Using Large Language Models To Translate Machine Results To Human Results

• URL: http://arxiv.org/abs/2512.24518v1
• Date: Tue, 30 Dec 2025 23:32:04 GMT
• Title: Using Large Language Models To Translate Machine Results To Human Results
• Authors: Trishna Niraula, Jonathan Stubblefield,
• Abstract summary: This study introduces a pipeline that integrates YOLOv5 and YOLOv8 for anomaly detection in chest X-ray images with a large language model (LLM) to generate natural-language radiology reports.<n>Results show strong semantic similarity between AI and human reports, while human evaluation reveals GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5)
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Artificial intelligence (AI) has transformed medical imaging, with computer vision (CV) systems achieving state-of-the-art performance in classification and detection tasks. However, these systems typically output structured predictions, leaving radiologists responsible for translating results into full narrative reports. Recent advances in large language models (LLMs), such as GPT-4, offer new opportunities to bridge this gap by generating diagnostic narratives from structured findings. This study introduces a pipeline that integrates YOLOv5 and YOLOv8 for anomaly detection in chest X-ray images with a large language model (LLM) to generate natural-language radiology reports. The YOLO models produce bounding-box predictions and class labels, which are then passed to the LLM to generate descriptive findings and clinical summaries. YOLOv5 and YOLOv8 are compared in terms of detection accuracy, inference latency, and the quality of generated text, as measured by cosine similarity to ground-truth reports. Results show strong semantic similarity between AI and human reports, while human evaluation reveals GPT-4 excels in clarity (4.88/5) but exhibits lower scores for natural writing flow (2.81/5), indicating that current systems achieve clinical accuracy but remain stylistically distinguishable from radiologist-authored text.

Related papers

• Bridging Vision and Language: Optimal Transport-Driven Radiology Report Generation via LLMs [4.273291010923853]
  Large language models (LLMs) have demonstrated remarkable performance across various domains.<n>We propose Optimal Transport-Driven Radiology Report Generation (OTDRG) to align image features with disease labels extracted from reports.<n>OTDRG achieves state-of-the-art performance in both natural language generation (NLG) and clinical efficacy (CE) metrics.
  arXiv  Detail & Related papers  (2025-07-05T05:48:48Z)
• RadAlign: Advancing Radiology Report Generation with Vision-Language Concept Alignment [10.67889367763112]
  RadAlign is a novel framework that combines the predictive accuracy of vision-language models with the reasoning capabilities of large language models.<n>Our framework maintains strong clinical interpretability while reducing hallucinations, advancing automated medical imaging and report analysis through integrated predictive and generative AI.
  arXiv  Detail & Related papers  (2025-01-13T17:55:32Z)
• Generative causal testing to bridge data-driven models and scientific theories in language neuroscience [82.995061475971]
  We present generative causal testing (GCT), a framework for generating concise explanations of language selectivity in the brain.<n>We show that GCT can dissect fine-grained differences between brain areas with similar functional selectivity.
  arXiv  Detail & Related papers  (2024-10-01T15:57:48Z)
• CXR-Agent: Vision-language models for chest X-ray interpretation with uncertainty aware radiology reporting [0.0]
  We evaluate the publicly available, state of the art, foundational vision-language models for chest X-ray interpretation. We find that vision-language models often hallucinate with confident language, which slows down clinical interpretation. We develop an agent-based vision-language approach for report generation using CheXagent's linear probes and BioViL-T's phrase grounding tools.
  arXiv  Detail & Related papers  (2024-07-11T18:39:19Z)
• RaTEScore: A Metric for Radiology Report Generation [59.37561810438641]
  This paper introduces a novel, entity-aware metric, as Radiological Report (Text) Evaluation (RaTEScore) RaTEScore emphasizes crucial medical entities such as diagnostic outcomes and anatomical details, and is robust against complex medical synonyms and sensitive to negation expressions. Our evaluations demonstrate that RaTEScore aligns more closely with human preference than existing metrics, validated both on established public benchmarks and our newly proposed RaTE-Eval benchmark.
  arXiv  Detail & Related papers  (2024-06-24T17:49:28Z)
• The current status of large language models in summarizing radiology report impressions [13.402769727597812]
  The effectiveness of large language models (LLMs) in summarizing radiology report impressions remains unclear. Three types of radiology reports, i.e., CT, PET-CT, and Ultrasound reports, are collected from Peking University Cancer Hospital and Institute. We use the report findings to construct the zero-shot, one-shot, and three-shot prompts with complete example reports to generate the impressions.
  arXiv  Detail & Related papers  (2024-06-04T09:23:30Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime [70.04389979779195]
  This paper explores training medical vision-language models (VLMs) where the visual and language inputs are embedded into a common space. We explore several candidate methods to improve low-data performance, including adapting generic pre-trained models to novel image and text domains. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports.
  arXiv  Detail & Related papers  (2023-03-30T18:20:00Z)
• Cross-Modal Causal Intervention for Medical Report Generation [107.76649943399168]
  Radiology Report Generation (RRG) is essential for computer-aided diagnosis and medication guidance.<n> generating accurate lesion descriptions remains challenging due to spurious correlations from visual-linguistic biases.<n>We propose a two-stage framework named CrossModal Causal Representation Learning (CMCRL)<n> Experiments on IU-Xray and MIMIC-CXR show that our CMCRL pipeline significantly outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-03-16T07:23:55Z)
• Medical Image Captioning via Generative Pretrained Transformers [57.308920993032274]
  We combine two language models, the Show-Attend-Tell and the GPT-3, to generate comprehensive and descriptive radiology records. The proposed model is tested on two medical datasets, the Open-I, MIMIC-CXR, and the general-purpose MS-COCO.
  arXiv  Detail & Related papers  (2022-09-28T10:27:10Z)
• Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation [3.3978173451092437]
  Radiology report generation aims at generating descriptive text from radiology images automatically. A typical setting consists of training encoder-decoder models on image-report pairs with a cross entropy loss. We propose a novel weakly supervised contrastive loss for medical report generation.
  arXiv  Detail & Related papers  (2021-09-25T00:06:23Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.