Emerging Technologies for Lung Disease Detection and Clinical Assessment
A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Clinical Diagnosis and Prognosis".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 524
Special Issue Editors
Interests: pulmonary medicine; critical care; lung diseases
Special Issue Information
Dear Colleagues,
The assessment of lung disease represents a complex multimodal challenge involving the interrogation of clinical histories, functional testing, laboratory testing, genetic testing, and importantly, radiologic imaging. For years, chest radiography has been the most frequently used general assessment imaging test, and chest computed tomography (CT) is the highest precision noninvasive test for better characterizing lung disease; both are capable of conveying meaningful insights into a wide range of lung diseases. Improved precision beyond subjective interpretation with fields like radiomic analysis dates back decades; however, modern artificial intelligence technologies represent a seismic shift from theoretical bench work to routine clinical application.
Uses of modern machine learning and artificial intelligence in lung imaging are finally making their way into day-to-day clinical practice, with regulatory authorizations for uses in pre-procedural planning and targeting; lesion risk stratification and malignancy assessment; better characterization of diffuse lung diseases; and early detection of disease.
This Special Issue of Diagnostics focuses on the clinical translation of machine learning and AI in lung imaging as it pertains to real-world clinical use. We aim to contribute to the substantial base of the medical research literature by emphasizing clinical utility and the impact of such technologies today and in the near future.
Dr. Scott Matson
Dr. Joshua Jay Reicher
Guest Editors
Manuscript Submission Information
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Keywords
- medical imaging
- artificial intelligence (AI)
- radiology
- machine learning (ML)
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