AIMC Topic: Reproducibility of Results

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Machine learning-driven prediction of eye irritation toxicity: Integration of in silico and in vitro study.

Toxicology and applied pharmacology
Eye irritation (EI) toxicity poses critical challenges in chemical safety assessment, demanding alternatives to ethically contentious animal testing. We present the first integrative framework combining computational prediction with experimental vali...

Automated quantitative analysis of peri-articular bone microarchitecture in HR-pQCT knee images.

Computer methods and programs in biomedicine
UNLABELLED: Applying HR-pQCT to image the knee necessitates the development and validation of novel image analysis workflows. Here, we present and validate the first automated workflow for in vivo quantitative assessment of peri-articular bone densit...

Evaluation of uncertainty estimation methods in medical image segmentation: Exploring the usage of uncertainty in clinical deployment.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Uncertainty estimation methods are essential for the application of artificial intelligence (AI) models in medical image segmentation, particularly in addressing reliability and feasibility challenges in clinical deployment. Despite their significanc...

Evaluating dental AI research papers: Key considerations for editors and reviewers.

Journal of dentistry
OBJECTIVE: Artificial intelligence (AI) is increasingly used in dental research for diagnosis, treatment planning, and disease prediction. However, many dental AI studies lack methodological rigor, transparency, or reproducibility, and no dedicated p...

Mitigating patient harm risks: A proposal of requirements for AI in healthcare.

Artificial intelligence in medicine
With the rise Artificial Intelligence (AI), mitigation strategies may be needed to integrate AI-enabled medical software responsibly, ensuring ethical alignment and patient safety. This study examines how to mitigate the key risks identified by the E...

Improving brain tumor diagnosis: A self-calibrated 1D residual network with random forest integration.

Brain research
Medical specialists need to perform precise MRI analysis for accurate diagnosis of brain tumors. Current research has developed multiple artificial intelligence (AI) techniques for the process automation of brain tumor identification. However, existi...