Artificial Intelligence in Thoracic Surgery: A Review Bridging Innovation and Clinical Practice for the Next Generation of Surgical Care

Journal of Clinical Medicine, Год журнала: 2025, Номер 14(8), С. 2729 - 2729

Опубликована: Апрель 16, 2025

Background: Artificial intelligence (AI) is rapidly transforming thoracic surgery by enhancing diagnostic accuracy, surgical precision, intraoperative guidance, and postoperative management. AI-driven technologies, including machine learning (ML), deep learning, computer vision, robotic-assisted surgery, have the potential to optimize clinical workflows improve patient outcomes. However, challenges such as data integration, ethical concerns, regulatory barriers must be addressed ensure AI’s safe effective implementation. This review aims analyze current applications, benefits, limitations, future directions of AI in surgery. Methods: was conducted following Preferred Reporting Items for Systematic Reviews Meta-Analyses (PRISMA) guidelines. A comprehensive literature search performed using PubMed, Scopus, Web Science, Cochrane Library studies published up January 2025. Relevant articles were selected based on predefined inclusion exclusion criteria, focusing applications diagnostics, care. risk bias assessment Risk Bias Tool ROBINS-I non-randomized studies. Results: Out 279 identified studies, 36 met criteria qualitative synthesis, highlighting growing role care imaging analysis radiomics improved pulmonary nodule detection, lung cancer classification, lymph node metastasis prediction, while (RATS) has enhanced reduced operative times, recovery rates. Intraoperatively, AI-powered image-guided navigation, augmented reality (AR), real-time decision-support systems optimized planning safety. Postoperatively, predictive models wearable monitoring devices enabled early complication detection follow-up. remain, algorithmic biases, a lack multicenter validation, high implementation costs, concerns regarding security accountability. Despite these shown significant enhance outcomes, requiring further research standardized validation widespread adoption. Conclusions: poised revolutionize decision-making, improving optimizing workflows. adoption requires addressing key limitations through frameworks, governance. Future should focus digital twin technology, federated explainable (XAI) interpretability, reliability, accessibility. With continued advancements responsible will play pivotal shaping next generation precision

Язык: Английский

Enhancing Radiologist Productivity with Artificial Intelligence in Magnetic Resonance Imaging (MRI): A Narrative Review

Diagnostics, Год журнала: 2025, Номер 15(9), С. 1146 - 1146

Опубликована: Апрель 30, 2025

Artificial intelligence (AI) shows promise in streamlining MRI workflows by reducing radiologists' workload and improving diagnostic accuracy. Despite MRI's extensive clinical use, systematic evaluation of AI-driven productivity gains remains limited. This review addresses that gap synthesizing evidence on how AI can shorten scanning reading times, optimize worklist triage, automate segmentation. On 15 November 2024, we searched PubMed, EMBASE, MEDLINE, Web Science, Google Scholar, Cochrane Library for English-language studies published between 2000 focusing applications MRI. Additional searches grey literature were conducted. After screening relevance full-text review, 67 met inclusion criteria. Extracted data included study design, techniques, productivity-related outcomes such as time savings The categorized into five themes: scan automating segmentation, optimizing workflow, decreasing general time-saving or reduction. Convolutional neural networks (CNNs), especially architectures like ResNet U-Net, commonly used tasks ranging from segmentation to automated reporting. A few also explored machine learning-based automation software and, more recently, large language models. Although most demonstrated efficiency accuracy, limited external validation dataset heterogeneity could reduce broader adoption. offer potential enhance radiologist productivity, mainly through accelerated scans, streamlined workflows. Further research, including prospective standardized metrics, is needed enable safe, efficient, equitable deployment tools practice.

Язык: Английский