations · 2 across the 1 of their papers we have counts for
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Entry-level guide to the use of large language models for medical research
Qiao Jin, Nicholas Wan, Robert Leaman +20
Frontier large language models (LLMs), such as GPT-5, Claude 4.5, Gemini 3, Llama 4, and DeepSeek-R1, represent a transformative class of AI tools capable of revolutionizing variou…
Adaptation of Agentic AI: A Survey of Post-Training, Memory, and Skills
Pengcheng Jiang, Jiacheng Lin, Zhiyi Shi +31
Large language model (LLM) agents are moving beyond prompting alone. ChatGPT marked the rise of general-purpose LLM assistants, DeepSeek showed that on-policy reinforcement learnin…
Developing Large Language Models for Clinical Research Using One Million Clinical Trials
Zifeng Wang, Jiacheng Lin, Qiao Jin +5
Developing artificial intelligence (AI) for clinical research requires a comprehensive data foundation that supports model training and rigorous evaluation. Here, we introduce Tria…
s3: You Don't Need That Much Data to Train a Search Agent via RL
Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin +4
Retrieval-augmented generation (RAG) systems empower large language models (LLMs) to access external knowledge during inference. Recent advances have enabled LLMs to act as search…
BioDSA-1K: Benchmarking Data Science Agents for Biomedical Research
Zifeng Wang, Benjamin Danek, Jimeng Sun
Validating scientific hypotheses is a central challenge in biomedical research, and remains difficult for artificial intelligence (AI) agents due to the complexity of real-world da…
Can Large Language Models Replace Data Scientists in Biomedical Research?
Zifeng Wang, Benjamin Danek, Ziwei Yang +2
Data science plays a critical role in biomedical research, but it requires professionals with expertise in coding and medical data analysis. Large language models (LLMs) have shown…