mlsanityData to 14 Oct 2026

Coarse-to-Fine Personalized LLM Impressions for Streamlined Radiology Reports

arXiv 2508.15845 ↗First posted 19 Aug 2025cs.CL cs.AIcited by 1

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Abstract (arXiv)

The manual creation of the "Impression" section in radiology reports is a primary driver of radiologist burnout. To address this challenge, we propose a coarse-to-fine framework that leverages open-source large language models (LLMs) to automatically generate and personalize impressions from clinical findings. The system first produces a draft impression and then refines it using machine learning and reinforcement learning from human feedback (RLHF) to align with individual radiologists' styles while ensuring factual accuracy. We fine-tune LLaMA and Mistral models on a large dataset of reports from the University of Chicago Medicine. Our approach is designed to significantly reduce administrative workload and improve reporting efficiency while maintaining high standards of clinical precision.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 67 of its 121 references that are in the map. First posted on arXiv 19 Aug 2025.

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67 of 121 references.

References · 67

  1. 2023GPT-4 Technical Reportcited by 18,396
  2. 2024The Llama 3 Herd of Modelscited by 14,715
  3. 2019BERTScore: Evaluating Text Generation with BERTcited by 5,512
  4. 2023Mistral 7Bcited by 3,164
  5. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,947
  6. 2019Publicly Available Clinical BERT Embeddingscited by 1,002
  7. 2024Large Language Model based Multi-Agents: A Survey of Progress and Challengescited by 769
  8. 2019Evaluating the Factual Consistency of Abstractive Text Summarizationcited by 584
  9. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  10. 2019Transfer Learning in Biomedical Natural Language Processing: An Evaluation of BERT and ELMo on Ten Benchmarking Datasetscited by 299
  11. 2020FEQA: A Question Answering Evaluation Framework for Faithfulness Assessment in Abstractive Summarizationcited by 293
  12. 2024Chain of Thought Empowers Transformers to Solve Inherently Serial Problemscited by 260
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  2. 2025FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Drivingcited by 166
  3. 2018Lessons from Natural Language Inference in the Clinical Domaincited by 151
  4. 2024ODTrack: Online Dense Temporal Token Learning for Visual Trackingcited by 129
  5. 2024Flora: Low-Rank Adapters Are Secretly Gradient Compressorscited by 108
  6. 2024Enhancing Code LLMs with Reinforcement Learning in Code Generation: A Surveycited by 83
  7. 2023Towards Unified Token Learning for Vision-Language Trackingcited by 62
  8. 2022Language Modelling with Pixelscited by 55
  9. 2025Frequency-Aligned Knowledge Distillation for Lightweight Spatiotemporal Forecastingcited by 55
  10. 2025Dobi-SVD: Differentiable SVD for LLM Compression and Some New Perspectivescited by 48
  11. 2025SCORE: Story Coherence and Retrieval Enhancement for AI Narrativescited by 42
  12. 2023FastPillars: A Deployment-friendly Pillar-based 3D Detectorcited by 39
  13. 2020An Empirical Study of Multi-Task Learning on BERT for Biomedical Text Miningcited by 35
  14. 2022BALF: Simple and Efficient Blur Aware Local Feature Detectorcited by 34
  15. 2025Towards Universal Modal Tracking with Online Dense Temporal Token Learningcited by 30
  16. 2024LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detectioncited by 29
  17. 2024Human Motion Instruction Tuningcited by 29
  18. 2021Understanding the Dynamics of DNNs Using Graph Modularitycited by 26
  19. 2024CMAT: A Multi-Agent Collaboration Tuning Framework for Enhancing Small Language Modelscited by 26
  20. 2024RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructionscited by 25
  21. 2024Reassessing Layer Pruning in LLMs: New Insights and Methodscited by 24
  22. 2025Similarity-Guided Layer-Adaptive Vision Transformer for UAV Trackingcited by 23
  23. 2025Exploring Decoupled Spatio-Temporal Consistency Learning and Self-Prompting Evolution for Self-Supervised Trackingcited by 23
  24. 2025GSQ-Tuning: Group-Shared Exponents Integer in Fully Quantized Training for LLMs On-Device Fine-tuningcited by 21
  25. 2024A Generic Layer Pruning Method for Signal Modulation Recognition Deep Learning Modelscited by 18
  26. 2025ViTCoT: Video-Text Interleaved Chain-of-Thought for Boosting Video Understanding in Large Language Modelscited by 18
  27. 2025Selective KV-Cache Sharing to Mitigate Timing Side-Channels in LLM Inferencecited by 18
  28. 2024Efficient Fine-Tuning of Large Language Models for Automated Medical Documentationcited by 16
  29. 2025Convex Relaxation for Robust Vanishing Point Estimation in Manhattan Worldcited by 15
  30. 2025QFFT, Question-Free Fine-Tuning for Adaptive Reasoningcited by 15
  31. 2023Deep Learning based Multi-Label Image Classification of Protest Activitiescited by 14
  32. 2024Information Entropy Guided Height-aware Histogram for Quantization-friendly Pillar Feature Encodercited by 14
  33. 2024CoreInfer: Accelerating Large Language Model Inference with Semantics-Inspired Adaptive Sparse Activationcited by 14
  34. 2025Intrinsic Entropy of Context Length Scaling in LLMscited by 14
  35. 2025ChatMotion: A Multimodal Multi-Agent for Human Motion Analysiscited by 14
  36. 2025Angles Don't Lie: Unlocking Training-Efficient RL Through the Model's Own Signalscited by 13
  37. 2023Can pre-trained models assist in dataset distillation?cited by 12
  38. 2024GlobalPointer: Large-Scale Plane Adjustment with Bi-Convex Relaxationcited by 11

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Citing papers per year: 2025: 1.

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