ReviewPulmonary therapy2020
Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review.
Review in Pulmonary therapy, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
28 citing papers in PubMed.
- Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case-control and prospective cohort study.BMC pulmonary medicine · 2021Trial
- The Role of Machine Learning and Artificial Intelligence in Enhancing Critical Care Nursing Practice: A Scoping Review.Nursing in critical care · 2026Article
- AI for Prognosis Among People Living With HIV: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Paired Validation of a Multimodal Vision-Transformer System for the Non-Invasive Diagnosis of Idiopathic Pulmonary Fibrosis.Diagnostics (Basel, Switzerland) · 2026Article
- Defining physician-AI collaboration in pulmonary and critical care medicine: concepts and illustrative examples for clinical reasoning.ATS scholar · 2026Article
- An interpretable machine-learning model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury.Frontiers in medicine · 2026Article
- Cost-effectiveness of novel diagnostic tools for idiopathic pulmonary fibrosis in the United States.BMC health services research · 2025Article
- Development and External Validation of a Machine Learning Model to Predict Restriction from Spirometry.medRxiv : the preprint server for health sciences · 2025Article
- Unveiling the Influence of AI on Advancements in Respiratory Care: Narrative Review.Interactive journal of medical research · 2024Review
- A Machine Learning System to Indicate Diagnosis of Idiopathic Pulmonary Fibrosis Non-Invasively in Challenging Cases.Diagnostics (Basel, Switzerland) · 2024Article
- 21st century critical care medicine: An overview.World journal of critical care medicine · 2024Review
- Analysis of Validation Performance of a Machine Learning Classifier in Interstitial Lung Disease Cases Without Definite or Probable Usual Interstitial Pneumonia Pattern on CT Using Clinical and Pathology-Supported Diagnostic Labels.Journal of imaging informatics in medicine · 2024Article
- Machine learning predicts lung recruitment in acute respiratory distress syndrome using single lung CT scan.Annals of intensive care · 2023Article
- Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study.BMC pulmonary medicine · 2023Article
- Assessing Barriers to Implementation of Machine Learning and Artificial Intelligence-Based Tools in Critical Care: Web-Based Survey Study.JMIR perioperative medicine · 2023Article
- A novel infrasound and audible machine-learning approach to the diagnosis of COVID-19.ERJ open research · 2022Article
- The principles of whole-hospital predictive analytics monitoring for clinical medicine originated in the neonatal ICU.NPJ digital medicine · 2022Review
- Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review.BMJ open respiratory research · 2022Article
- Diagnosing capillary leak in critically ill patients: development of an innovative scoring instrument for non-invasive detection.Annals of intensive care · 2021Article
- Utilizing Artificial Intelligence in Critical Care: Adding A Handy Tool to Our Armamentarium.Cureus · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.