Results 211 to 220 of about 73,912 (246)

Recent Advances in Variable‐Stiffness Robotic Systems Enabled by Phase‐Change Materials

Advanced Robotics Research, EarlyView.
Phase‐change materials (PCMs), such as shape memory alloys, hydrogels, shape memory polymers, liquid crystal elastomers, and low‐melting‐point alloys, are driving advancements in stiffness‐tunable robotic systems across a wide range of applications. This review highlights recent progress in PCM‐enabled robotics, focusing on their underlying mechanisms,
Sukrit Gaira, Liao Wu, Ming Li, Priyank Kumar, Xiaopeng Li, Shi‐Yang Tang   +5 more
wiley   +1 more source

Detecting label noise in longitudinal Alzheimer's data with explainable artificial intelligence. [PDF]

Brain Inform
Sorino P, Lombardi A, Lofù D, Colafiglio T, Ferrara A, Narducci F, Sciascio ED, Di Noia T.   +7 more
europepmc   +1 more source

Explainable artificial intelligence and ensemble learning for hepatocellular carcinoma classification: State of the art, performance, and clinical implications. [PDF]

World J Hepatol
Akbulut S, Colak C.
europepmc   +1 more source

A Comprehensive Review of Explainable Artificial Intelligence (XAI) in Computer Vision. [PDF]

Sensors (Basel)
Cheng Z, Wu Y, Li Y, Cai L, Ihnaini B.
europepmc   +1 more source

Improving the Potential for Predicting Prostate Cancer Progression in Patients on Active Surveillance Using Explainable Artificial Intelligence. [PDF]

Cancers (Basel)
Vershinina O, Sushentsev N, Zaikin A, Blyuss O, Barrett T, Ivanchenko M.   +5 more
europepmc   +1 more source

A systematic review of EEG-based biomarkers for depression, anxiety, and bipolar disorder: trends in explainable artificial intelligence (XAI). [PDF]

BMC Psychiatry
Zhai L, Zhao M, Zhang J, Jamil M, Naz R, Li C.   +5 more
europepmc   +1 more source

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Explainable Artificial Intelligence

2023
The explainability of artificial intelligence (AI) is one of the central challenges for the wider use of the new technology in many industries and applications. The more powerful and efficient the algorithms of AI work, the less it is usually comprehensible to users.
Zhixin Pan, Prabhat Mishra
  +6 more sources

Explainable artificial intelligence in ophthalmology

Current Opinion in Ophthalmology, 2023
Purpose of review Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models.
Ting Fang, Tan, Peilun, Dai, Xiaoman, Zhang, Liyuan, Jin, Stanley, Poh, Dylan, Hong, Joshua, Lim, Gilbert, Lim, Zhen Ling, Teo, Nan, Liu, Daniel Shu Wei, Ting   +10 more
openaire   +2 more sources