AIMC Topic: Reproducibility of Results

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Artificial intelligence in maxillofacial trauma: expert ally or unreliable assistant?

Medicina oral, patologia oral y cirugia bucal
BACKGROUND: Large language models (LLMs), such as ChatGPT, have demonstrated potential in synthesizing complex clinical information, yet concerns persist regarding their accuracy and reliability in specialized domains. The rationale of this study is ...

Development of the generative artificial intelligence awareness scale for secondary school students in Türkiye.

European journal of pediatrics
The widespread adoption of generative artificial intelligence (AI) in education and daily life necessitates a deeper understanding of students' awareness and attitudes. However, there is a lack of appropriate and psychometrically validated tools to a...

Evaluation of deep learning models using explainable AI with qualitative and quantitative analysis for rice leaf disease detection.

Scientific reports
Deep learning models have shown remarkable success in disease detection and classification tasks, but lack transparency in their decision-making process, creating reliability and trust issues. Although traditional evaluation methods focus entirely on...

Development of a Large-Scale Dataset of Chest Computed Tomography Reports in Japanese and a High-Performance Finding Classification Model: Dataset Development and Validation Study.

JMIR medical informatics
BACKGROUND: Recent advances in large language models have highlighted the need for high-quality multilingual medical datasets. Although Japan is a global leader in computed tomography (CT) scanner deployment and use, the absence of large-scale Japane...

Exploring the risks of over-reliance on AI in diagnostic pathology. What lessons can be learned to support the training of young pathologists?

PloS one
The integration of Artificial Intelligence (AI) algorithms into pathology practice presents both opportunities and challenges. Although it can improve accuracy and inter-rater reliability, it is not infallible and can produce erroneous diagnoses, hen...

Transforming Patient Feedback Into Actionable Insights Through Natural Language Processing: Knowledge Discovery and Action Research Study.

JMIR formative research
BACKGROUND: Patient feedback has emerged as a critical measure of health care quality and a key driver of organizational performance. Traditional manual analysis of unstructured patient feedback presents significant challenges as data volumes grow, m...

Optimizing meningioma grading with radiomics and deep features integration, attention mechanisms, and reproducibility analysis.

European journal of medical research
OBJECTIVE: This study aims to develop a robust and clinically applicable framework for preoperative grading of meningiomas using T1-contrast-enhanced and T2-weighted MRI images. The approach integrates radiomic feature extraction, attention-guided de...

Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT.

Stroke and vascular neurology
BACKGROUND: CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessa...

Towards expert-level autonomous carotid ultrasonography with large-scale learning-based robotic system.

Nature communications
Carotid ultrasound requires skilled operators due to small vessel dimensions and high anatomical variability, exacerbating sonographer shortages and diagnostic inconsistencies. Prior automation attempts, including rule-based approaches with manual he...

Enhanced residual-attention deep neural network for disease classification in maize leaf images.

Scientific reports
Disease classification in maize plant is necessary for immediate treatment to enhance agricultural production and assure global food sustainability. Recent advancements in deep learning, specifically convolutional neural networks, have shown outstand...