newsGoogle News — LLMTrust 62 · AggregatorPublished yesterdayLive · 16h ago
Evidence, use cases, and implementation safeguards of large language models in primary care - Nature
Evidence, use cases, and implementation safeguards of large language models in primary care Nature
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- PossiblePossibly related (embedding) · 58%The strength of clinical evidence is recoverable from language model representations but not from their stated grades →
- PossiblePossibly related (embedding) · 56%Safety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language Models →
- PossiblePossibly related (embedding) · 50%Multi-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC) →
- PossiblePossibly related (embedding) · 50%Evaluating Large Language Models on Misconceptions in Multi-Turn Medical Conversations →
- PossiblePossibly related (embedding) · 47%HealMed: Multilingual Evaluation of Large Language Models in Medicine →
Covers
paperThe strength of clinical evidence is recoverable from language model representations but not from their stated gradespaperSafety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language ModelspaperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)paperEvaluating Large Language Models on Misconceptions in Multi-Turn Medical ConversationspaperHealMed: Multilingual Evaluation of Large Language Models in Medicine
Related across the graph
paperHealMed: Multilingual Evaluation of Large Language Models in MedicinepaperEvaluating Large Language Models on Misconceptions in Multi-Turn Medical ConversationspaperMulti-Large Language Model Orchestrated Severity Assessment of Clinical Records (MOSAIC)paperThe strength of clinical evidence is recoverable from language model representations but not from their stated gradespaperSafety That Does Not Transfer: Cross-Lingual Clinical Correctness Drift in Deployable Medical Language Models