Hasil untuk "artificial intelligence"

K. Roy, Akhilesh R. Jaiswal, P. Panda

Friedemann Zenke, Ben Poole, S. Ganguli

Jun Tang, Gang Liu, Qingtao Pan

I. Arel, Derek C. Rose, T. Karnowski

D. Karaboğa, B. Basturk

L. Castro, J. Timmis

M. Genesereth, N. Nilsson

M. Minsky

R. Brooks

Vahid Nourani, A. H. Baghanam, J. Adamowski et al.

Muhammad Imran, Carlos Castillo, J. Lucas et al.

E. Davis, G. Marcus

Z. Yaseen, A. El-Shafie, O. Jaafar et al.

Eva B. Aamodt, Martin Røvang, Mona K. Beyer et al.

BackgroundMeasures of white matter hyperintensities (WMHs) represent a crucial part of post-stroke outcome prediction. Automatic WMH segmentation has proven particularly challenging in stroke cases. Using an improved method for WMH segmentation that incorporates stroke lesions, we set out to explore factors associated with higher WMH burden, as well as the association between WMH burden and post-stroke dependency across two different countries that may demonstrate significant variation in radiological presentation.MethodsA total of 384 acute ischemic stroke (AIS) survivors from the Norwegian Cognitive Impairment After Stroke (Nor-COAST; NO) study and the Houston Methodist Registry of Neurological Endpoint Assessments among Patients with Ischemic and Hemorrhagic Stroke (REINAH; US) database were analyzed. MRI and clinical data were collected upon acute care hospital admission. WMHs were measured automatically using the nnU-Net methodology, taking into account the acute stroke lesion.ResultsNo significant difference in WMH percentage was found between sites. Factors associated with higher WMH burden included only age in NO, while in US, very high age (≥ 85), smoking, and being underweight were key factors. The two sites showed significant differences in demographics and clinical characteristics: the US cohort exhibited greater racial heterogeneity, higher body mass index (BMI) with more extremely obese patients, higher National Institutes of Health Stroke Scale (NIHSS) scores, and more thrombectomies, whereas the NO cohort exhibited more tobacco use, hypercholesterolemia, and longer stay at the hospital. Post-stroke dependency was initially associated with higher WMH percentage overall but only remained significant after adjusment in Norwegians aged ≥85, while in the US, dependency was driven by stroke severity and treatment after adjustment.ConclusionCohorts from the US and Norway exhibit no significant difference in WMH burden, but differ in the factors associated with WMHs.

Xiaohang Liu, Pengcheng Ma, Xuan Liu et al.

Implant-associated infections (IAIs) have become a major challenge in clinical orthopedics due to the formation of bacterial biofilms and their complex resistance mechanisms. This review systematically summarizes the resistance mechanisms of bacterial biofilms on the surface of orthopedic implants and critically analyzes the research progress of novel anti-biofilm coatings. Novel antibiofilm coating strategies have shown a diversified development, which are mainly classified into: antimicrobial drug-releasing strategies, surface physicochemical modification strategies, nanotechnology-based antimicrobial strategies, and emerging bioactive strategies. Studies have shown that it is difficult to balance long-lasting antimicrobial activity and biocompatibility with a single strategy, and there is a need to develop multi-mechanism synergistic coatings (e.g., anti-adhesion, contact-killing, immune-modulatory) and to optimize the coating design by combining with artificial intelligence. Despite the potential of nanotechnology and bioactive strategies, their biosafety assessment, scale-up and long-term in vivo efficacy still need to be thoroughly investigated. This review provides an interdisciplinary perspective and theoretical basis for revealing the nature of biofilm resistance and developing efficient strategies for the prevention and treatment of IAIs.

Qi Zhang, Bidusha Neupane, Priyanka Patel et al.

Abstract Objective: To assess the feasibility of using large language models (LLM) to develop research questions about changes to the Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) food packages. Design: We conducted a controlled experiment using ChatGPT-4 and its plugin, MixerBox Scholarly, to generate research questions based on a section of the U.S. Department of Agriculture (USDA) summary of the final public comments on the WIC revision. Five questions weekly for 3 weeks were generated using LLM under two conditions: fed with or without relevant literature. The experiment generated ninety questions, which were evaluated using the Feasibility, Innovation, Novelty, Ethics and Relevance criteria. t tests and multivariate regression examined the difference by feeding status, artificial intelligence model, evaluator and criterion. Setting: The United States. Participants: Six WIC expert evaluators from academia, government, industry and non-profit sectors. Results: Five themes were identified: administrative barriers, nutrition outcomes, participant preferences, economics and other topics. Feeding and non-feeding groups had no significant differences (Coeff. = 0·03, P = 0·52). MixerBox-generated questions received significantly lower scores than ChatGPT (Coeff. = –0·11, P = 0·02). Ethics scores were significantly higher than feasibility scores (Coeff. = 0·65, P < 0·001). Significant differences were found between the evaluators (P < 0·001). Conclusions: The LLM applications can assist in developing research questions with acceptable qualities related to the WIC food package revisions. Future research is needed to compare the development of research questions between LLM and human researchers.

James Babcock, János Kramár, Roman Yampolskiy

There is considerable uncertainty about what properties, capabilities and motivations future AGIs will have. In some plausible scenarios, AGIs may pose security risks arising from accidents and defects. In order to mitigate these risks, prudent early AGI research teams will perform significant testing on their creations before use. Unfortunately, if an AGI has human-level or greater intelligence, testing itself may not be safe; some natural AGI goal systems create emergent incentives for AGIs to tamper with their test environments, make copies of themselves on the internet, or convince developers and operators to do dangerous things. In this paper, we survey the AGI containment problem – the question of how to build a container in which tests can be conducted safely and reliably, even on AGIs with unknown motivations and capabilities that could be dangerous. We identify requirements for AGI containers, available mechanisms, and weaknesses that need to be addressed.

Saydirasulov Norkobil Saydirasulovich, Sabina Umirzakova, Abduazizov Nabijon Azamatovich et al.

Agricultural fires significantly threaten global food systems, ecosystems, and rural economies, necessitating timely detection to prevent widespread damage. This study presents a lightweight and enhanced version of the YOLOv5s model, optimized for early-stage agricultural fire detection. The core innovation involves deepening the C3 block and integrating DarknetBottleneck modules to extract finer visual features from subtle fire indicators such as light smoke and small flames. Experimental evaluations were conducted on a custom dataset of 3200 annotated agricultural fire images. The proposed model achieved a precision of 88.9%, a recall of 85.7%, and a mean Average Precision (mAP) of 87.3%, outperforming baseline YOLOv5s and several state-of-the-art (SOTA) detectors such as YOLOv7-tiny and YOLOv8n. The model maintains a compact size (7.5 M parameters) and real-time capability (74 FPS), making it suitable for resource-constrained deployment. Our findings demonstrate that focused architectural refinement can significantly improve early fire detection accuracy, enabling more effective response strategies and reducing agricultural losses.

Soumyadeep Ray, Pronaya Bhattacharya, Ebrahim A. Mattar et al.

This survey examines the convergence of Explainable Artificial Intelligence (XAI) and Quantum Computing (QC) toward precision medicine. We review developments from 2018 to 2025, summarizing quantum algorithms, quantum-machine-learning models and XAI techniques applied to drug discovery, disease diagnosis, patient monitoring and biomarker identification. We introduce a taxonomy of hybrid and quantum-explainable approaches, evaluate NISQ hardware and encoding constraints, and compare interpretability methods (SHAP, LIME, QSHAP, QLRP, TSBA). Two case studies (doxorubicin cardiotoxicity prediction and pre-symptomatic IBD flare forecasting) demonstrate hybrid variational-quantum pipelines wrapped with SHAP-based explanations. We identify practical barriers (noise, data encoding, regulation, privacy) and outline research directions to benchmark clinical quantum advantage and develop scalable, transparent QXAI frameworks. The survey aims to guide interdisciplinary efforts toward trustworthy, scalable quantum-enabled precision healthcare.

Arın CELAYIR, Musa Batuhan YOLCU, Bedri KARAISMAILOĞLU et al.

Since the development of ChatGPT, many areas are explored about its use and its potentials. Medicine in general and especially medical education is one of these promising areas. This study aims to investigate the capabilities of artificial intelligence technologies, focusing specifically on ChatGPT and emerging text-to-video features, in the development of educational materials for orthopedic medicine. The study is structured into steps, where the first focus is on their application in generating content related to shoulder examination techniques, evaluating the accuracy and effectiveness of these artificial intelligence–generated materials. The primary research question examines whether artificial intelligence–generated educational tools can serve as reliable and accessible resources for these purpose. ChatGPT and other artificial intelligence tools were utilized with simple prompts to generate detailed descriptions of shoulder examination techniques. These texts were reviewed by orthopedic specialists for accuracy. Visual aids based on the artificial intelligence–generated content were created and assessed for anatomical correctness. Additionally, these visuals were converted into short videos using the Vidful.ai platform to assess the feasibility and effectiveness of incorporating dynamic content into medical education. The artificial intelligence–generated texts were comprehensive and showed promise as educational materials. However, the visuals derived from these texts exhibited deficiencies in anatomical accuracy. The attempt to transform these visuals into short videos using Vidful.ai demonstrated limited success, highlighting challenges in producing dynamic and precise content from artificial intelligence–generated visuals. Artificial intelligence–supported tools can offer an accessible and innovative approach to medical education but require expert oversight to ensure content accuracy and effectiveness. This study suggests that leveraging broader and higher-quality datasets could enhance the quality of artificial intelligence–generated educational materials. As artificial intelligence technologies continue to evolve, their role in medical education and patient information dissemination is expected to expand, potentially establishing them as effective and widely adopted tools. Cite this article as: Celayir, A., Yolcu, M. B., Karaismailoğlu, B., R., & Celayir, S. (2025). First experiences in creating orthopedic  medical education content using ChatGPT and similar AI tools. HAYEF: Journal of Education, 22, 0005, doi:10.5152/ hayef.2025.25005.