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Paper

Adapted large language models can outperform medical experts in clinical text summarization

by Independent / Community 007c3d9b8dab341d2c77c4ee764fd921f7f14956

Free2AITools Nexus Index

72.6

S: Semantic 50

Query-time baseline · scored live at search

A: Authority 92

P: Popularity 71

R: Recency 100

Q: Quality 65

Tech Context

Vital Performance —

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progres...

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Semantic Scholar 744 Citations

Paper Information Summary
Entity Passport
Registry ID	007c3d9b8dab341d2c77c4ee764fd921f7f14956
License	ArXiv
Provider	semantic_scholar

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Cite this paper

Academic & Research Attribution

BibTeX

@misc{007c3d9b8dab341d2c77c4ee764fd921f7f14956,
  author = {Unknown},
  title = {Adapted large language models can outperform medical experts in clinical text summarization Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/007c3d9b8dab341d2c77c4ee764fd921f7f14956}},
  note = {Accessed via Free2AITools.}
}

APA Style

Unknown. (2026). Adapted large language models can outperform medical experts in clinical text summarization [Paper]. Free2AITools. https://api.semanticscholar.org/007c3d9b8dab341d2c77c4ee764fd921f7f14956

🔬Technical Deep Dive

Full Specifications [+]

⚖️ Free2AITools Nexus Index V2.0

Methodology How FNI works

Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 92

Popularity (P) 71

Recency (R) 100

Quality (Q) 65

💬 Index Insight

FNI V2.0 for Adapted large language models can outperform medical experts in clinical text summarization: Authority (A:92), Popularity (P:71), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

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HuggingFace API GitHub Metadata Arxiv Citation DB Methodology

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📝 Executive Summary

"Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progres..."

❝ Cite Node

@article{Unknown2026Adapted,
  title={Adapted large language models can outperform medical experts in clinical text summarization},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

🔗 Full Paper

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📊 Research Signals

📈744CitationsSemantic Scholar

🏛️92AuthorityFNI pillar

⏱️100RecencyFNI pillar

✅65QualityFNI pillar

🗂️text generationField

📦Data Source: semantic_scholar

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Source summary: Based on semantic_scholar metadata. Not a recommendation.

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🆔 Identity & Source

source: semantic_scholar
author: Unknown
license: ArXiv
tags: paper, research, academic

⚙️ Technical Specs

architecture: null
params billions: null
context length: null
pipeline tag

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downloads: 0
stars: null
forks: null
citations: 744

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