πŸ“„
K-QA: A Real-World Medical Q&A Benchmark paper by Independent / Community
⭐ 68
πŸ“„
Paper

K-QA: A Real-World Medical Q&A Benchmark

by Independent / Community 00d4e5bccaba9248538295f4e117c9620f676ec1
Free2AITools Nexus Index
68.0
S: Semantic 50

Query-time baseline · scored live at search

A: Authority 82
P: Popularity 58
R: Recency 100
Q: Quality 65
Tech Context
Vital Performance —

Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient health. To address this challenge, we construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on a popular clinical online platform. We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. Additionall...

Source β†’
Semantic Scholar 33 Citations
Paper Information Summary
Entity Passport
Registry ID 00d4e5bccaba9248538295f4e117c9620f676ec1
License ArXiv
Provider semantic_scholar
πŸ“œ

Cite this paper

Academic & Research Attribution

BibTeX 
@misc{00d4e5bccaba9248538295f4e117c9620f676ec1,
  author = {Unknown},
  title = {K-QA: A Real-World Medical Q&A Benchmark Paper},
  year = {2026},
  howpublished = {\url{https://api.semanticscholar.org/00d4e5bccaba9248538295f4e117c9620f676ec1}},
  note = {Accessed via Free2AITools.}
}
APA Style
Unknown. (2026). K-QA: A Real-World Medical Q&A Benchmark [Paper]. Free2AITools. https://api.semanticscholar.org/00d4e5bccaba9248538295f4e117c9620f676ec1

πŸ”¬Technical Deep Dive

Full Specifications [+]

βš–οΈ Free2AITools Nexus Index V2.0

Methodology How FNI works
Semantic (S) 50

Query-time baseline · scored live at search

Authority (A) 82
Popularity (P) 58
Recency (R) 100
Quality (Q) 65

πŸ’¬ Index Insight

FNI V2.0 for K-QA: A Real-World Medical Q&A Benchmark: Authority (A:82), Popularity (P:58), Recency (R:100), Quality (Q:65). Semantic (S) is a query-time baseline scored live at search.

Free2AITools Nexus Index

Data Sources / Provenance

HuggingFace API GitHub Metadata Arxiv Citation DB Methodology
Open data Updated: Live data

πŸ“ Executive Summary

"Ensuring the accuracy of responses provided by large language models (LLMs) is crucial, particularly in clinical settings where incorrect information may directly impact patient health. To address this challenge, we construct K-QA, a dataset containing 1,212 patient questions originating from real-world conversations held on a popular clinical online platform. We employ a panel of in-house physicians to answer and manually decompose a subset of K-QA into self-contained statements. Additionall..."

❝ Cite Node 

@article{Unknown2026K-QA:,
  title={K-QA: A Real-World Medical Q&A Benchmark},
  author={},
  note={Indexed by Free2AITools},
  year={2026}
}

πŸ”— Full Paper

Free2AITools indexes the abstract and factual metadata for this paper. Read the complete, authoritative paper on the official source.

Read the full paper on arXiv

πŸ“Š Research Signals

πŸ“ˆ33CitationsSemantic Scholar
πŸ›οΈ82AuthorityFNI pillar
⏱️100RecencyFNI pillar
βœ…65QualityFNI pillar
πŸ—‚οΈautomation workflowField
πŸ“¦Data Source: semantic_scholar
πŸ”„ Updated daily

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

πŸ“Š FNI Methodology πŸ“š Knowledge Baseℹ️ Verify with original source

πŸ›‘οΈ Paper Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

πŸ†” Identity & Source

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

βš™οΈ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag

πŸ“Š Engagement & Metrics

downloads
0
stars
null
forks
null
citations
33

Data indexed from public sources. Updated daily.