AIMC Topic: Artificial Intelligence

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PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Nature genetics
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals...

Hit Identification Driven by Combining Artificial Intelligence and Computational Chemistry Methods: A PI5P4K-β Case Study.

Journal of chemical information and modeling
Computer-aided drug design (CADD), especially artificial intelligence-driven drug design (AIDD), is increasingly used in drug discovery. In this paper, a novel and efficient workflow for hit identification was developed within the drug discovery pla...

Thinking about God increases acceptance of artificial intelligence in decision-making.

Proceedings of the National Academy of Sciences of the United States of America
Thinking about God promotes greater acceptance of Artificial intelligence (AI)-based recommendations. Eight preregistered experiments ( = 2,462) reveal that when God is salient, people are more willing to consider AI-based recommendations than when G...

Multitasking via baseline control in recurrent neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Changes in behavioral state, such as arousal and movements, strongly affect neural activity in sensory areas, and can be modeled as long-range projections regulating the mean and variance of baseline input currents. What are the computational benefit...

Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review.

Journal of Zhejiang University. Science. B
Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has not been systematically assessed. This scoping review was conducted to outline the development of AI...

Optimized Classifier Learning for Face Recognition Performance Boost in Security and Surveillance Applications.

Sensors (Basel, Switzerland)
Face recognition has become an integral part of modern security processes. This paper introduces an optimization approach for the quantile interval method (QIM), a promising classifier learning technique used in face recognition to create face templa...

Unleashing the potential of AI for pathology: challenges and recommendations.

The Journal of pathology
Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potent...

Explanations as a New Metric for Feature Selection: A Systematic Approach.

IEEE journal of biomedical and health informatics
With the extensive use of Machine Learning (ML) in the biomedical field, there was an increasing need for Explainable Artificial Intelligence (XAI) to improve transparency and reveal complex hidden relationships between variables for medical practiti...

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

IEEE journal of biomedical and health informatics
Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mainly rely on the extraction of image features but ignore the use of clinical text information of pati...

Characterization of Synthetic Health Data Using Rule-Based Artificial Intelligence Models.

IEEE journal of biomedical and health informatics
The aim of this study is to apply and characterize eXplainable AI (XAI) to assess the quality of synthetic health data generated using a data augmentation algorithm. In this exploratory study, several synthetic datasets are generated using various co...