AI Medical Compendium Topic

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

Mental Recall

Showing 1 to 10 of 72 articles

Clear Filters

A Computational Framework for Memory Engrams.

Advances in neurobiology
Memory engrams in mice brains are potentially related to groups of concept cells in human brains. A single concept cell in human hippocampus responds, for example, not only to different images of the same object or person but also to its name written...

Research on helmet wearing detection method based on deep learning.

Scientific reports
The vigorous development of the construction industry has also brought unprecedented safety risks. The wearing of safety helmets at the construction site can effectively reduce casualties. As a result, this paper suggests employing a deep learning-ba...

Automated detection and recognition system for chewable food items using advanced deep learning models.

Scientific reports
Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cu...

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting.

Scientific reports
Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during...

Comparing human evaluations of eyewitness statements to a machine learning classifier under pristine and suboptimal lineup administration procedures.

Cognition
Recent work highlights the ability of verbal machine learning classifiers to distinguish between accurate and inaccurate recognition memory decisions (Dobbins, 2022; Dobbins & Kantner, 2019; Seale-Carlisle, Grabman, & Dodson, 2022). Given the surge o...

Improving the diagnostic value of lineup rejections.

Cognition
Erroneous eyewitness identification evidence is likely the leading cause of wrongful convictions. To minimize this error, scientists recommend collecting confidence. Research shows that eyewitness confidence and accuracy are strongly related when an ...

Can AI-generated faces serve as fillers in eyewitness lineups?

Memory (Hove, England)
To create a photo lineup for an eyewitness, police embed the suspect in a group of similar-looking individuals (i.e., fillers). If the witness selects the suspect from these photos of similar-looking people, then this provides evidence they remember ...

Improving Recall in Sparse Associative Memories That Use Neurogenesis.

Neural computation
The creation of future low-power neuromorphic solutions requires specialist spiking neural network (SNN) algorithms that are optimized for neuromorphic settings. One such algorithmic challenge is the ability to recall learned patterns from their nois...

Assessing autobiographical memory consistency: Machine and human approaches.

Behavior research methods
Memory is far from a stable representation of what we have encountered. Over time, we can forget, modify, and distort the details of our experiences. How autobiographical memory-the memories we have for our personal past-changes has important ramific...

AI assistance improves people's ability to distinguish correct from incorrect eyewitness lineup identifications.

Proceedings of the National Academy of Sciences of the United States of America
Mistaken eyewitness identification is one of the leading causes of false convictions. Improving law enforcement's ability to identify correct identifications could have profound implications for criminal justice. Across two experiments, we show that ...