AI Medical Compendium Topic

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

Cognition

Showing 61 to 70 of 694 articles

Clear Filters

Comparing effects of wearable robot-assisted gait training on functional changes and neuroplasticity: A preliminary study.

PloS one
Robot-assisted gait training (RAGT) is a promising technique for improving the gait ability of elderly adults and patients with gait disorders by enabling high-intensive and task-specific training. Gait functions involve multiple brain regions and ne...

Modelling and Controlling System Dynamics of the Brain: An Intersection of Machine Learning and Control Theory.

Advances in neurobiology
The human brain, as a complex system, has long captivated multidisciplinary researchers aiming to decode its intricate structure and function. This intricate network has driven scientific pursuits to advance our understanding of cognition, behavior, ...

Moral Association Graph: A Cognitive Model for Automated Moral Inference.

Topics in cognitive science
Automated moral inference is an emerging topic of critical importance in artificial intelligence. The contemporary approach typically relies on language models to infer moral relevance or moral properties of a concept. This approach demands complex p...

Identification and cognitive function prediction of Alzheimer's disease based on multivariate pattern analysis of hippocampal volumes.

Journal of Alzheimer's disease : JAD
BACKGROUND: Alzheimer's disease (AD) is strongly associated with slowly progressive hippocampal atrophy. Elucidating the relationships between local morphometric changes and disease status for early diagnosis could be aided by machine learning algori...

Self-Supervised Learning for Near-Wild Cognitive Workload Estimation.

Journal of medical systems
Feedback on cognitive workload may reduce decision-making mistakes. Machine learning-based models can produce feedback from physiological data such as electroencephalography (EEG) and electrocardiography (ECG). Supervised machine learning requires la...

Neuromorphic engineering: Artificial brains for artificial intelligence.

Annals of the New York Academy of Sciences
Neuromorphic engineering is a research discipline that tries to bridge the gaps between neuroscience and engineering, cognition and algorithms, and natural and artificial intelligence. Neuromorphic engineering promises revolutionary breakthroughs tha...

Moral enhancement and cheapened achievement: Psychedelics, virtual reality and AI.

Bioethics
A prominent critique of cognitive or athletic enhancement claims that certain performance-improving drugs or technologies may 'cheapen' resulting achievements. Considerably less attention has been paid to the impact of enhancement on the value of mor...

Depression diagnosis: EEG-based cognitive biomarkers and machine learning.

Behavioural brain research
Depression is a complex mental illness that has significant effects on people as well as society. The traditional techniques for the diagnosis of depression, along with the potential of nascent biomarkers especially EEG-based biomarkers, are studied....

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Clinical and translational science
Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techni...

Cognitive process and information processing model based on deep learning algorithms.

Neural networks : the official journal of the International Neural Network Society
According to the developmental process of infants, cognitive abilities are divided into four stages: the Exploration Stage (ES), the Mapping Stage (MS), the Phenomena-causality Stage (PCS), and the Essence-causality Stage (ECS). The MS is a training ...