AIMC Topic: Artificial Intelligence

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Imagine there is no paperwork… it's easy if you try.

The British journal of radiology
Artificial Intelligence (AI) applied to radiology is so vast that it provides applications ranging from becoming a complete replacement for radiologists (a potential threat) to an efficient paperwork-saving time assistant (an evident strength). Nowad...

Assessing radiologists' and radiographers' perceptions on artificial intelligence integration: opportunities and challenges.

The British journal of radiology
OBJECTIVES: The objective of this study was to evaluate radiologists' and radiographers' opinions and perspectives on artificial intelligence (AI) and its integration into the radiology department. Additionally, we investigated the most common challe...

PCAO2: an ontology for integration of prostate cancer associated genotypic, phenotypic and lifestyle data.

Briefings in bioinformatics
Disease ontologies facilitate the semantic organization and representation of domain-specific knowledge. In the case of prostate cancer (PCa), large volumes of research results and clinical data have been accumulated and needed to be standardized for...

Artificial intelligence-based automated preprocessing and classification of impacted maxillary canines in panoramic radiographs.

Dento maxillo facial radiology
OBJECTIVES: Automating the digital workflow for diagnosing impacted canines using panoramic radiographs (PRs) is challenging. This study explored feature extraction, automated cropping, and classification of impacted and nonimpacted canines as a firs...

The performance of artificial intelligence chatbot large language models to address skeletal biology and bone health queries.

Journal of bone and mineral research : the official journal of the American Society for Bone and Mineral Research
Artificial intelligence (AI) chatbots utilizing large language models (LLMs) have recently garnered significant interest due to their ability to generate humanlike responses to user inquiries in an interactive dialog format. While these models are be...

[Preliminary Study on the Identification of Aerobic Vaginitis by Artificial Intelligence Analysis System].

Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
OBJECTIVE: To develop an artificial intelligence vaginal secretion analysis system based on deep learning and to evaluate the accuracy of automated microscopy in the clinical diagnosis of aerobic vaginitis (AV).

Explainable artificial intelligence model for mortality risk prediction in the intensive care unit: a derivation and validation study.

Postgraduate medical journal
BACKGROUND: The lack of transparency is a prevalent issue among the current machine-learning (ML) algorithms utilized for predicting mortality risk. Herein, we aimed to improve transparency by utilizing the latest ML explicable technology, SHapley Ad...