ArticleNPJ digital medicine2026
The effects of multitype prompt engineering for large language models in hypertension treatment decisions.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Application of AI in Hypertension Health Education: Scoping Review.Journal of medical Internet research · 2026Article
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Authors and funding
15 authors.
Funding
Abstract
The effects of various prompt engineering on Large Language Models (LLMs) performance in hypertension decision-making are not yet fully understood. We evaluate the impact of different prompt engineering on LLM performance in hypertension treatment decision-making. We conducted a two-stage validation study using 300 de-identified simulated hypertension cases based on real-world clinical scenarios. ChatGPT-4.1 with Guidance-Self-Consistency achieved optimal performance (91.3% accuracy), nearing expert-level competency, while zero-shot prompting yielded worst results (62.7% with DeepSeek-V3). Optimal LLM assistance consistently enhanced physicians' average accuracy across all levels (community hospital: 73.4% to 82.5%; county hospital: 84.0% to 87.9%; teaching hospital: 91.5% to 92.0%) and reduced inappropriate regimen rates. The worst LLM configurations decreased physician performance below baseline, increasing inappropriate regimen rates from 26.6% to 35.2% across all levels. Effectively designed prompt strategies enable LLMs to provide reliable hypertension treatment recommendations, thereby supporting physicians' clinical decisions. This study has been trial-registered (ChiCTR2500099307, March 21, 2025).
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