ReviewJournal of medical physics
Artificial Intelligence in Chest Computed Tomography Optimization: A Task-based Framework for Balancing Detectability and Radiation Dose.
Review in Journal of medical physics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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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.
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0 citing papers in PubMed.
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computed tomography (CT) of the chest is essential for thoracic imaging because of its high spatial resolution and broad clinical applications. However, variability in acquisition parameters and reconstruction techniques contributes to inconsistent radiation dose and image quality, while conventional optimization approaches remain limited by complex parameter interactions and operator dependence. Optimization in chest CT is increasingly recognized as a task-based challenge in which diagnostic performance depends on lesion detectability rather than global image quality metrics alone. This review synthesizes artificial intelligence (AI)-assisted approaches for optimizing chest CT protocols within a task-based framework, with emphasis on lesion detectability, diagnostic efficacy, and radiation dose reduction, while examining how AI can address protocol variability and improve patient safety. A structured narrative review of PubMed-indexed literature published between January 2015 and January 2025 was conducted, focusing on studies evaluating AI-assisted acquisition, deep learning reconstruction, and radiation dose optimization in adult chest CT. The review highlights that the most effective outcomes are achieved when AI-based reconstruction is integrated with dose optimization strategies such as automatic exposure control and patient-adapted acquisition, rather than applied in isolation. Although the magnitude of benefit varies according to diagnostic task, study design, and evaluation metrics, AI consistently reduces operator variability and promotes protocol standardization. Overall, AI enables task-based optimization of chest CT by prioritizing lesion detectability and diagnostic performance over traditional image quality metrics alone. By integrating acquisition, reconstruction, and continuous feedback mechanisms, AI has the potential to support consistent, patient-specific optimization while maintaining diagnostic efficacy when appropriately validated.
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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.