Role for artificial intelligence in respiratory diseases—chronic obstructive pulmonary disease

Carlin, C; Taylor, Ashleigh; Loon, Isabel van; McDowell, G; Burns, Shane; McGinness, P; Lowe, David J

doi:10.21037/jhmhp-20-119

Cited by 4 publications

(4 citation statements)

References 64 publications

(61 reference statements)

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“…2021) research on AI's role in COPD is valuable for healthcare professionals and provides a wide range from the diagnosis of the disease to the prediction of the disease outcome and management. These studies provide healthcare workers with a greater understanding to about the AI implementation in various phases of COPD [18,19].…”

Section: Discussionmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

Hussain,

Marlowe,

Ali

et al. 2024

Cureus

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This systematic review examines the transformative impact of artificial intelligence (AI) in managing lung disorders through a comprehensive analysis of articles spanning 2014 to 2023. Evaluating AI's multifaceted roles in radiological imaging, disease burden prediction, detection, diagnosis, and molecular mechanisms, this review presents a critical synthesis of key insights from select articles. The findings underscore AI's significant strides in bolstering diagnostic accuracy, interpreting radiological imaging, predicting disease burdens, and deepening the understanding of tuberculosis (TB), chronic obstructive pulmonary disease (COPD), silicosis, pneumoconiosis, and lung fibrosis. The synthesis positions AI as a revolutionary tool within the healthcare system, offering vital implications for healthcare workers, policymakers, and researchers in comprehending and leveraging AI's pivotal role in lung disease management.

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Section: Discussionmentioning

confidence: 99%

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

Hussain,

Marlowe,

Ali

et al. 2024

Cureus

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“…In another case a ref. [13] presented AI tools that can help address the quality gaps that are limiting accurate diagnosis and the use of evidence-based interventions to reorient care toward proactive preventative management in chronic obstructive pulmonary disease (COPD). Also, ref.…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Christopoulou

2024

BioMedInformatics

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Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area.

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“…It has enabled clinical support research and applications to provide actionable insights by utilizing large amounts of intensive care unit patient datasets that are useful in many clinical scenarios [ 16 ]. Machine learning can predict in-hospital mortality and the risk of 30-day readmission due to COPD exacerbation [ 21 ].…”

Section: Related Workmentioning

confidence: 99%

Intelligent Clinical Decision Support System for Managing COPD Patients

Pereira,

Antunes,

Rosa

et al. 2023

JPM

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Chronic obstructive pulmonary disease (COPD) is the third leading cause of death worldwide. Health remote monitoring systems (HRMSs) play a crucial role in managing COPD patients by identifying anomalies in their biometric signs and alerting healthcare professionals. By analyzing the relationships between biometric signs and environmental factors, it is possible to develop artificial intelligence models that are capable of inferring patients’ future health deterioration risks. In this research work, we review recent works in this area and develop an intelligent clinical decision support system (CIDSS) that is capable of providing early information concerning patient health evolution and risk analysis in order to support the treatment of COPD patients. The present work’s CIDSS is composed of two main modules: the vital signs prediction module and the early warning score calculation module, which generate the patient health information and deterioration risks, respectively. Additionally, the CIDSS generates alerts whenever a biometric sign measurement falls outside the allowed range for a patient or in case a basal value changes significantly. Finally, the system was implemented and assessed in a real case and validated in clinical terms through an evaluation survey answered by healthcare professionals involved in the project. In conclusion, the CIDSS proves to be a useful and valuable tool for medical and healthcare professionals, enabling proactive intervention and facilitating adjustments to the medical treatment of patients.

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Role for artificial intelligence in respiratory diseases—chronic obstructive pulmonary disease

Cited by 4 publications

References 64 publications

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

A Systematic Review of Artificial Intelligence Applications in the Management of Lung Disorders

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Intelligent Clinical Decision Support System for Managing COPD Patients

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