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April 15, 2026Journal of ImagingOpen Access

Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions

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Authors

SLSarah LopesMMMaria R. MascarenhasJFJoão Fonseca

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Overview

This review highlights AI's applications in pulmonary endoscopy, revealing limitations and suggesting improvements for future implementation.

Key Points

  • This review examines the current applications of artificial intelligence in pulmonary endoscopy and identifies existing limitations.
  • Reviewed applications of AI in white-light bronchoscopy, image-enhanced bronchoscopy, and endobronchial ultrasound.
  • Analyzed current technologies like robotic bronchoscopies and AI-assisted training platforms.
  • Explored regulatory considerations and integration challenges in clinical practice.
  • Found advancements in deep learning models for detecting mucosal abnormalities and lymph-node characterization.
  • Noted improvements in lesion localization and reduction of operator-dependent variability.
  • Identified challenges such as limited external validation and the need for standardized datasets.

Cite This Study

Lopes et al. (2026) studied this question.

synapsesocial.com/papers/69df2c2fe4eeef8a2a6b1455https://doi.org/10.3390/jimaging12040167
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