Artificial intelligence and pneumonia: a rapidly evolving frontier

Ginsburg, Amy Sarah; McCollum, Eric D

doi:10.1016/s2214-109x(23)00463-1

Cited by 4 publications

(1 citation statement)

References 6 publications

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“…The benefits of artificial intelligence in pneumonia include improving human performance, democratizing medical knowledge and excellence, automating repetitive tasks, and optimizing the allocation of limited resources [ 48 ].…”

Section: Discussionmentioning

confidence: 99%

Work of Breathing: Physiology, Measurement, and Diagnostic Value in Childhood Pneumonia

Amirav,

Manucot,

Crawley

et al. 2024

Children

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In clinical practice, increased “work of breathing” (WOB) is used to rapidly identify the acutely ill child in need of immediate clinical care, and is commonly used to support a clinical diagnosis of pneumonia. However, this key clinical sign is poorly understood and inconsistently defined. This review discusses the physiology, measurement, and clinical assessment of WOB, highlighting its utility in the recognition of pneumonia in under-resourced settings, where access to diagnostic imaging may be limited.

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Section: Discussionmentioning

confidence: 99%

Work of Breathing: Physiology, Measurement, and Diagnostic Value in Childhood Pneumonia

Amirav,

Manucot,

Crawley

et al. 2024

Children

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Artificial intelligence for the optimal management of community-acquired pneumonia

Barbieri,

Battini,

Sessa

2024

Current Opinion in Pulmonary Medicine

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Purpose of review This timely review explores the integration of artificial intelligence (AI) into community-acquired pneumonia (CAP) management, emphasizing its relevance in predicting the risk of hospitalization. With CAP remaining a global public health concern, the review highlights the need for efficient and reliable AI tools to optimize resource allocation and improve patient outcomes. Recent findings Challenges in CAP management delve into the application of AI in predicting CAP-related hospitalization risks, and complications, and mortality. The integration of AI-based risk scores in managing CAP has the potential to enhance the accuracy of predicting patients at higher risk, facilitating timely intervention and resource allocation. Moreover, AI algorithms reduce variability associated with subjective clinical judgment, promoting consistency in decision-making, and provide real-time risk assessments, aiding in the dynamic management of patients with CAP. Summary The development and implementation of AI-tools for hospitalization in CAP represent a transformative approach to improving patient outcomes. The integration of AI into healthcare has the potential to revolutionize the way we identify and manage individuals at risk of severe outcomes, ultimately leading to more efficient resource utilization and better overall patient care.

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Unveiling Disease Progression in Chest Radiographs through AI

Alves,

Venkadesh

2024

Radiology: Artificial Intelligence

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Artificial intelligence and pneumonia: a rapidly evolving frontier

Cited by 4 publications

References 6 publications

Work of Breathing: Physiology, Measurement, and Diagnostic Value in Childhood Pneumonia

Work of Breathing: Physiology, Measurement, and Diagnostic Value in Childhood Pneumonia

Artificial intelligence for the optimal management of community-acquired pneumonia

Unveiling Disease Progression in Chest Radiographs through AI

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