Results 261 to 270 of about 33,125 (301)

Unsupervised risk factor identification across cancer types and data modalities via explainable artificial intelligence. [PDF]

NPJ Digit Med
Ferle M, Ader J, Wiemers T, Grieb N, Berneck B, Lindenmeyer A, Goldschmidt H, Mai EK, Bertsch U, Meyer HJ, Neumuth T, Kreuz M, Reiche K, Merz M.   +13 more
europepmc   +1 more source

KidneyTox_v1.0 enables explainable artificial intelligence prediction of nephrotoxicity in small molecules. [PDF]

Sci Rep
Amin SA, Kar S, Piotto S.
europepmc   +1 more source

Trustworthy artificial intelligence in predictive medicine: cancer survival analysis using ethical-by-design and explainable artificial intelligence models. [PDF]

JAMIA Open
Farahani S, Siddiqui IA, Taheri A, Fox C, Einhorn A, Helman S, Mathew J, Harshman K, Myers N, Koleck TA, Weiss KR, Lohse I, Tafti AP.   +12 more
europepmc   +1 more source

Explainable artificial intelligence

American Journal of Orthodontics and Dentofacial Orthopedics
The increasing deployment of complex, learning-based artificial intelligence systems has heightened concerns regarding transparency, accountability, and trust, as improvements in predictive performance often come at the expense of interpretability. This chapter provides a structured, non-technical introduction to explainable artificial intelligence ...
Axel-Jan Rousseau, Melvin Geubbelmans, Dirk Valkenborg, Tomasz Burzykowski   +3 more
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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

Principles of Explainable Artificial Intelligence

2021
The last decade has witnessed the rise of a black box society where obscure classification models are adopted by Artificial Intelligence systems (AI). The lack of explanations of how AI systems make decisions is a key ethical issue to their adoption in socially sensitive and safety-critical contexts.
Guidotti, R., Monreale, A., Pedreschi, Dino, Giannotti, Fosca   +3 more
openaire   +2 more sources

Explainable Artificial Intelligence: An Updated Perspective

2022 45th Jubilee International Convention on Information, Communication and Electronic Technology (MIPRO), 2022
Artificial intelligence has become mainstream and its applications will only proliferate. Specific measures must be done to integrate such systems into society for the general benefit. One of the tools for improving that is explainability which boosts trust and understanding of decisions between humans and machines.
Agneza Krajna, Mihael Kovac, Mario Brcic, Ana Sarcevic   +3 more
openaire   +1 more source

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.
Vanessa Keppeler, Matthias Lederer, Ulli Alexander Leucht   +2 more
openaire   +2 more sources

Explainable Artificial Intelligence for Cybersecurity

Computers and Electrical Engineering, 2022
Deepak Kumar Sharma, Jahanavi Mishra, Aeshit Singh, Raghav Govil, Gautam Srivastava 0001, Jerry Chun-Wei Lin   +5 more
openaire   +1 more source