Call for papers - Artificial intelligence and machine learning: applications in pulmonary medicine

Edited by:

Feredun Azari, MD, Cleveland Clinic Foundation, USA
Shu-Yi Liao, MD, MPH, ScD, National Jewish Health, USA

Submission Status: Open | Submission Deadline: 25 April 2025

BMC Pulmonary Medicine is calling for submissions to our Collection on Artificial intelligence and machine learning: applications in pulmonary medicine. Advancements in artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize pulmonary medicine by enabling innovative approaches to diagnosis, treatment, and prevention of pulmonary disorders. BMC Pulmonary Medicine is launching this collection in alignment with the United Nations' Sustainable Development Goals (SDGs) 3: Good health and well-being and SDG 10: Reduced inequalities. The aim of this collection is to consolidate both fundamental and clinical research to advance our comprehension of pulmonary disorders.

New Content Item This Collection supports and amplifies research related to SDG 3: Good health and well-being and SDG 10: Reduced inequalities.

Submit to Collection

Meet the Guest Editors

Back to top

Feredun Azari, MD, Cleveland Clinic Foundation, USA

Dr Feredun S. Azari is a cardiothoracic surgery fellow at the Cleveland Clinic Foundation with a strong background in surgical research and innovation. He completed his residency at the University of Pennsylvania, where he also conducted postdoctoral research in thoracic surgery. Dr Azari's work focuses on developing novel artificial intelligence-guided intraoperative diagnostic modalities for lung cancer. His research has earned numerous awards, including the Thoracic Surgery Foundation Resident Research Award. As a pioneer in applying AI to pulmonary medicine, Dr Azari brings valuable expertise to this special issue on artificial intelligence and machine learning applications in pulmonary medicine.

Shu-Yi Liao, MD, MPH, ScD, National Jewish Health, USA

Dr Shu-Yi Liao is a distinguished expert in genomic medicine and pulmonary diseases. He is Assistant Professor of Medicine at National Jewish Health, Denver, and has a joint appointment at the University of Colorado School of Medicine. Dr Liao's pioneering research focuses on the genetic and environmental factors influencing sarcoidosis and other granulomatous diseases. With numerous high-impact publications and significant contributions to clinical guidelines, Dr Liao's work has earned him national and international recognition. His innovative approaches in multi-omics and machine learning have advanced the understanding of complex diseases, leading to improved patient care and personalized treatment strategies.

About the Collection

BMC Pulmonary Medicine is calling for submissions to our Collection on Artificial intelligence and machine learning: applications in pulmonary medicine. Advancements in artificial intelligence (AI) and machine learning (ML) have the potential to revolutionize pulmonary medicine by enabling innovative approaches to diagnosis, treatment, and prevention of pulmonary disorders. In this era of rapid technological advancements, AI can assist in early detection, risk assessment, and prognostic evaluation by analyzing large datasets, thus leading to improved patient outcomes and better management strategies.

BMC Pulmonary Medicine is launching this collection in alignment with the United Nations' Sustainable Development Goals (SDGs) 3: Good health and well-being and SDG 10: Reduced inequalities. The aim of this collection is to consolidate both fundamental and clinical research to advance our comprehension of pulmonary disorders.

BMC Pulmonary Medicine welcomes original research on the design, implementation, optimization, and clinical impact of AI applications in the field of pulmonary medicine. Topics of interest include, but are not limited to, the following:

• Machine learning (ML) algorithms for early detection of pulmonary diseases
• AI applications for diagnostic accuracy studies
• AI systems as an intervention in live clinical settings
• Predictive modeling using AI for personalized risk assessment of pulmonary disorders
• Application of AI in pulmonary imaging analysis
• Utilizing natural language processing and AI for analyzing electronic health records in pulmonary care
• Exploring the potential of AI in optimizing pulmonary surgical procedures
• Wearable devices and AI algorithms for continuous monitoring of pulmonary health
• AI-enabled precision medicine approaches for personalized treatment
• AI-powered automated risk scoring systems for exacerbations of pulmonary diseases
• Ethical considerations and challenges in the implementation of AI in pulmonary medicine

We encourage the use of standardized reporting guidelines for research with AI/ ML components to encourage authors to provide information to allow their work to be evaluated appropriately. Reporting guidelines and checklists have been developed for a broad range of study design and research types with AI/ML components. Those that have been developed, adapted, or are planned to be adapted for research using AI/ML can be found summarized in the table below:

Reporting guideline	AI-guideline	Study design	AI- guideline description
SPIRIT, 2013	SPIRIT-AI, 2020	Randomized controlled trials (protocols)	Used to report the protocols of randomized controlled trials evaluating AI systems as interventions.
CONSORT, 2010	CONSORT-AI, 2020	Randomized controlled trials	Used to report randomized controlled trials evaluating AI systems as interventions (large-scale, summative evaluation), independently of the AI system modality (diagnostic, prognostic, therapeutic). Focuses on effectiveness and safety.
TRIPOD, 2015	TRIPOD-AI, 2024	Prediction model evaluation	Used to report prediction models (diagnostic or prognostic) development, validation and updates.
STARD, 2015	STARD-AI	Diagnostic accuracy studies	Used to report diagnostic accuracy studies, either at development stage or as an offline validation in clinical settings.
N/A	CLAIM , 2020	Diagnostic accuracy studies	Used to report a wide spectrum of AI applications using medical images. Contains elements of the STARD 2015 guideline. Lists information such as descriptions of ground truth, data partitions, model description, and training and evaluation steps.
N/A	DECIDE-AI, 2022	Various (e.g. prospective cohort studies and non-randomized controlled trials) with additional features, such as modification of intervention, analysis of pre-specified subgroups or learning curve analysis.	Used to report the early evaluation of AI systems as an intervention in live clinical settings (small-scale, formative evaluation), independently of the study design and AI system modality (diagnostic, prognostic, therapeutic). Focuses on clinical utility, safety and human factors.

There are currently no articles in this collection.

Submission Guidelines

Back to top

This Collection welcomes submission of original research. Should you wish to submit a different article type, please read our submission guidelines to confirm that type is accepted by the journal.

Articles for this Collection should be submitted via our submission system, Snapp. Please, select the appropriate Collection title “Artificial intelligence and machine learning: applications in pulmonary medicine" under the “Details” tab during the submission stage.

Articles will undergo the journal’s standard peer-review process and are subject to all the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer-review process. The peer-review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.