Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps.
Pleural diseases pose a significant burden on healthcare systems due to diagnostic challenges and high costs. Artificial intelligence (AI) has the potential to provide faster, more accurate, and more reliable results in the diagnosis of these diseases. This review evaluates the current status of AI...
| Publicado en: | Thoracic Research & Practice Vol. 27; no. 1; pp. 57 - 68 |
|---|---|
| Autores principales: | , |
| Formato: | review tables/charts Journal Article |
| Publicado: |
Galenos Yayinevi Tic. LTD. STI
Jan2026
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191304693&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191304693 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29799139 N9E4 jtl: Thoracic Research & Practice issn: 29799139 maglogo: N pubinfo: dt: Jan2026 vid: 27 iid: 1 pid: 28155 pub: Galenos Yayinevi Tic. LTD. STI artinfo: ui: 191304693 191304693 191304693 10.4274/ThoracResPract.2025.2025-6-2 191304693 ppf: 57 ppct: 11 formats: tig: atl: Artificial Intelligence in Pleural Diseases: Current Applications and Next Steps. aug: au: Karataş, Ferhan Dikensoy, Öner affil: Department of Pulmonary Diseases, Koç University Hospital, İstanbul, Türkiye sug: subj: Pleural Diseases Diagnosis Pleural Diseases Therapy Artificial Intelligence Pleural Effusion Diagnosis Pleural Effusion, Malignant Diagnosis Tuberculosis Diagnosis Pleurisy Diagnosis Pneumothorax Diagnosis Mesothelioma, Malignant Diagnosis Deep Learning Algorithms Diagnosis, Computer Assisted Radiography, Computed Radiography, Thoracic Positron Emission Tomography Computed Tomography Lung Ultrasonography Biological Markers Blood ab: Pleural diseases pose a significant burden on healthcare systems due to diagnostic challenges and high costs. Artificial intelligence (AI) has the potential to provide faster, more accurate, and more reliable results in the diagnosis of these diseases. This review evaluates the current status of AI technologies in the diagnosis of pleural effusion (PE), malignant PE, tuberculosis pleurisy (TP), pneumothorax, and malignant pleural mesothelioma (MPM). Deep learning algorithms developed for radiological diagnosis provide high sensitivity and specificity in determining the presence and severity of PE. AI models that integrate clinical parameters such as chest computed tomography (CT), positron emission tomography (PET)-CT, and tumour markers in distinguishing between benign and malignant effusions have significantly improved diagnostic accuracy (area under the curve: >0.90). In cytological diagnosis, computer-assisted systems such as Aitrox have demonstrated performance comparable to that of expert cytopathologists in diagnosing malignant effusions. In the diagnosis of TP, AI models outperform conventional diagnostic methods, particularly when combined with laboratory parameters such as adenosine deaminase. Food and Drug Administration-approved AI models are effectively used for the rapid diagnosis of pneumothorax and for emergency interventions. In MPM diagnosis, AI models using PET-CT images and three-dimensional segmentation offer significant advantages in prognostic evaluation and treatment response monitoring. However, large-scale, multi-centre studies are needed to standardise and generalise AI models. In light of these developments, AI may fundamentally change the diagnostic management of pleural diseases. pubtype: Academic Journal doctype: review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|