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Updated: Sep 9, 2025

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Characterising research trends in bronchiectasis through AI-powered analytics.

Jayanth Kumar Narayana1, Yolanda Koo Wei Ling1, Micheál Mac Aogáin2,3

  • 1Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.

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|August 28, 2025
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Summary
This summary is machine-generated.

Artificial Intelligence (AI) analysis reveals a significant increase in bronchiectasis research, with new topics emerging rapidly. This shift indicates a move towards patient-centric care and highlights evolving research trends.

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Area of Science:

  • Pulmonary Medicine
  • Medical Informatics
  • Bibliometrics

Background:

  • Bronchiectasis research interest is growing.
  • Previous studies have not utilized Artificial Intelligence (AI) to analyze research trends, themes, and knowledge gaps in bronchiectasis.

Purpose of the Study:

  • To characterize research trends, themes, and knowledge gaps in bronchiectasis literature using AI.
  • To assess the evolution of bronchiectasis research over a 75-year period.

Main Methods:

  • A 75-year review (1949-2024) of original bronchiectasis research.
  • Two AI-powered approaches were employed: ATLAS (AI-topic modeling) and a custom ChatGPT-based model.

Main Results:

  • Bronchiectasis research has accelerated nine-fold since 2000, with four new topics emerging every five years.
  • Publication trends correlate with advances like High-Resolution Computed Tomography (HRCT), next-generation sequencing, and clinical guidelines.
  • Sustained research areas include bronchiectasis-COPD overlap and microbiome-infection; trending topics involve microbial pathogens and primary ciliary dyskinesia (PCD) genetics. Mortality is a nascent topic with high year-on-year interest.

Conclusions:

  • AI demonstrates bronchiectasis is a dynamic, interdisciplinary field with continuous growth.
  • Emerging topics suggest a transition from disease-centric to patient-centric research approaches for improved clinical care.