AIMC Topic: Databases, Factual

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Clinical science and practice in the age of large language models and generative artificial intelligence.

Journal of consulting and clinical psychology
In this article, Schueller and Morris discuss the recent advances made from large language models (LLMs) and generative artificial intelligence (AI). These advances include supporting humans to provide better interventions, understanding processes in...

Current status and progress of laparoscopic inguinal hernia repair: A review.

Medicine
After 30 years of development, laparoscopic inguinal hernia repair (LIHR) has become the main method for treating adult inguinal hernia. LIHR is more standardized, the approach of single-port laparoscopic hernioplasty, the advantages of robotic ingui...

Deep learning networks in the segmentation of the left atrial appendage in 2D ultrasound: A comparative analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Left atrial appendage (LAA) is the major source of thromboembolism in patients with non-valvular atrial fibrillation. Currently, LAA occlusion can be offered as a treatment for these patients, obstructing the LAA through a percutaneously delivered de...

Exploring artificial intelligence from a clinical perspective: A comparison and application analysis of two facial age predictors trained on a large-scale Chinese cosmetic patient database.

Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI)
BACKGROUND: Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age-estimation models are trained on public datasets with the Caucasian race as t...

Symbiosis of an artificial neural network and models of biological neurons: Training and testing.

Chaos (Woodbury, N.Y.)
In this paper, we show the possibility of creating and identifying the features of an artificial neural network (ANN), which consists of mathematical models of biological neurons. The FitzHugh-Nagumo (FHN) system is used as a paradigmatic model demon...

Deep Learning in Colorectal Cancer Classification: A Scoping Review.

Studies in health technology and informatics
Colorectal cancer (CRC) is one of the most common cancers worldwide, and its diagnosis and classification remain challenging for pathologists and imaging specialists. The use of artificial intelligence (AI) technology, specifically deep learning, has...

Increasing Trust in AI Using Explainable Artificial Intelligence for Histopathology - An Overview.

Studies in health technology and informatics
Digital Pathology is an area that could benefit a lot from the automatic classification of scanned microscopic slides. One of the main problems with this is that the experts need to understand and trust the decisions of the system. This paper is an o...

[Brief analysis of research and application status of artificial intelligence and related advanced technology in oral medicine].

Zhonghua kou qiang yi xue za zhi = Zhonghua kouqiang yixue zazhi = Chinese journal of stomatology
Artificial intelligence revealed its application prospects that could bring change in oral medicine. Artificial intelligence related papers in oral medicine field increased year by year since the 1990s. In order to provide reference for further resea...

Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers.

Briefings in bioinformatics
PURPOSE: Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-...

Profiling mechanisms that drive acute oral toxicity in mammals and its prediction via machine learning.

Toxicological sciences : an official journal of the Society of Toxicology
We present a mechanistic machine-learning quantitative structure-activity relationship (QSAR) model to predict mammalian acute oral toxicity. We trained our model using a rat acute toxicity database compiled by the US National Toxicology Program. We ...