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Microbiological Rapid On-Site Evaluation for Pulmonary Infectious Diseases
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Large Language Models Utility for Rapid On-Site Evaluation in Interventional Pulmonology.

Maayan Flaschner1,2, Mordechai Reuven Kramer1,2, Alex Krolik1,2

  • 1Pulmonary Institute, Rabin Medical Center, Petah Tikva 4941492, Israel.

Diagnostics (Basel, Switzerland)
|June 12, 2026
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Summary

This study evaluated large language models (LLMs) like ChatGPT and Gemini for rapid on-site evaluation (ROSE) of lung biopsy samples. While cytologists achieved 0.75 accuracy, LLMs showed potential for accessible AI-assisted diagnostics in interventional pulmonology.

Keywords:
ChatGPTGeminiartificial intelligencecytologyinterventional pulmonologylarge language modelrapid on-site evaluation

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Area of Science:

  • Pulmonology
  • Pathology
  • Artificial Intelligence

Background:

  • Rapid on-site evaluation (ROSE) is crucial for assessing biopsy adequacy during interventional procedures.
  • Integrating artificial intelligence (AI) into ROSE enhances diagnostic accuracy but faces development challenges.
  • Free large language models (LLMs) offer a more accessible alternative for AI-driven diagnostic support.

Purpose of the Study:

  • To assess the diagnostic accuracy of ChatGPT and Gemini LLMs in evaluating cytological smears from interventional pulmonology procedures.
  • To explore the feasibility of using LLMs for AI-assisted ROSE in pulmonary diagnostics.

Main Methods:

  • Retrospective analysis of cytological smears from 48 interventional bronchoscopic and ultrasound-guided biopsies (2020-2025).
  • Images of ROSE-prepared samples were evaluated by ChatGPT-4o, ChatGPT-5, ChatGPT-5 "thinking", and Gemini 2.5 models.
  • Comparison of LLM performance against final histopathology reports and cytologist accuracy.

Main Results:

  • Cytologists achieved a balanced accuracy of 0.75.
  • ChatGPT-5 "thinking" demonstrated high concordance with an accuracy of 0.65 (Gwet's AC1 = 0.81).
  • Gemini 2.5 achieved an accuracy of 0.59 (Gwet's AC1 = 0.76).

Conclusions:

  • This is the first study to evaluate LLM-assisted ROSE in interventional pulmonology.
  • LLMs show potential for integration into pulmonary division workflows, offering accessible AI diagnostic support.
  • Further prospective studies are required to validate the impact of LLMs on diagnostic yield.