Dynamic prompting helps LLMs predict cancer outcomes from EHR notes
Leveraging dynamic prompting for outcome prediction of cancer patients using large language models and electronic health record notes
Dynamic prompting offers an alternative to fine-tuning for clinical prediction, letting teams adapt general-purpose LLMs to patient records with prompt engineering alone. Researchers and data scientists in healthcare can test the method on their own EHR text without training custom models. The study appears in npj Digital Medicine, a journal accessible to medical AI practitioners worldwide.
A study published in npj Digital Medicine on 22 September 2026 explores dynamic prompting as a technique for predicting outcomes of cancer patients.
The approach applies large language models to electronic health record notes.It was published online with doi:10.
1038/s41746-026-03280-3.
Leveraging dynamic prompting for outcome prediction of cancer patients using large language models and electronic health record notes
npj Digital Medicine, Published online: 22 September 2026; doi:10.1038/s41746-026-03280-3 Leveraging dynamic prompting for outcome prediction of cancer patients using large language models and electronic health record notes
This report is categorized under AITOP's "Papers" desk. Within our cross-regional analytical framework, it directly maps to AI developer toolchains, coding agents, and engineering workflows. Key entities and technical topics under continuous tracking include dynamic prompting, LLM, clinical prediction, prompt engineering.
Source attribution: curated from npj Digital Medicine (@Hui Lin).