AgentClinic is a multimodal agent benchmark demonstrating that LLM diagnostic accuracy on MedQA drops to below one-tenth in sequential clinical simulations, with Claude-3.5 leading and large tool-use differences across models.
Benchmarking retrieval-augmented generation for medicine.arXiv preprint arXiv:2402.13178
8 Pith papers cite this work, alongside 23 external citations. Polarity classification is still indexing.
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CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
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.
SHM-Agents is an LLM-plus-specialist-agent framework that claims to execute a wide range of SHM tasks end-to-end via natural language on data from a long-span cable-stayed bridge.
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.
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.
Deepchecks is a new multi-faceted evaluation framework for RAG that incorporates root cause analysis and production monitoring to assess reliability, relevance, and user satisfaction.
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.
citing papers explorer
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AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments
AgentClinic is a multimodal agent benchmark demonstrating that LLM diagnostic accuracy on MedQA drops to below one-tenth in sequential clinical simulations, with Claude-3.5 leading and large tool-use differences across models.
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CHI-Bench: Can AI Agents Automate End-to-End, Long-Horizon, Policy-Rich Healthcare Workflows?
CHI-Bench shows current AI agents achieve at most 28% success on long-horizon healthcare workflows that require dense policy adherence, multi-role handoffs, and multi-turn interactions.
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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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SHM-Agents: A Generalist-Specialist Integrated Agent System for Structural Health Monitoring
SHM-Agents is an LLM-plus-specialist-agent framework that claims to execute a wide range of SHM tasks end-to-end via natural language on data from a long-span cable-stayed bridge.
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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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MedRLM: Recursive Multimodal Health Intelligence for Long-Context Clinical Reasoning, Sensor-Guided Screening, Evidence-Grounded Decision Support, and Community-to-Tertiary Referral Optimization
Proposes MedRLM, a recursive agent-based multimodal framework for long-context clinical reasoning, sensor-guided screening, and referral optimization using a Clinical Evidence Graph Memory.
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Deepchecks: Evaluating Retrieval-Augmented Generation (RAG)
Deepchecks is a new multi-faceted evaluation framework for RAG that incorporates root cause analysis and production monitoring to assess reliability, relevance, and user satisfaction.
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A Survey on Retrieval-Augmented Text Generation for Large Language Models
A survey that categorizes RAG methods for LLMs into four retrieval-centric stages, reviews their evolution and evaluation, and outlines challenges and future directions.