Characterising research trends in bronchiectasis through AI-powered analytics.

BACKGROUND

Interest in bronchiectasis is increasing and no prior study has used artificial intelligence (AI) to interrogate its rich, multidimensional literature to characterise research trends, themes and knowledge gaps.

METHODS

We reviewed original bronchiectasis research between 1949 and 2024 (a 75-year period) to identify, characterise and assess research trends and trajectories using two AI-powered approaches: 1) Atlas, an AI topic-modelling tool; and 2) a custom model, leveraging ChatGPT embedding and text generation models.

RESULTS

AI-powered analytics revealed a nine-fold increase in bronchiectasis research speed since 2000, typified by enhanced richness with four new research topics emerging every 5 years. Publication trends mirror clinical and technological advances, exemplified by significant rises in computed tomography, microbiome and clinical studies following the adoption of high-resolution computed tomography (1970s), next-generation sequencing (2005) and the first clinical guidelines (2008-2010), respectively. Topics with sustained growth (i.e. popular topics) include bronchiectasis-COPD overlap, microbiome infection, cardiovascular health and exacerbations. Those with sudden, short-term increased interest (i.e. trending topics) have focused on microbial pathogens and primary ciliary dyskinesia genetics. Mortality represents a nascent topic, demonstrating the highest year-on-year interest. Growth of research within the "vicious vortex" demonstrates thematic imbalance, with few studies overlapping with non-vortex components. Evolving research focus towards inflammation is evident, with increased work on comorbidities and quality of life demonstrating a shift from disease-centric to patient-centric research.

CONCLUSION

AI captures bronchiectasis as a dynamic and interdisciplinary field in continuing growth. Emerging research topics extend beyond the vicious vortex framework, indicating a transition from disease-centric to patient-centric approaches to optimise clinical care.