newsGoogle News — LLMTrust 62 · AggregatorPublished 1mo agoLive · 1mo ago
Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming - Nature
Addressing benchmarking gaps in large language models for health and medicine with dynamic red-teaming 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) · 60%Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC) →
- PossiblePossibly related (embedding) · 56%MedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical Consultation →
- PossiblePossibly related (embedding) · 55%Clinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI Benchmarking →
- PossiblePossibly related (embedding) · 53%tyang816/Awesome-TCM-LLM →
- PossiblePossibly related (embedding) · 52%Evaluating and Understanding Model Editing for Medical Vision Language Models →
- PossiblePossibly related (embedding) · 58%Safety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language Models →
- PossiblePossibly related (embedding) · 52%An Early Warning of Emerging Biosecurity Risks in Frontier LLMs →
- PossiblePossibly related (embedding) · 51%Toward Better Assessment of LLMs' Performance in Clinical Error Detection →
Covers
paperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)paperMedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical ConsultationpaperClinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI Benchmarkingrepotyang816/Awesome-TCM-LLMpaperEvaluating and Understanding Model Editing for Medical Vision Language Models
Covers (incoming)
paperSafety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language ModelspaperAn Early Warning of Emerging Biosecurity Risks in Frontier LLMspaperToward Better Assessment of LLMs' Performance in Clinical Error DetectionpaperUnderstanding Multilingual Medical ASR Adaptation Through Layer-Wise AnalysispaperTest-Time Scaling in the Wild: Why Exploitation, Not Exploration, Is the BottleneckpaperRadMatch: Auditable Radiology Report Evaluation via Finding-Level MatchingpaperHealMed: Multilingual Evaluation of Large Language Models in MedicinepaperMaking Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust Prediction
Related across the graph
paperMedRealMM: A Real-World Multimodal Benchmark for Chinese Online Medical ConsultationpaperHealMed: Multilingual Evaluation of Large Language Models in MedicinepaperUnderstanding Multilingual Medical ASR Adaptation Through Layer-Wise AnalysispaperEvaluating and Understanding Model Editing for Medical Vision Language ModelspaperMaking Clinical Language Models Auditable: Concept-Guided Fine-Tuning for Robust PredictionpaperAn Early Warning of Emerging Biosecurity Risks in Frontier LLMsrepotyang816/Awesome-TCM-LLMpaperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)paperTest-Time Scaling in the Wild: Why Exploitation, Not Exploration, Is the BottleneckpaperClinician-Level Agreement Without Clinical Caution: LLM Evaluator Limits in Medical AI BenchmarkingpaperToward Better Assessment of LLMs' Performance in Clinical Error DetectionpaperRadMatch: Auditable Radiology Report Evaluation via Finding-Level MatchingpaperSafety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language Models