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Related Concept Videos

Classification of Systems-I01:26

Classification of Systems-I

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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
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Classification of Systems-II01:31

Classification of Systems-II

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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Related Experiment Video

Updated: May 5, 2026

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Evaluating Large Language Models for Automated Reporting and Data Systems Categorization: Cross-Sectional Study.

Qingxia Wu1, Qingxia Wu2,3, Huali Li4

  • 1Department of Medical Imaging, Henan Provincial People's Hospital & People's Hospital of Zhengzhou University, Zhengzhou, China.

JMIR Medical Informatics
|July 17, 2024
PubMed
Summary
This summary is machine-generated.

Large language models show promise for radiology, but their performance on Reporting and Data Systems (RADS) categorization varies. Claude-2, with structured prompts and guideline PDFs, achieved higher accuracy, especially with LI-RADS 2018.

Keywords:
ChatGPTLI-RADSLung-RADSO-RADSRadiology Reporting and Data Systemsaccuracycategorizationchatbotchatbotslarge language modelrecommendationrecommendations

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Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Natural Language Processing in Healthcare
  • Radiology Workflow Optimization

Background:

  • Large language models (LLMs) offer potential for enhancing radiology workflows.
  • Performance of LLMs on structured radiological tasks like Reporting and Data Systems (RADS) categorization is largely unexplored.
  • This study investigates LLM capabilities in standardized radiological reporting.

Purpose of the Study:

  • To evaluate three LLM chatbots: Claude-2, GPT-3.5, and GPT-4.
  • To assess their accuracy in assigning RADS categories to radiology reports.
  • To determine the impact of different prompting strategies on LLM performance.

Main Methods:

  • A cross-sectional study compared three chatbots using 30 radiology reports across LI-RADS, Lung-RADS, and O-RADS.
  • A three-level prompting strategy was employed: zero-shot, few-shot, and guideline PDF-informed prompts.
  • Radiology reports were prepared by board-certified radiologists, and chatbot responses were assessed by blinded reviewers.

Main Results:

  • Claude-2 demonstrated the highest accuracy (57% average) with few-shot prompts and guideline PDFs, particularly for LI-RADS 2018 (75% accuracy).
  • Prompt engineering significantly improved accuracy for all chatbots; Claude-2 showed enhanced performance with specific prompts, unlike GPT-4.
  • Chatbots performed better with LI-RADS 2018 compared to Lung-RADS 2022 and O-RADS.

Conclusions:

  • Claude-2 shows potential for RADS categorization when provided with structured prompts and guideline PDFs, especially for LI-RADS 2018.
  • Current LLM generations struggle with accurately categorizing cases based on more recent RADS criteria.
  • Further development is needed to improve LLM accuracy and reliability in diverse radiological reporting scenarios.