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AI applications in pediatric pulmonary, sleep, and critical care medicine
Airways Disorders Network
Pediatric Chest Medicine Section
Artificial intelligence (AI) refers to the science and engineering of making intelligent machines that mimic human cognitive functions, such as learning and problem solving.1 Asthma exacerbations in young children were detected reliably by AI-aided stethoscope alone.2 Inhaler use has been successfully tracked using active and passive patient input to cloud-based dashboards.3 Asthma specialists can potentially use this knowledge to intervene in real time or more frequent intervals than the current episodic care.
Sleep trackers using commercial-grade sensors can provide useful information about sleep hygiene, sleep duration, and nocturnal awakenings. An increasing number of “wearables” and “nearables” that utilize AI algorithms to evaluate sleep duration and quality are FDA approved. AI-based scoring of polysomnography data can improve the efficiency of a sleep laboratory. Big data analysis of CPAP compliance in children led to identification of actionable items that can be targeted to improve patient outcomes.4
The use of AI models in clinical decision support can result in fewer false alerts and missed patients due to increased model accuracy. Additionally, large language model tools can automatically generate comprehensive progress notes incorporating relevant electronic medical records data, thereby reducing physician charting time.
These case uses highlight the potential to improve workflow efficiency and clinical outcomes in pediatric pulmonary and critical care by incorporating AI tools in medical decision-making and management.
References
1. McCarthy JF, Marx KA, Hoffman PE, et al. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci. 2004;1020:239-262.
2. Emeryk A, Derom E, Janeczek K, et al. Home monitoring of asthma exacerbations in children and adults with use of an AI-aided stethoscope. Ann Fam Med. 2023;21(6):517-525.
3. Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A. How is my child’s asthma?” Digital phenotype and actionable insights for pediatric asthma. JMIR Pediatr Parent. 2018;1(2):e11988.
4. Bhattacharjee R, Benjafield AV, Armitstead J, et al. Adherence in children using positive airway pressure therapy: a big-data analysis [published correction appears in Lancet Digit Health. 2020 Sep;2(9):e455.]. Lancet Digit Health. 2020;2(2):e94-e101.
Airways Disorders Network
Pediatric Chest Medicine Section
Artificial intelligence (AI) refers to the science and engineering of making intelligent machines that mimic human cognitive functions, such as learning and problem solving.1 Asthma exacerbations in young children were detected reliably by AI-aided stethoscope alone.2 Inhaler use has been successfully tracked using active and passive patient input to cloud-based dashboards.3 Asthma specialists can potentially use this knowledge to intervene in real time or more frequent intervals than the current episodic care.
Sleep trackers using commercial-grade sensors can provide useful information about sleep hygiene, sleep duration, and nocturnal awakenings. An increasing number of “wearables” and “nearables” that utilize AI algorithms to evaluate sleep duration and quality are FDA approved. AI-based scoring of polysomnography data can improve the efficiency of a sleep laboratory. Big data analysis of CPAP compliance in children led to identification of actionable items that can be targeted to improve patient outcomes.4
The use of AI models in clinical decision support can result in fewer false alerts and missed patients due to increased model accuracy. Additionally, large language model tools can automatically generate comprehensive progress notes incorporating relevant electronic medical records data, thereby reducing physician charting time.
These case uses highlight the potential to improve workflow efficiency and clinical outcomes in pediatric pulmonary and critical care by incorporating AI tools in medical decision-making and management.
References
1. McCarthy JF, Marx KA, Hoffman PE, et al. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci. 2004;1020:239-262.
2. Emeryk A, Derom E, Janeczek K, et al. Home monitoring of asthma exacerbations in children and adults with use of an AI-aided stethoscope. Ann Fam Med. 2023;21(6):517-525.
3. Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A. How is my child’s asthma?” Digital phenotype and actionable insights for pediatric asthma. JMIR Pediatr Parent. 2018;1(2):e11988.
4. Bhattacharjee R, Benjafield AV, Armitstead J, et al. Adherence in children using positive airway pressure therapy: a big-data analysis [published correction appears in Lancet Digit Health. 2020 Sep;2(9):e455.]. Lancet Digit Health. 2020;2(2):e94-e101.
Airways Disorders Network
Pediatric Chest Medicine Section
Artificial intelligence (AI) refers to the science and engineering of making intelligent machines that mimic human cognitive functions, such as learning and problem solving.1 Asthma exacerbations in young children were detected reliably by AI-aided stethoscope alone.2 Inhaler use has been successfully tracked using active and passive patient input to cloud-based dashboards.3 Asthma specialists can potentially use this knowledge to intervene in real time or more frequent intervals than the current episodic care.
Sleep trackers using commercial-grade sensors can provide useful information about sleep hygiene, sleep duration, and nocturnal awakenings. An increasing number of “wearables” and “nearables” that utilize AI algorithms to evaluate sleep duration and quality are FDA approved. AI-based scoring of polysomnography data can improve the efficiency of a sleep laboratory. Big data analysis of CPAP compliance in children led to identification of actionable items that can be targeted to improve patient outcomes.4
The use of AI models in clinical decision support can result in fewer false alerts and missed patients due to increased model accuracy. Additionally, large language model tools can automatically generate comprehensive progress notes incorporating relevant electronic medical records data, thereby reducing physician charting time.
These case uses highlight the potential to improve workflow efficiency and clinical outcomes in pediatric pulmonary and critical care by incorporating AI tools in medical decision-making and management.
References
1. McCarthy JF, Marx KA, Hoffman PE, et al. Applications of machine learning and high-dimensional visualization in cancer detection, diagnosis, and management. Ann N Y Acad Sci. 2004;1020:239-262.
2. Emeryk A, Derom E, Janeczek K, et al. Home monitoring of asthma exacerbations in children and adults with use of an AI-aided stethoscope. Ann Fam Med. 2023;21(6):517-525.
3. Jaimini U, Thirunarayan K, Kalra M, Venkataraman R, Kadariya D, Sheth A. How is my child’s asthma?” Digital phenotype and actionable insights for pediatric asthma. JMIR Pediatr Parent. 2018;1(2):e11988.
4. Bhattacharjee R, Benjafield AV, Armitstead J, et al. Adherence in children using positive airway pressure therapy: a big-data analysis [published correction appears in Lancet Digit Health. 2020 Sep;2(9):e455.]. Lancet Digit Health. 2020;2(2):e94-e101.