The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics.
Abstract
Context and objectives Lung ultrasound (LUS) is a safe low-cost tool that enables diagnosis, monitoring and guidance for interventional procedures at the patient's bedside. However, its expansion is hindered by a lack of training programs and the inherent difficulty of interpreting ultrasound images. In this context, Artificial Intelligence (AI) is emerging as a supportive tool for LUS interpretation, ensuring diagnostic efficacy and mitigating the shortage of experts. This systematic review aims to summarize and analyze recent advances in AI-based tools to support LUS interpretation. Methods A systematic literature search was conducted across Web of Science, IEEE Xplore, and PubMed databases to identify peer-reviewed original journal articles published between 2015 and November 2025 that employed AI for the identification and localization of lung artifacts, anatomical structures, and pathological findings. Risk of bias was assessed using PROBAST + AI. Results Twenty-four studies were included, identifying three main strategies: segmentation (10 studies), object detection (4 studies), and the generation of visual explanations through saliency maps (10 studies). All employed CNN-based architectures. The evaluation metrics used were heterogeneous. The PROBAST + AI assessment showed relevant risk-of-bias concerns, mainly concentrated in the participants and analysis domains. Conclusions The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics. It is necessary to move towards solutions designed for specific clinical environments and to adopt standardized protocols and evaluations that facilitate their implementation in clinical practice. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420261322517, PROSPERO CRD420261322517.
AI should be viewed not as a replacement for ultrasound professionals but as a decision-support technology that may improve consistency, efficiency, and diagnostic confidence when carefully validated in real-world clinical settings.
Bayan Alghamdi, Eman M Alrewily, Sharefa S Alghamdi· Ultrasound Quarterly· 0 citations
Background: Artificial intelligence (AI) is increasingly being incorporated into radiology, not only for image interpretation but also for scheduling, examination protocoling, image acquisition, reconstruction, worklist prioritisation, quantitative analysis, reporting, communication, and follow-up. The clinical value of these systems depends on more than algorithmic accuracy. It also depends on interoperability, usability, external validation, human oversight, institutional readiness, and the ability to demonstrate measurable improvement in patient care. Objective: This review examines how AI is reshaping radiology workflow, summarises clinically relevant applications across imaging modalities, evaluates evidence regarding diagnostic performance and operational efficiency, and discusses implementation, ethics, regulation, workforce, economic, and equity-related considerations. Methods: A structured narrative review framework was developed using PubMed/MEDLINE, Embase, Scopus, Web of Science, Cochrane Library, PubMed Central, major radiology journals, and publicly available regulatory and professional sources. Original clinical research articles published between January 2016 and August 2026 were prioritised. Studies were considered when they evaluated an AI application in clinical imaging, measured diagnostic or workflow outcomes, or described prospective implementation. Because the studies differed substantially in task, population, modality, endpoint, and reference standard, findings were synthesised narratively rather than pooled statistically. Results: AI has demonstrated value in selected tasks involving mammographic screening, chest-radiograph interpretation, CT triage, MRI reconstruction, segmentation, quantitative imaging, and worklist prioritisation. Prospective and randomised studies suggest that AI can preserve or improve diagnostic performance while reducing selected forms of reader workload. Nevertheless, reported workflow gains are variable. Benefits may be attenuated by false-positive alerts, additional review requirements, poor system integration, case-mix differences, and local staffing patterns. Evidence connecting AI deployment with improved patient outcomes, cost-effectiveness, and long-term equity remains less mature. Conclusions: AI should be treated as a sociotechnical intervention rather than as a stand-alone software product. The most defensible implementation strategy is to begin with narrowly defined clinical problems, conduct local validation, integrate outputs into existing systems, train users, monitor performance after deployment, and retain accountable human oversight. Sustainable transformation will require prospective multicentre research, transparent reporting, interoperable architectures, lifecycle regulation, and deliberate protection against bias and unequal access.
Neelam Rao Bharti, Deeksha Jaiswal, Nidhi Goswami et al.· Genetics and Molecular Resea...· 0 citations
It is demonstrated that the hybrid AI-human model achieved high diagnostic performance for ruling out proximal DVT, with a sensitivity of 90-100% and a negative predictive value (NVP) of 87.5-100%.
Syed Zulfiqar Ali Shah, Muhammad Kaleem Akhtar, Waqar Ahmad et al.· Veredas do Direito· 0 citations
Background and Objective Ultrasound imaging has become a vital clinical diagnostic tool due to its cost-effectiveness and absence of ionizing radiation. In fetal ultrasound, the precise localization of standard ultrasound planes is a prerequisite for subsequent quantitative analysis and diagnosis. However, manual identification is highly operator-dependent and subjective, and the multi-planar data acquired by three-dimensional (3D) fetal ultrasound present challenges for efficient localization within large datasets. The objective of this narrative review is to systematically summarize the research progress and methodologies of artificial intelligence (AI) in the automated and precise localization of fetal ultrasound standard planes, while discussing the current challenges and future directions for its clinical application. Methods We conducted structured literature search in the PubMed and Web of Science databases to identify studies applying AI to ultrasound imaging, with a primary focus on fetal standard plane recognition. The search focused on all types of publications involving AI-based standard plane recognition in ultrasound between 1998 and 2025. Only peer-reviewed articles and review papers published in English were included. Titles and abstracts were screened to determine eligibility. Key Content and Findings This review details the transformative impact of AI, particularly deep learning, on automating standard plane localization in ultrasound, with a primary emphasis on fetal applications. Key findings reveal a progression from handcrafted features to advanced architectures like convolutional neural network and reinforcement learning, achieving expert-level accuracy across fetal standard planes, including abdominal, facial, brain, and cardiac views, while representative adult applications (e.g., hepatobiliary and thyroid imaging) are included as supplementary examples. The review identifies technical challenges, including data variability and computational costs, which are being addressed via transfer learning, attention mechanisms, and lightweight network design. The emergence of multi-organ models highlights a trend towards comprehensive AI systems for holistic screening. Conclusions AI significantly advances ultrasound by automating standard plane localization, particularly in fetal ultrasound, enhancing diagnostic consistency and workflow efficiency. Integration into clinical practice promises to reduce operator dependency and improve screening accessibility. Persistent challenges include model generalizability, data scarcity, and real-world clinical integration. Future work should prioritize multi-center validation, explainable AI, and end-to-end system development to fully realize this technology’s potential.
Ke-Zhen Wang, Ling-Ling Lei, Zi-Chao Liu et al.· Quantitative Imaging in Medi...· 0 citations
Abstract Objective This article aims to synthesize the diagnostic accuracy of artificial intelligence (AI) for CT-based diagnosis of major respiratory diseases (COVID-19, tuberculosis [TB], COPD/ILA, lung nodules/cancer) between 2020 and 2025, and to identify barriers to clinical adoption spanning data standardization, interpretability, workflow integration, and radiation protection. Materials and Methods Following PRISMA 2020, we searched PubMed/MEDLINE, Scopus, Web of Science, IEEE Xplore, and screened preprints (January 2020 to September 2025). Eligible human studies reported the diagnostic performance of AI (ML/DL/CNN/CAD) using CT. Primary outcomes were sensitivity, specificity, and AUC; secondary outcomes included CT dose metrics, explainability, and workflow effects. Risk of bias was assessed using QUADAS-2/PROBAST-AI; reporting quality was assessed using CLAIM. Bivariate random-effects meta-analysis yielded pooled estimates with HSROC; heterogeneity ( τ 2 , I 2 ) and publication bias (Deeks) were assessed. Certainty was graded using GRADE for DTA. Results Thirty-nine studies met criteria (predominantly CT; CNN-based). Pooled sensitivity/specificity were as follows: TB 0.895/0.935, COVID-19 0.872/0.914, nodules/cancer 0.886/0.869, COPD/ILA 0.856/0.844; heterogeneity was extreme ( I 2 ≈ 99–100%). PROBAST-AI indicated highest concerns in analysis and predictors; CLAIM adherence was uneven (external validation: 33%, prospective evaluation: 12%). AI reduced reporting time by ∼20 to 40% and supported low-dose CT with ∼25 to 40% CTDIvol reductions while maintaining sensitivity. Deeks' plots suggested modest asymmetry. GRADE certainty was moderate (COVID-19, nodules/cancer), low–moderate (COPD), and low (TB). Conclusion AI demonstrates promising diagnostic performance across respiratory CT tasks but faces generalizability, bias, and reporting gaps. Prospective multicenter validation, standardized protocols and dose reporting, calibrated/transparent models with quantitatively validated explainability, and assistive workflow deployment are essential for safe, reliable clinical adoption.
M. Sadeghi, S. Sina, Mohammad-Reza Mohammadian-Behbahani et al.· Indian Journal of Radiology...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
