Results 171 to 180 of about 20,583 (298)

Explainable Artificial Intelligence in Dentistry: A Systematic Review of Its Trust and Translation. [PDF]

Int Dent J
Thaweesapphithak S, Thongchotchat V, Alinejad-Rokny H, Samaranayake L, Osathanon T, Porntaveetus T.   +5 more
europepmc   +1 more source

L‐VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts

Computer Graphics Forum, EarlyView.
L‐VISP is a human‐machine solution that uses visual analytics for LSTM modelling in clinical research. L‐VISP uses custom visual encodings to make multiple LSTM variants interpretable, supporting a full range of analysis, from understanding model operations and evaluating performance to interpreting results in a clinical context.
C. Floricel, Y. Wang, A. Wentzel, C. D. Fuller, G. E. Marai, M. E. Papka, G. Canahuate   +6 more
wiley   +1 more source

Interpretable graph-based models on multimodal biomedical data integration: a technical review and benchmarking. [PDF]

Nat Commun
Sadeghi A, Hajati F, Argha A, H Lovell N, Yang M, Alinejad-Rokny H.   +5 more
europepmc   +1 more source

ReTrace: Interactive Visualizations for Reasoning Traces of Large Reasoning Models

Computer Graphics Forum, EarlyView.
Abstract Recent advances in Large Language Models have led to Large Reasoning Models, which produce step‐by‐step reasoning traces. Such traces may offer insight into how models think, improving explainability and clarifying the underlying process. These traces, however, are often verbose and complex, making them cognitively demanding to comprehend ...
L. Felder, J. Miller, M. Wallinger, S. G. Kobourov, C. Chen   +4 more
wiley   +1 more source

Explainable and Interpretable AI for Voice and Speech Analysis in Clinical Care: Systematic Review. [PDF]

J Med Internet Res
Ebraheem M, Toghranegar J, Bridge2AI-Voice Consortium, Bensoussan Y, Templeton JM.   +4 more
europepmc   +1 more source

Visualizing Image Segmentation Network Behavior Through the Lens of Scale Space Analysis

Computer Graphics Forum, EarlyView.
Abstract Deep neural networks are widely used for image segmentation, also in sensitive applications such as medical imaging or autonomous driving. However, few explainable AI methods are available that help developers understand such networks beyond classification.
A. C. Mikliss, T. Schultz
wiley   +1 more source

SPINE: VAE‐driven Counterfactuals for Decision Boundary Maps

Computer Graphics Forum, EarlyView.
Abstract As Deep Learning models become increasingly complex, Explainable AI becomes essential for deploying machine learning classifiers. Decision Boundary Mapping (DBM) is a technique for visualizing a classifier's global decision boundary. Despite their relative success, current DBM methods rely on global inverse multidimensional projections that ...
I.M. Bloemen, V. Prasad, F. V. Paulovich
wiley   +1 more source

Secure healthcare data management using federated learning, blockchain, and explainable artificial intelligence: a systematic review. [PDF]

Front Digit Health
Bhardwaj T, Sumangali K.
europepmc   +1 more source

Deep Learning Models for Detection of Periapical Radiolucent Lesions on Panoramic Radiographs: A Systematic Review and Meta‐Analysis

International Endodontic Journal, EarlyView.
ABSTRACT Background Panoramic radiographs are used routinely to screen dental conditions and treatment patterns. Recently, numerous studies have suggested that deep learning (DL) models can be utilized for analysing panoramic radiographs. Objective This review aimed to evaluate the accuracy of DL models in detecting periapical radiolucent lesions (PRLs)
Ibrahim Ali Ahmad, Raidan Ba‐Hattab, Sadeq Ali Al‐Maweri, Faleh Tamimi   +3 more
wiley   +1 more source

Explainable AI in hospital clinical decision support systems: A scoping review of healthcare professionals' perspectives. [PDF]

PLOS Digit Health
Van Dort BA, Engelsma T, Sneekes P, Peute L, Medlock S.   +4 more
europepmc   +1 more source