Updates Surg
Iranian Research Center for HIV/AIDS, Iranian Institute for Reduction of High-Risk Behaviors, Tehran University of Medical Sciences, Tehran, Iran.
Published: March 2025
The proliferation of artificial intelligence (AI) in the healthcare sector is a present reality. The potential applications of Chat GPT in medicine are currently undergoing intense examination. This article seeks to examine the innovative capabilities and applications of Chat GPT in this field, highlighting its potential to revolutionize patient care and decision-making processes. PubMed, Scopus, Embase, Google Scholar, and Web of Science were searched by conducting a keyword search to locate studies examining the application of Chat GPT in the realm of plastic and reconstructive surgery. The titles, abstracts, and conclusions of the studies were scrutinized to select those most closely aligned with the focus of our study. This investigation involved a comprehensive review of 15 relevant articles from diverse geographical regions predominantly comprising original studies alongside five review articles. This study illustrates the significant promise of integrating Chat GPT across diverse areas of plastic surgery, encompassing research, surgeon and patient education, and clinical practice. However, the incorporation of Chat GPT into plastic surgery necessitates diligent oversight and the formulation of explicit guidelines and caution is necessary.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1007/s13304-025-02149-6 | DOI Listing |
Am J Respir Crit Care Med
March 2025
University of Iowa, Radiology and Biomedical Engineering, Iowa City, Iowa, United States;
Rationale: Quantifying functional small airways disease (fSAD) requires additional expiratory computed tomography (CT) scan, limiting clinical applicability. Artificial intelligence (AI) could enable fSAD quantification from chest CT scan at total lung capacity (TLC) alone (fSAD).
Objectives: To evaluate an AI model for estimating fSAD, compare it with dual-volume parametric response mapping fSAD (fSAD), and assess its clinical associations and repeatability in chronic obstructive pulmonary disease (COPD).
Front Artif Intell
February 2025
Department of Surgery, College of Medicine and Health Science, University of Gondar, Gondar, Ethiopia.
Heart disease is a leading cause of mortality worldwide, making accurate early detection essential for effective treatment and management. This study introduces a novel hybrid machine-learning approach that combines transfer learning using the VGG16 convolutional neural network (CNN) with various machine-learning classifiers for heart disease detection. A conditional tabular generative adversarial network (CTGAN) was employed to generate synthetic data samples from actual datasets; these were evaluated using statistical metrics, correlation analysis, and domain expert assessments to ensure the quality of the synthetic datasets.
View Article and Find Full Text PDFFront Digit Health
February 2025
Science of Functional Recovery and Reconstruction, Okayama University Graduate School of Medicine, Dentistry and Pharmaceutical Sciences, Okayama, Japan.
Background: Pediatric and adolescent/young adult (AYA) cancer patients face profound psychological challenges, exacerbated by limited access to continuous mental health support. While conventional therapeutic interventions often follow structured protocols, the potential of generative artificial intelligence (AI) chatbots to provide continuous conversational support remains unexplored. This study evaluates the feasibility and impact of AI chatbots in alleviating psychological distress and enhancing treatment engagement in this vulnerable population.
View Article and Find Full Text PDFSci Rep
March 2025
Univ Bretagne Occidentale, Brest, 29200, France.
Super-resolution (SR) techniques present a suitable solution to increase the image resolution acquired using an ultrasound device characterized by a low image resolution. This can be particularly beneficial in low-resource imaging settings. This work surveys advanced SR techniques applied to enhance the resolution and quality of fetal ultrasound images, focusing Dual back-projection based internal learning (DBPISR) technique, which utilizes internal learning for blind super-resolution, as opposed to blind super-resolution generative adversarial network (BSRGAN), real-world enhanced super-resolution generative adversarial network (Real-ESRGAN), swin transformer for image restoration (SwinIR) and SwinIR-Large.
View Article and Find Full Text PDFJ Dent
March 2025
Department of Conservative Dentistry and Periodontology, LMU University Hospital, LMU Munich, Goethestraße 70, 80 336, Munich, Germany. Electronic address:
Objectives: Class imbalance in datasets is one of the challenges of machine learning (ML) in medical image analysis. We employed synthetic data to overcome class imbalance when segmenting bitewing radiographs as an exemplary task for using ML.
Methods: After segmenting bitewings into classes, i.
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!
© LitMetric 2025. All rights reserved.