AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Software

Showing 201 to 210 of 3426 articles

Clear Filters

Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.

Nature communications
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neur...

Automatic generation of diffusion tensor imaging for the lumbar nerve using convolutional neural networks.

Magnetic resonance imaging
【PURPOSE】: Diffusion Tensor Imaging (DTI) with tractography is useful for the functional diagnosis of degenerative lumbar disorders. However, it is not widely used in clinical settings due to time and health care provider costs, as it is performed ma...

Exploring the Impact of GitHub Copilot on Health Informatics Education.

Applied clinical informatics
BACKGROUND:  The use of artificial intelligence-driven code completion tools, particularly the integration of GitHub Copilot with Visual Studio, has potential implications for Health Informatics education, particularly for students learning SQL and P...

SpeechBrain-MOABB: An open-source Python library for benchmarking deep neural networks applied to EEG signals.

Computers in biology and medicine
Deep learning has revolutionized EEG decoding, showcasing its ability to outperform traditional machine learning models. However, unlike other fields, EEG decoding lacks comprehensive open-source libraries dedicated to neural networks. Existing tools...

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Human genomics
Single nucleotide variants (SNVs) can exert substantial and extremely variable impacts on various cellular functions, making accurate predictions of their consequences challenging, albeit crucial especially in clinical settings such as in oncology. L...

Application of Machine Learning in a Rodent Malaria Model for Rapid, Accurate, and Consistent Parasite Counts.

The American journal of tropical medicine and hygiene
Rodent malaria models serve as important preclinical antimalarial and vaccine testing tools. Evaluating treatment outcomes in these models often requires manually counting parasite-infected red blood cells (iRBCs), a time-consuming process, which can...

Barriers and facilitators to implementing imaging-based diagnostic artificial intelligence-assisted decision-making software in hospitals in China: a qualitative study using the updated Consolidated Framework for Implementation Research.

BMJ open
OBJECTIVES: To identify the barriers and facilitators to the successful implementation of imaging-based diagnostic artificial intelligence (AI)-assisted decision-making software in China, using the updated Consolidated Framework for Implementation Re...

6G in medical robotics: development of network allocation strategies for a telerobotic examination system.

International journal of computer assisted radiology and surgery
PURPOSE: Healthcare systems around the world are increasingly facing severe challenges due to problems such as staff shortage, changing demographics and the reliance on an often strongly human-dependent environment. One approach aiming to address the...

DeepPhoPred: Accurate Deep Learning Model to Predict Microbial Phosphorylation.

Proteins
Phosphorylation is a substantial posttranslational modification of proteins that refers to adding a phosphate group to the amino acid side chain after translation process in the ribosome. It is vital to coordinate cellular functions, such as regulati...

Deep Learning Based Shear Wave Detection and Segmentation Tool for Use in Point-of-Care for Chronic Liver Disease Assessments.

Ultrasound in medicine & biology
OBJECTIVE: As metabolic dysfunction-associated steatotic liver disease (MASLD) becomes more prevalent worldwide, it is imperative to create more accurate technologies that make it easy to assess the liver in a point-of-care setting. The aim of this s...