Open-source LLMs achieve moderate F1 on straightforward clinical trial criteria but underperform challenge-winning systems on criteria requiring fine-grained reasoning across three n2c2 datasets.
Prompt engineering paradigms for medical applications: scoping review and recommendations for better practices
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Prompt engineering is crucial for harnessing the potential of large language models (LLMs), especially in the medical domain where specialized terminology and phrasing is used. However, the efficacy of prompt engineering in the medical domain remains to be explored. In this work, 114 recent studies (2022-2024) applying prompt engineering in medicine, covering prompt learning (PL), prompt tuning (PT), and prompt design (PD) are reviewed. PD is the most prevalent (78 articles). In 12 papers, PD, PL, and PT terms were used interchangeably. ChatGPT is the most commonly used LLM, with seven papers using it for processing sensitive clinical data. Chain-of-Thought emerges as the most common prompt engineering technique. While PL and PT articles typically provide a baseline for evaluating prompt-based approaches, 64% of PD studies lack non-prompt-related baselines. We provide tables and figures summarizing existing work, and reporting recommendations to guide future research contributions.
fields
cs.CL 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Clinical trial cohort selection using Large Language Models on n2c2 Challenges
Open-source LLMs achieve moderate F1 on straightforward clinical trial criteria but underperform challenge-winning systems on criteria requiring fine-grained reasoning across three n2c2 datasets.