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Use of Augmented Reality for Training Assistance in Laparoscopic Surgery: Scoping Literature Review.
J Med Internet Res
January 2025
I3A, LoUISE Research Group, University of Castilla-La Mancha, Albacete, Spain.
Background: Laparoscopic surgery training is a demanding process requiring technical and nontechnical skills. Surgical training has evolved from traditional approaches to the use of immersive digital technologies such as virtual, augmented, and mixed reality. These technologies are now integral to laparoscopic surgery training.
View Article and Find Full Text PDFAdaptive Multi-Kernel Graph Neural Network for Drug-Drug Interaction Prediction.
Interdiscip Sci
January 2025
School of Computer Science, Qufu Normal University, Rizhao, 276826, China.
Combination therapy, which synergistically enhances treatment efficacy and inhibits disease progression through the combined effects of multiple drugs, has emerged as a mainstream approach for treating complex diseases and alleviating symptoms. However, drug-drug interactions (DDIs) can sometimes lead to adverse reactions, potentially endangering lives. Therefore, developing efficient and accurate DDI prediction methods is crucial for elucidating drug mechanisms and preventing side effects.
View Article and Find Full Text PDFThe clinical application of artificial intelligence in cancer precision treatment.
J Transl Med
January 2025
Department of General Surgery & Laboratory of Gastric Cancer, State Key Laboratory of Biotherapy / Collaborative Innovation Center of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Chengdu, China.
Background: Artificial intelligence has made significant contributions to oncology through the availability of high-dimensional datasets and advances in computing and deep learning. Cancer precision medicine aims to optimize therapeutic outcomes and reduce side effects for individual cancer patients. However, a comprehensive review describing the impact of artificial intelligence on cancer precision medicine is lacking.
View Article and Find Full Text PDFA systematic review of the impact of artificial intelligence on educational outcomes in health professions education.
BMC Med Educ
January 2025
Centre for Medical Education, University of Dundee, Dundee, UK.
Background: Artificial intelligence (AI) has a variety of potential applications in health professions education and assessment; however, measurable educational impacts of AI-based educational strategies on learning outcomes have not been systematically evaluated.
Methods: A systematic literature search was conducted using electronic databases (CINAHL Plus, EMBASE, Proquest, Pubmed, Cochrane Library, and Web of Science) to identify studies published until October 1st 2024, analyzing the impact of AI-based tools/interventions in health profession assessment and/or training on educational outcomes. The present analysis follows the PRISMA 2020 statement for systematic reviews and the structured approach to reporting in health care education for evidence synthesis.
Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.
J Imaging Inform Med
January 2025
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.
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