Application of Artificial Intelligence in Pediatric Pulmonology: Current Scenario and Future Prospective | ||||
| SVU-International Journal of Medical Sciences | ||||
| Article 48, Volume 6, Issue 2, July 2023, Page 501-510 PDF (235.15 K) | ||||
| Document Type: Reviews Articles. | ||||
| DOI: 10.21608/svuijm.2023.195963.1544 | ||||
 View on SCiNiTO | ||||
| Author | ||||
Abdullah Abdullah Alzayed 
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| Department of Pediatrics, College of Medicine, Imam Mohammad Ibn Saud Islamic University, Riyadh 13317, Saudi Arabia | ||||
| Abstract | ||||
| Background: Artificial intelligence is well poised to be a multi-dimensional resource for children and their health in the future. Recent advancement in machine learning and algorithms have helped tackle diseases like asthma, pneumonia, and lung nodules. Objectives: Present study aims to provide a detailed overview of AI application in Pediatric Pulmonology. Methods: Many articles, published in review journals have been included to write the current review. The literature search was done by using electronic databases such as PubMed, Google Scholar, ResearchGate, Frontiers. Pictorial descriptions of AI efficiency have been included for better understanding. Studies have been reviewed to highlight the pandemic scenario and its effect on children. Results: Various studies have shown promising results of AI application in Pediatric Pulmonology through efficient imaging and digital technology-based devices. The utility of AI technique has been included under the following subheadings 1) Artificial Intelligence in Pediatric Auscultation, 2) Artificial Intelligence in Pediatric Imaging, 3) Artificial Intelligence based Pediatric PFTs, 4) Machine learning in prediction of childhood asthma persistence, 5) AI in Pneumonia diagnosis in children, 6) AI in Pediatric Pulmo-oncology, 6) Covid-19 scenario, 7) Current and Future Perspective of AI, 8) Challenges and Pitfalls of AI in Pediatric Pulmonology. Conclusion: AI technology has come a long way in the field of Pediatrics especially during the post-covid scenario through novel digital devices and automation. Lack of technology awareness, funding and AI in study curriculum are a few challenges faced by the health care professionals currently. These limitations must be addressed for more clinical utility in daily practice. | ||||
| Keywords | ||||
| Artificial Intelligence; Machine learning, Pediatrics; Pediatric pulmonology; Deep learning. pulmonary function test | ||||
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