Lung Cancer Staging Using Chest CT and FDG PET/CT Free-Text Reports: Comparison Among Three ChatGPT Large-Language Models and Six Human Readers of Varying Experience

American Journal of Roentgenology, Год журнала: 2024, Номер 223(6)

Опубликована: Сен. 4, 2024

Although radiology reports are commonly used for lung cancer staging, this task can be challenging given radiologists' variable reporting styles as well reports' potentially ambiguous and/or incomplete staging-related information.

Язык: Английский

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Navigating Artificial Intelligence in Scientific Manuscript Writing: Tips and Traps

Indian journal of radiology and imaging - new series/Indian journal of radiology and imaging/Indian Journal of Radiology & Imaging, Год журнала: 2025, Номер 35(S 01), С. S178 - S186

Опубликована: Янв. 1, 2025

Abstract It is being increasingly recognized that the strategic use of artificial intelligence (AI) can catalyze process manuscript writing. However, it imperative we recognize hidden biases, pitfalls, and disadvantages relying solely on AI, such as accuracy concerns potential erosion nuanced human insight. With an emphasis crafting effective prompts inputs, this article reveals how to navigate labyrinth AI capabilities create a good-quality manuscript. also addresses evolving guidelines from various publishers, shedding light “leverage digital genie” responsibly ethically. We further explore which tools be harnessed for literature reviews, executing statistical analyses, polishing language Providing practical strategies maximizing AI's benefits, underscores indispensable value creativity critical thinking, stressing while “streamline mundane,” author's insight remains vital profound intellectual contributions.

Язык: Английский

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Effective Structured Information Extraction from Chest Radiography Reports Using Open-Weights Large Language Models

Radiology, Год журнала: 2025, Номер 314(1)

Опубликована: Янв. 1, 2025

Язык: Английский

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Large Language Models and Large Multimodal Models in Medical Imaging: A Primer for Physicians

Journal of Nuclear Medicine, Год журнала: 2025, Номер unknown, С. jnumed.124.268072 - jnumed.124.268072

Опубликована: Янв. 16, 2025

Large language models (LLMs) are poised to have a disruptive impact on health care. Numerous studies demonstrated promising applications of LLMs in medical imaging, and this number will grow as further evolve into large multimodal (LMMs) capable processing both text images. Given the substantial roles that LMMs care, it is important for physicians understand underlying principles these technologies so they can use them more effectively responsibly help guide their development. This article explains key concepts behind development application LLMs, including token embeddings, transformer networks, self-supervised pretraining, fine-tuning, others. It also describes technical process creating discusses cases imaging.

Язык: Английский

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Aligning large language models with radiologists by reinforcement learning from AI feedback for chest CT reports

European Journal of Radiology, Год журнала: 2025, Номер 184, С. 111984 - 111984

Опубликована: Фев. 6, 2025

Язык: Английский

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Benchmarking the diagnostic performance of open source LLMs in 1933 Eurorad case reports

npj Digital Medicine, Год журнала: 2025, Номер 8(1)

Опубликована: Фев. 12, 2025

Abstract Recent advancements in large language models (LLMs) have created new ways to support radiological diagnostics. While both open-source and proprietary LLMs can address privacy concerns through local or cloud deployment, provide advantages continuity of access, potentially lower costs. This study evaluated the diagnostic performance fifteen one closed-source LLM (GPT-4o) 1,933 cases from Eurorad library. provided differential diagnoses based on clinical history imaging findings. Responses were considered correct if true diagnosis appeared top three suggestions. Models further tested 60 non-public brain MRI a tertiary hospital assess generalizability. In datasets, GPT-4o demonstrated superior performance, closely followed by Llama-3-70B, revealing how are rapidly closing gap models. Our findings highlight potential as decision tools for challenging, real-world cases.

Язык: Английский

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Prompts to Table: Specification and Iterative Refinement for Clinical Information Extraction with Large Language Models

medRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Фев. 13, 2025

Extracting structured data from free-text medical records is laborious and error-prone. Traditional rule-based early neural network methods often struggle with domain complexity require extensive tuning. Large language models (LLMs) offer a promising solution but must be tailored to nuanced clinical knowledge complex, multipart entities. We developed flexible, end-to-end LLM pipeline extract diagnoses, per-specimen anatomical-sites, procedures, histology, detailed immunohistochemistry results pathology reports. A human-in-the-loop process create validated reference annotations for development set of 152 kidney tumor reports guided iterative refinement. To drive assessment performance we comprehensive error ontology- categorizing by significance (major vs. minor), source (LLM, manual annotation, or insufficient instructions), contextual origin. The finalized was applied 3,520 internal (of which 2,297 had pre-existing templated available cross referencing) evaluated adaptability using 53 publicly breast cancer After six iterations, major errors on the decreased 0.99% (14/1413 entities). identified 11 key contexts complications arose-including history integration, entity linking, specification granularity-which provided valuable insight in understanding our research goals. Using as reference, achieved macro-averaged F1 score 0.99 identifying subtypes 0.97 detecting metastasis. When adapted dataset, three iterations were required align domain-specific instructions, attaining 89% agreement curated data. This work illustrates that LLM-based extraction pipelines can achieve near expert-level accuracy carefully constructed instructions specific aims. Beyond raw metrics, itself-balancing specificity relevance-proved essential. approach offers transferable blueprint applying emerging capabilities other complex information tasks.

Язык: Английский

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Large language models for error detection in radiology reports: a comparative analysis between closed-source and privacy-compliant open-source models

European Radiology, Год журнала: 2025, Номер unknown

Опубликована: Фев. 20, 2025

Язык: Английский

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Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports

Deleted Journal, Год журнала: 2025, Номер 9(1)

Опубликована: Март 9, 2025

This study evaluates the performance of four large language models (LLMs) in classifying malignant lymphoma stages using Lugano classification from free-text FDG-PET reports Japanese Specifically, we assess GPT-4o, Claude 3.5 Sonnet, Llama 3 70B, and Gemma 2 27B their ability interpret unstructured radiology texts. In a retrospective single-center study, 80 patients who underwent staging FDG-PET/CT for were included. The "Findings" sections analyzed without pre-processing. Each LLM assigned based on these reports. Performance was compared to reference standard determined by expert radiologists. Statistical analyses involved overall accuracy, weighted kappa agreement. GPT-4o achieved highest accuracy at 75% (60/80 cases) with substantial agreement (weighted κ = 0.801). Sonnet had 61.3% (49/80, 0.763). 70B showed accuracies 58.8% 57.5%, respectively, all indicating outperformed other LLMs assigning demonstrated potential advanced clinical While immediate utility automatically predicting stage an existing report may be limited, results highlight value understanding standardizing data.

Язык: Английский

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Comprehensive testing of large language models for extraction of structured data in pathology

Communications Medicine, Год журнала: 2025, Номер 5(1)

Опубликована: Март 31, 2025

Abstract Background Pathology departments generate large volumes of unstructured data as free-text diagnostic reports. Converting these reports into structured formats for analytics or artificial intelligence projects requires substantial manual effort by specialized personnel. While recent studies show promise in using advanced language models structuring pathology data, they primarily rely on proprietary models, raising cost and privacy concerns. Additionally, important aspects such prompt engineering model quantization deployment consumer-grade hardware remain unaddressed. Methods We created a dataset 579 annotated German English versions. Six (proprietary: GPT-4; open-source: Llama2 13B, 70B, Llama3 8B, Qwen2.5 7B) were evaluated their ability to extract eleven key parameters from we investigated performance across different strategies techniques assess practical scenarios. Results Here that open-source with high precision, matching the accuracy GPT-4 model. The precision varies significantly configurations. These variations depend specific methods used during deployment. Conclusions Open-source demonstrate comparable solutions report data. This finding has significant implications healthcare institutions seeking cost-effective, privacy-preserving solutions. configurations provide valuable insights departments. Our publicly available bilingual serves both benchmark resource future research.

Язык: Английский

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