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A Review on Explainable Artificial Intelligence for Healthcare: Why,
  How, and When?

A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?

IEEE Transactions on Artificial Intelligence (IEEE TAI), 2023
10 April 2023
M. Rubaiyat
Hossain Mondal
Prajoy Podder
ArXiv (abs)PDFHTML

Papers citing "A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?"

13 / 13 papers shown
Taming Identity Consistency and Prompt Diversity in Diffusion Models via Latent Concatenation and Masked Conditional Flow Matching
Taming Identity Consistency and Prompt Diversity in Diffusion Models via Latent Concatenation and Masked Conditional Flow MatchingIEEE Transactions on Artificial Intelligence (IEEE TAI), 2024
Aditi Singhania
Arushi Jain
Krutik Malani
Riddhi Dhawan
Souymodip Chakraborty
Vineet Batra
Ankit Phogat
DiffM
99
8
0
11 Nov 2025
Governable AI: Provable Safety Under Extreme Threat Models
Governable AI: Provable Safety Under Extreme Threat Models
Donglin Wang
Weiyun Liang
Chunyuan Chen
Jing Xu
Yulong Fu
66
1
0
28 Aug 2025
ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
ITL-LIME: Instance-Based Transfer Learning for Enhancing Local Explanations in Low-Resource Data Settings
Rehan Raza
Guanjin Wang
Kevin Wong
Hamid Laga
Marco Fisichella
FAttLRM
267
1
0
19 Aug 2025
Intercept Cancer: Cancer Pre-Screening with Large Scale Healthcare Foundation Models
Intercept Cancer: Cancer Pre-Screening with Large Scale Healthcare Foundation Models
Liwen Sun
Hao-Ren Yao
Gary Gao
O. Frieder
Chenyan Xiong
171
1
0
30 May 2025
Soft-CAM: Making black box models self-explainable for high-stakes decisions
K. Djoumessi
Philipp Berens
FAttBDL
405
1
0
23 May 2025
Investigating the Duality of Interpretability and Explainability in Machine Learning
Investigating the Duality of Interpretability and Explainability in Machine LearningIEEE International Conference on Tools with Artificial Intelligence (ICTAI), 2024
Moncef Garouani
Josiane Mothe
Ayah Barhrhouj
Julien Aligon
AAML
320
11
0
27 Mar 2025
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity Quantification
CoE: Chain-of-Explanation via Automatic Visual Concept Circuit Description and Polysemanticity QuantificationComputer Vision and Pattern Recognition (CVPR), 2025
Wenlong Yu
Qilong Wang
Chuang Liu
Dong Li
Q. Hu
LRM
288
2
0
19 Mar 2025
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
Explainable Artificial Intelligence: A Survey of Needs, Techniques, Applications, and Future Direction
Melkamu Mersha
Khang Lam
Joseph Wood
Ali AlShami
Jugal Kalita
XAIAI4TS
711
94
0
30 Aug 2024
Interpretable Machine Learning for Survival Analysis
Interpretable Machine Learning for Survival Analysis
Sophie Hanna Langbein
Mateusz Krzyzinski
Mikolaj Spytek
Hubert Baniecki
P. Biecek
Marvin N. Wright
284
7
0
15 Mar 2024
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
QUCE: The Minimisation and Quantification of Path-Based Uncertainty for Generative Counterfactual Explanations
Jamie Duell
Monika Seisenberger
Hsuan Fu
Xiuyi Fan
BDLUQCV
504
1
0
27 Feb 2024
The Significance of Machine Learning in Clinical Disease Diagnosis: A
  Review
The Significance of Machine Learning in Clinical Disease Diagnosis: A ReviewInternational Journal of Computer Applications (IJCA), 2023
S. M. A. Rahman
Sifat Ibtisum
Ehsan Bazgir
Tumpa Barai
LM&MA
179
26
0
25 Oct 2023
SurvLIME: A method for explaining machine learning survival models
SurvLIME: A method for explaining machine learning survival modelsKnowledge-Based Systems (KBS), 2020
M. Kovalev
Lev V. Utkin
E. Kasimov
443
102
0
18 Mar 2020
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
2.5K
19,805
0
16 Feb 2016
1
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