What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

13 May 2019

Papers citing "What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use"

50 / 50 papers shown

Title
Early Detection of Patient Deterioration from Real-Time Wearable Monitoring System Lo Pang-Yun Ting Hong-Pei Chen An-Shan Liu Chun-Yin Yeh Po-Lin Chen Kun-Ta Chuang 26 0 0 02 May 2025
ProtoECGNet: Case-Based Interpretable Deep Learning for Multi-Label ECG Classification with Contrastive Learning Shri Kiran Srinivasan David Chen Thomas Statchen Michael C. Burkhart Nipun Bhandari Bashar Ramadan Brett Beaulieu-Jones 45 1 0 11 Apr 2025
No Black Box Anymore: Demystifying Clinical Predictive Modeling with Temporal-Feature Cross Attention Mechanism Yubo Li Xinyu Yao R. Padman FAtt AI4TS 54 0 0 25 Mar 2025
Self-Explaining Hypergraph Neural Networks for Diagnosis Prediction Leisheng Yu Yanxiao Cai Minxing Zhang Xia Hu FAtt 225 0 0 15 Feb 2025
Controlling for Unobserved Confounding with Large Language Model Classification of Patient Smoking Status Samuel Lee Zach Wood-Doughty CML 50 0 0 05 Nov 2024
Rideshare Transparency: Translating Gig Worker Insights on AI Platform Design to Policy Varun Nagaraj Rao Samantha Dalal Eesha Agarwal D. Calacci Andrés Monroy-Hernández 29 2 0 16 Jun 2024
Towards Optimising EEG Decoding using Post-hoc Explanations and Domain Knowledge Param S. Rajpura Y. Meena 26 0 0 02 May 2024
Designing for Complementarity: A Conceptual Framework to Go Beyond the Current Paradigm of Using XAI in Healthcare Elisa Rubegni Omran Ayoub Stefania Maria Rita Rizzo Marco Barbero G. Bernegger Francesca Faraci Francesca Mangili Emiliano Soldini P. Trimboli Alessandro Facchini 31 1 0 06 Apr 2024
Inadequacy of common stochastic neural networks for reliable clinical decision support Adrian Lindenmeyer Malte Blattmann S. Franke Thomas Neumuth Daniel Schneider BDL 37 1 0 24 Jan 2024
Elucidating Discrepancy in Explanations of Predictive Models Developed using EMR A. Brankovic Wenjie Huang David Cook Sankalp Khanna K. Bialkowski 11 2 0 28 Nov 2023
Predictability and Comprehensibility in Post-Hoc XAI Methods: A User-Centered Analysis Anahid N. Jalali Bernhard Haslhofer Simone Kriglstein Andreas Rauber FAtt 37 4 0 21 Sep 2023
Towards a Comprehensive Human-Centred Evaluation Framework for Explainable AI Ivania Donoso-Guzmán Jeroen Ooge Denis Parra K. Verbert 50 6 0 31 Jul 2023
Evaluation of Popular XAI Applied to Clinical Prediction Models: Can They be Trusted? A. Brankovic David Cook Jessica Rahman Wenjie Huang Sankalp Khanna 31 1 0 21 Jun 2023
Counterfactual Explanations and Predictive Models to Enhance Clinical Decision-Making in Schizophrenia using Digital Phenotyping Juan Sebastián Canas Francisco Gomez Omar Costilla-Reyes 21 1 0 06 Jun 2023
Appraising the Potential Uses and Harms of LLMs for Medical Systematic Reviews Hye Sun Yun Iain J. Marshall T. Trikalinos Byron C. Wallace 26 17 0 19 May 2023
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When? M. Rubaiyat Hossain Mondal Prajoy Podder 28 56 0 10 Apr 2023
Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma T. Chanda Katja Hauser S. Hobelsberger Tabea-Clara Bucher Carina Nogueira Garcia ... J. Utikal K. Ghoreschi S. Fröhling E. Krieghoff-Henning T. Brinker 26 67 0 17 Mar 2023
Informing clinical assessment by contextualizing post-hoc explanations of risk prediction models in type-2 diabetes Shruthi Chari Prasanth Acharya Daniel Gruen Olivia R. Zhang Elif Eyigoz ... Oshani Seneviratne Fernando Jose Suarez Saiz Pablo Meyer Prithwish Chakraborty D. McGuinness 32 17 0 11 Feb 2023
Ignore, Trust, or Negotiate: Understanding Clinician Acceptance of AI-Based Treatment Recommendations in Health Care Venkatesh Sivaraman L. Bukowski J. Levin J. Kahn Adam Perer 40 83 0 31 Jan 2023
Towards Reconciling Usability and Usefulness of Explainable AI Methodologies Pradyumna Tambwekar Matthew C. Gombolay 36 8 0 13 Jan 2023
Context-dependent Explainability and Contestability for Trustworthy Medical Artificial Intelligence: Misclassification Identification of Morbidity Recognition Models in Preterm Infants Isil Guzey Ozlem Ucar N. A. Çiftdemir B. Acunaş 25 1 0 17 Dec 2022
Explainability of Traditional and Deep Learning Models on Longitudinal Healthcare Records L. Cheong Tesfagabir Meharizghi Wynona Black Yang Guang Weilin Meng FAtt AI4TS 8 0 0 22 Nov 2022
Fully-attentive and interpretable: vision and video vision transformers for pain detection Giacomo Fiorentini Itir Onal Ertugrul A. A. Salah MedIm ViT 21 2 0 27 Oct 2022
Trustworthy clinical AI solutions: a unified review of uncertainty quantification in deep learning models for medical image analysis Benjamin Lambert Florence Forbes A. Tucholka Senan Doyle Harmonie Dehaene M. Dojat 34 81 0 05 Oct 2022
"Help Me Help the AI": Understanding How Explainability Can Support Human-AI Interaction Sunnie S. Y. Kim E. A. Watkins Olga Russakovsky Ruth C. Fong Andrés Monroy-Hernández 43 108 0 02 Oct 2022
Explainable Artificial Intelligence Applications in Cyber Security: State-of-the-Art in Research Zhibo Zhang H. A. Hamadi Ernesto Damiani C. Yeun Fatma Taher AAML 34 148 0 31 Aug 2022
Exploring How Anomalous Model Input and Output Alerts Affect Decision-Making in Healthcare Marissa Radensky Dustin Burson Rajya Bhaiya Daniel S. Weld 29 0 0 27 Apr 2022
Example-based Explanations with Adversarial Attacks for Respiratory Sound Analysis Yi Chang Zhao Ren THANH VAN NGUYEN Wolfgang Nejdl Björn W. Schuller AAML 33 14 0 30 Mar 2022
Trust in AI: Interpretability is not necessary or sufficient, while black-box interaction is necessary and sufficient Max W. Shen 27 18 0 10 Feb 2022
Towards a Shapley Value Graph Framework for Medical peer-influence J. Duell M. Seisenberger Gert Aarts Shang-Ming Zhou Xiuyi Fan 21 0 0 29 Dec 2021
Explainable Deep Learning in Healthcare: A Methodological Survey from an Attribution View Di Jin Elena Sergeeva W. Weng Geeticka Chauhan Peter Szolovits OOD 53 55 0 05 Dec 2021
Self-explaining Neural Network with Concept-based Explanations for ICU Mortality Prediction Sayantan Kumar Sean C. Yu Thomas Kannampallil Zachary B. Abrams Andrew Michelson Philip R. O. Payne FAtt 19 7 0 09 Oct 2021
BI-RADS-Net: An Explainable Multitask Learning Approach for Cancer Diagnosis in Breast Ultrasound Images Boyu Zhang Aleksandar Vakanski Min Xian 30 11 0 05 Oct 2021
VBridge: Connecting the Dots Between Features and Data to Explain Healthcare Models Furui Cheng Dongyu Liu F. Du Yanna Lin Alexandra Zytek Haomin Li Huamin Qu K. Veeramachaneni 24 37 0 04 Aug 2021
A Survey on Graph-Based Deep Learning for Computational Histopathology David Ahmedt-Aristizabal M. Armin Simon Denman Clinton Fookes L. Petersson GNN AI4CE 26 108 0 01 Jul 2021
A Review on Explainability in Multimodal Deep Neural Nets Gargi Joshi Rahee Walambe K. Kotecha 29 140 0 17 May 2021
Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction Aniruddh Raghu John Guttag K. Young E. Pomerantsev Adrian Dalca Collin M. Stultz 13 9 0 04 Mar 2021
Towards Personalized Federated Learning A. Tan Han Yu Li-zhen Cui Qiang Yang FedML AI4CE 209 847 0 01 Mar 2021
Intuitively Assessing ML Model Reliability through Example-Based Explanations and Editing Model Inputs Harini Suresh Kathleen M. Lewis John Guttag Arvind Satyanarayan FAtt 42 25 0 17 Feb 2021
Designing AI for Trust and Collaboration in Time-Constrained Medical Decisions: A Sociotechnical Lens Maia L. Jacobs Jeffrey He Melanie F. Pradier Barbara D. Lam Andrew C Ahn T. McCoy R. Perlis Finale Doshi-Velez Krzysztof Z. Gajos 49 145 0 01 Feb 2021
Beyond Expertise and Roles: A Framework to Characterize the Stakeholders of Interpretable Machine Learning and their Needs Harini Suresh Steven R. Gomez K. Nam Arvind Satyanarayan 34 126 0 24 Jan 2021
Explaining the Black-box Smoothly- A Counterfactual Approach Junyu Chen Yong Du Yufan He W. Paul Segars Ye Li MedIm FAtt 67 100 0 11 Jan 2021
Concept-based model explanations for Electronic Health Records Diana Mincu Eric Loreaux Shaobo Hou Sebastien Baur Ivan V. Protsyuk Martin G. Seneviratne A. Mottram Nenad Tomašev Alan Karthikesanlingam Jessica Schrouff 11 27 0 03 Dec 2020
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images Pablo Messina Pablo Pino Denis Parra Alvaro Soto Cecilia Besa S. Uribe Marcelo andía C. Tejos Claudia Prieto Daniel Capurro MedIm 36 62 0 20 Oct 2020
Uncertainty-Aware Deep Ensembles for Reliable and Explainable Predictions of Clinical Time Series Kristoffer Wickstrøm Karl Øyvind Mikalsen Michael C. Kampffmeyer A. Revhaug Robert Jenssen AI4TS 30 34 0 16 Oct 2020
Assessing the (Un)Trustworthiness of Saliency Maps for Localizing Abnormalities in Medical Imaging N. Arun N. Gaw P. Singh Ken Chang M. Aggarwal ... J. Patel M. Gidwani Julius Adebayo M. D. Li Jayashree Kalpathy-Cramer FAtt 32 109 0 06 Aug 2020
Generative causal explanations of black-box classifiers Matthew R. O’Shaughnessy Gregory H. Canal Marissa Connor Mark A. Davenport Christopher Rozell CML 30 73 0 24 Jun 2020
What went wrong and when? Instance-wise Feature Importance for Time-series Models S. Tonekaboni Shalmali Joshi Kieran Campbell David Duvenaud Anna Goldenberg FAtt OOD AI4TS 53 14 0 05 Mar 2020
When Segmentation is Not Enough: Rectifying Visual-Volume Discordance Through Multisensor Depth-Refined Semantic Segmentation for Food Intake Tracking in Long-Term Care Kaylen J. Pfisterer Robert Amelard A. Chung Braeden Syrnyk Alexander MacLean Heather H. Keller A. Wong 22 18 0 24 Oct 2019
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 287 9,156 0 06 Jun 2015