MED-VRAG reaches 78.6% average accuracy on four medical QA benchmarks by iteratively retrieving PMC page images with ColQwen2.5 embeddings and a VLM that refines queries over up to three rounds.
citation dossier
Ca- pabilities of GPT-4 on medical challenge problems
16Pith papers citing it
16reference links
cs.CLtop field · 9 papers
UNVERDICTEDtop verdict bucket · 14 papers
why this work matters in Pith
Pith has found this work in 16 reviewed papers. Its strongest current cluster is cs.CL (9 papers). The largest review-status bucket among citing papers is UNVERDICTED (14 papers). For highly cited works, this page shows a dossier first and a bounded explorer second; it never tries to render every citing paper at once.
representative citing papers
Indistinguishability-based privacy is incomparable to extractability in LLMs, and a new (l, b)-inextractability definition with rank-based bounds provides a tighter measure of extraction risk than prior proxies.
CuraView detects sentence-level faithfulness hallucinations in medical discharge summaries via GraphRAG knowledge graphs and multi-agent evidence grading, achieving 0.831 F1 on critical contradictions with a fine-tuned Qwen3-14B model and 50% relative improvement over baselines.
MedSkillAudit is a new domain-specific audit framework for medical research agent skills that achieved moderate agreement with expert reviews (ICC 0.449), exceeding the human inter-rater baseline (ICC 0.300).
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
EvidenceNet releases disease-specific biomedical knowledge bases with 7,872 and 6,622 evidence records for HCC and CRC, plus graphs, extracted via LLM pipeline with reported high fidelity.
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
Domain fine-tuning of a 4B LLM yields a statistically significant 6.8 pp accuracy gain on MedQA-USMLE over a general baseline, while RAG over medical explanations produces no significant improvement.
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
A 14B model trained on synthetic data from Brazilian clinical guidelines outperforms larger LLMs on new benchmarks for Brazilian healthcare protocols.
The paper introduces a dual-layer AI identification framework that integrates cryptographic, blockchain, and zero-knowledge techniques with governance checkpoints to support lifecycle accountability in digital enterprises.
Dense retrieval plus query reformulation and reranking reaches 60.49% accuracy on MedQA USMLE, outperforming other setups while domain-specialized models make better use of the retrieved evidence.
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
citing papers explorer
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Iterative Multimodal Retrieval-Augmented Generation for Medical Question Answering
MED-VRAG reaches 78.6% average accuracy on four medical QA benchmarks by iteratively retrieving PMC page images with ColQwen2.5 embeddings and a VLM that refines queries over up to three rounds.
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Beyond Indistinguishability: Measuring Extraction Risk in LLM APIs
Indistinguishability-based privacy is incomparable to extractability in LLMs, and a new (l, b)-inextractability definition with rank-based bounds provides a tighter measure of extraction risk than prior proxies.
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CuraView: A Multi-Agent Framework for Medical Hallucination Detection with GraphRAG-Enhanced Knowledge Verification
CuraView detects sentence-level faithfulness hallucinations in medical discharge summaries via GraphRAG knowledge graphs and multi-agent evidence grading, achieving 0.831 F1 on critical contradictions with a fine-tuned Qwen3-14B model and 50% relative improvement over baselines.
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MedSkillAudit: A Domain-Specific Audit Framework for Medical Research Agent Skills
MedSkillAudit is a new domain-specific audit framework for medical research agent skills that achieved moderate agreement with expert reviews (ICC 0.449), exceeding the human inter-rater baseline (ICC 0.300).
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The Provenance Gap in Clinical AI: Evidence-Traceable Temporal Knowledge Graphs for Rare Disease Reasoning
HEG-TKG grounds LLM clinical reasoning in hierarchical evidence-based temporal knowledge graphs from 4,512 PubMed records, delivering 100% citation verifiability and error detectability where standard RAG and unprompted LLMs produce none.
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MedDialBench: Benchmarking LLM Diagnostic Robustness under Parametric Adversarial Patient Behaviors
MedDialBench shows LLMs suffer 1.7-3.4x larger diagnostic accuracy drops from patients fabricating symptoms than withholding them, with fabrication driving super-additive interaction effects across models.
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Building evidence-based knowledge bases from full-text literature for disease-specific biomedical reasoning
EvidenceNet releases disease-specific biomedical knowledge bases with 7,872 and 6,622 evidence records for HCC and CRC, plus graphs, extracted via LLM pipeline with reported high fidelity.
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Towards an AI co-scientist
A multi-agent AI system generates novel biomedical hypotheses that show promising experimental validation in drug repurposing for leukemia, new targets for liver fibrosis, and a bacterial gene transfer mechanism.
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Domain Fine-Tuning vs. Retrieval-Augmented Generation for Medical Multiple-Choice Question Answering: A Controlled Comparison at the 4B-Parameter Scale
Domain fine-tuning of a 4B LLM yields a statistically significant 6.8 pp accuracy gain on MedQA-USMLE over a general baseline, while RAG over medical explanations produces no significant improvement.
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VeriLLMed: Interactive Visual Debugging of Medical Large Language Models with Knowledge Graphs
VeriLLMed is an interactive visual debugging tool that maps LLM diagnostic reasoning to knowledge graphs to identify and categorize relation, branch, and missing errors.
-
EviCare: Enhancing Diagnosis Prediction with Deep Model-Guided Evidence for In-Context Reasoning
EviCare uses deep model-guided evidence to enhance LLM in-context reasoning for accurate diagnosis prediction from EHRs, outperforming baselines by 20.65% on average and 30.97% for novel diagnoses on MIMIC datasets.
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Teaching LLMs Brazilian Healthcare: Injecting Knowledge from Official Clinical Guidelines
A 14B model trained on synthetic data from Brazilian clinical guidelines outperforms larger LLMs on new benchmarks for Brazilian healthcare protocols.
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AI Identification: An Integrated Framework for Sustainable Governance in Digital Enterprises
The paper introduces a dual-layer AI identification framework that integrates cryptographic, blockchain, and zero-knowledge techniques with governance checkpoints to support lifecycle accountability in digital enterprises.
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A Systematic Study of Retrieval Pipeline Design for Retrieval-Augmented Medical Question Answering
Dense retrieval plus query reformulation and reranking reaches 60.49% accuracy on MedQA USMLE, outperforming other setups while domain-specialized models make better use of the retrieved evidence.
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Comparative Analysis of Large Language Models in Healthcare
Domain-specific models like ChatDoctor excel at medically accurate and contextually reliable text while general-purpose models like Grok and LLaMA perform better on structured medical question-answering tasks.
- EpiGraph: Building Generalists for Evidence-Intensive Epilepsy Reasoning in the Wild