Introduction: We aimed to show the effect of patient information videos on preoperative anxiety before performing the percutaneous nephrolithotomy (PCNL ) for kidney stones.
Methods: This study was designed as a randomized, controlled trial with patients scheduled for PCNL operation for kidney stones. Demographic information, such as age, gender, and American Society of Anesthesiologists (ASA) score, were collected. State-Trait Anxiety Inventory (ST AI) was used to measure anxiety levels. Before informing the patients, anxiety levels were evaluated using the ST AI-state (pre-information ST AI-S) and ST AI-trait (ST AI-T). Patients were randomly divided into two groups: both groups received written and verbal information, while the "video" group was also shown a video of a PCNL procedure. The post-information anxiety levels of both groups were evaluated using ST AI-S (post-information).
Results: A total of 109 patients were included in the study and 50 patients were included in each group after nine patients were excluded. The participants in the two groups were similar in terms of gender distribution, mean age, and pre-information ST AI-S scores. Post-information ST AI-S scores were statistically significantly lower in the video group (p=0.02). There was no significant difference between post-information and pre-information ST AI-S scores in the no-video group (p=0.86), whereas a significant decrease was found in post-information ST AI-S scores in the video group (p<0.01).
Conclusions: In addition to written and verbal information before PCNL operations, informative videos are an inexpensive, effective method to reduce preoperative anxiety levels. Video-based briefing may be routinely used in addition to preoperative verbal and written information.
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http://dx.doi.org/10.5489/cuaj.8005 | DOI Listing |
Clin Pract
December 2024
Department of Restorative Dental Medicine and Endodontics, Study of Dental Medicine, School of Medicine, University of Split, 21000 Split, Croatia.
Aim: This study aims to assess Croatian dentists' knowledge, attitudes, and use of artificial intelligence (AI) and modern technology, while also identifying perceived barriers to AI and modern technology adoption and evaluating the need for further education and training.
Materials And Methods: A cross-sectional survey was conducted in February 2024 among general dentists in Croatia using a self-structured questionnaire. A total of 200 respondents filled out the questionnaire.
Intern Med J
December 2024
Department of Interventional Neuroradiology, Austin Health, Melbourne, Victoria, Australia.
Enhancing patient comprehension of their health is crucial in improving health outcomes. The integration of artificial intelligence (AI) in distilling medical information into a conversational, legible format can potentially enhance health literacy. This review aims to examine the accuracy, reliability, comprehensiveness and readability of medical patient education materials (PEMs) simplified by AI models.
View Article and Find Full Text PDFPlast Reconstr Surg Glob Open
December 2024
From Division of Plastic and Reconstructive Surgery, Rutgers New Jersey Medical School, Newark, N.J.
Background: Given the public's tendency to overestimate the capability of artificial intelligence (AI) in surgical outcomes for plastic surgery, this study assesses the accuracy of AI-generated images for breast augmentation and reduction, aiming to determine if AI technology can deliver realistic expectations and can be useful in a surgical context.
Methods: We used AI platforms GetIMG, Leonardo, and Perchance to create pre- and postsurgery images of breast augmentation and reduction. Board-certified plastic surgeons and plastic surgery residents evaluated these images using 11 metrics and divided them into 2 categories: realism and clinical value.
J Plast Reconstr Aesthet Surg
November 2024
Department of Plastic Surgery, Frankston Hospital, Peninsula Health, 2 Hastings Road, Frankston 3199, Australia; Department of Surgery, Peninsula Clinical School, Central Clinical School, Faculty of Medicine, Monash University, 2 Hastings Road, Frankston 3199, Australia.
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn from data and discern patterns without direct instruction. This review explores cutting-edge developments in microsurgery through the lens of AI applications. By analyzing a wide range of studies, this paper highlights AI's transformative role in enhancing microsurgical techniques and decision-making processes.
View Article and Find Full Text PDFFront Big Data
December 2024
School of Business, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, Kerala, India.
Introduction: The rapid escalation of cyber threats necessitates innovative strategies to enhance cybersecurity and privacy measures. Artificial Intelligence (AI) has emerged as a promising tool poised to enhance the effectiveness of cybersecurity strategies by offering advanced capabilities for intrusion detection, malware classification, and privacy preservation. However, this work addresses the significant lack of a comprehensive synthesis of AI's use in cybersecurity and privacy across the vast literature, aiming to identify existing gaps and guide further progress.
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