newsGoogle News — LLMTrust 62 · AggregatorPublished 1mo agoLive · 1mo ago
Clinical drug report generation using multi-phase prompt large language models - Nature
Clinical drug report generation using multi-phase prompt large language models Nature
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- PossiblePossibly related (embedding) · 56%tyang816/Awesome-TCM-LLM →
- PossiblePossibly related (embedding) · 60%Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC) →
- PossiblePossibly related (embedding) · 50%KennispuntTwente/tidyprompt →
- PossiblePossibly related (embedding) · 45%Towards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment Guidance →
- PossiblePossibly related (embedding) · 49%A Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation Study →
- PossiblePossibly related (embedding) · 50%Prompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models →
Covers (incoming)
repotyang816/Awesome-TCM-LLMpaperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)repoKennispuntTwente/tidypromptpaperTowards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment GuidancepaperA Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation StudypaperPrompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language Models
Related across the graph
repotyang816/Awesome-TCM-LLMpaperTowards Precision Therapy in Hepatocellular Carcinoma: A Clinical-Reasoning LLM for Risk Stratification and Treatment GuidancepaperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)paperPrompt Design at Scale: How Format, Instruction Count, and Context Length Shape Instruction Adherence and Hallucination in Large Language ModelspaperA Multi-Agent System for Autonomous, Fine-Tuning-Free Clinical Symptom Detection: Development and Validation StudyrepoKennispuntTwente/tidyprompt