Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability | Paper Citation Record

Observation e0f43930-6db5-48f0-90be-13b507e56f1a · outbound

This paper cites [Accessed July 2025].

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability [Accessed July 2025]

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Observation c61cd9b3-a855-4044-bd3b-28285867b452 · outbound

This paper cites https://digital-strategy.ec.europa.eu/en/library/ ethics-guidelines-trustworthy-ai.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability https://digital-strategy.ec.europa.eu/en/library/ ethics-guidelines-trustworthy-ai

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This paper cites https://scikit-learn.org/stable/modules/ generated/sklearn.datasets.load_breast_cancer.html.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability https://scikit-learn.org/stable/modules/ generated/sklearn.datasets.load_breast_cancer.html

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Observation 1d683cf4-c6ad-4d35-8356-21f52afa5870 · outbound

This paper cites https://www.nist.gov/ trustworthy-and-responsible-ai.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability https://www.nist.gov/ trustworthy-and-responsible-ai

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This paper cites From attribution maps to human-understandable explanations through concept relevance propagation.Nature Machine Intelligence, 5(9):1006– 1019, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability From attribution maps to human-understandable explanations through concept relevance propagation.Nature Machine Intelligence, 5(9):1006– 1019, 2023

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This paper cites A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion.Information Fusion, 96:156–191, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A systematic review of trustworthy and explainable artificial intelligence in healthcare: Assessment of quality, bias risk, and data fusion.Information Fusion, 96:156–191, 2023

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This paper cites ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition

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This paper cites Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Evaluating the faithfulness of saliency maps in explaining deep learning models using realistic perturbations

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This paper cites Deep learning for ecg arrhythmia detection and classification: an overview of progress for period 2017–2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Deep learning for ecg arrhythmia detection and classification: an overview of progress for period 2017–2023

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Observation f6e238dc-d566-4bd6-9863-5692b7ccad3d · outbound

This paper cites Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Glyman: Glycemic management using patient-centric counterfactuals

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This paper cites GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability GlyTwin: Digital Twin for Glucose Control in Type 1 Diabetes Through Optimal Behavioral Modifications Using Patient-Centric Counterfactuals

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This paper cites Semi-factual explanations in ai.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Semi-factual explanations in ai

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This paper cites A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A hybrid method for imputation of missing values using optimized fuzzy c-means with support vector regression and a genetic algorithm

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This paper cites Cudle: Learning under label scarcity to detect cannabis use in uncontrolled environments using wearables.IEEE Sensors Journal, 2025.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Cudle: Learning under label scarcity to detect cannabis use in uncontrolled environments using wearables.IEEE Sensors Journal, 2025

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This paper cites Robust counterfactual explanations on graph neural networks.Advances in Neural Information Processing Systems, 34:5644–5655, 2021.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Robust counterfactual explanations on graph neural networks.Advances in Neural Information Processing Systems, 34:5644–5655, 2021

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This paper cites Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Explainable AI is Responsible AI: How Explainability Creates Trustworthy and Socially Responsible Artificial Intelligence

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This paper cites Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods.Informatics in Medicine Unlocked, 40:101286, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Application of explainable artificial intelligence in medical health: A systematic review of interpretability methods.Informatics in Medicine Unlocked, 40:101286, 2023

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This paper cites Mhealth dataset.UCI machine learning repository, 2014.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Mhealth dataset.UCI machine learning repository, 2014

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Llama-nemotron: Efficient reasoning models.arXiv preprint arXiv:2505.00949, 2025

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Responsible development of clinical speech ai: Bridging the gap between clinical research and technology.NPJ Digital Medicine, 7(1):208, 2024

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Unresolved cited work

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Fine-tuning a llm using reinforcement learning from human feedback for a therapy chatbot application, 2023

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Layer-wise relevance propagation for neural networks with local renor- malization layers

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Nice: an algorithm for nearest instance counterfactual explanations.Data mining and knowledge discovery, 38(5):2665–2703, 2024

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Artificial intelligence revolution in turkish health consultancy: Development of llm-based virtual doctor assistants

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability e-snli: Nat- ural language inference with natural language explanations.Advances in Neural Information Processing Systems, 31, 2018

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Role of Orthogonality Constraints in Improving Properties of Deep Networks for Image Classification

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A multitask learning approach to per- sonalized blood glucose prediction.IEEE Journal of Biomedical and Health Informatics, 26(1):436–445, 2021

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Towards A Rigorous Science of Interpretable Machine Learning

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability The Llama 3 Herd of Models

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A trustworthy ai reality-check: the lack of transparency of artificial intelligence products in healthcare.Frontiers in Digital Health, 6:1267290, 2024

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Towards an AI co-scientist

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Counterfactual explanations and how to find them: literature review and benchmarking.Data Mining and Knowledge Discovery, 38(5):2770–2824, 2024

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Observation 18dfdd1e-8a20-4673-98ee-a9d3b360afc6 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:13.878365Z

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Observation b7b8c192-d93d-464d-9833-8831ad8a3c61 · outbound

This paper cites Trustworthy artificial intelligence in medical imaging.PET clinics, 17(1):1, 2022.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Trustworthy artificial intelligence in medical imaging.PET clinics, 17(1):1, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:14.048640Z

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Observation 2135004d-29f8-44e2-a2cc-446c55b37241 · outbound

This paper cites Adasyn: Adaptive synthetic sampling approach for imbalanced learning.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Adasyn: Adaptive synthetic sampling approach for imbalanced learning

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:14.158383Z

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Observation db38aa72-58ec-4a79-ac7e-1711fc754c4e · outbound

This paper cites Masked autoencoders are scalable vision learners.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Masked autoencoders are scalable vision learners

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Observation ac901a6b-a315-45d1-886b-353b7b15e35b · outbound

This paper cites Exploring inherent sensor redundancy for automotive anomaly detection.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Exploring inherent sensor redundancy for automotive anomaly detection

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Observation 93ff3b13-fb6f-4151-bba6-13f8221ff798 · outbound

This paper cites A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, 2025.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A survey on hallucination in large language models: Principles, taxonomy, challenges, and open questions.ACM Transactions on Information Systems, 43(2):1–55, 2025

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:14.615415Z

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Observation 84f070b3-9ccc-454d-afe8-56f864599bba · outbound

This paper cites Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ Digital Medicine, 6(1):74, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Self-supervised learning for medical image classification: a systematic review and implementation guidelines.NPJ Digital Medicine, 6(1):74, 2023

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Observation 23830338-8148-4b69-9545-afff25672411 · outbound

This paper cites Energy-efficient missing data recovery in wearable devices: A novel search-based approach.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Energy-efficient missing data recovery in wearable devices: A novel search-based approach

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Observation 46993dee-5238-4f97-af14-7ba1d57bdedf · outbound

This paper cites Cim: A novel clustering-based energy-efficient data impu- tation method for human activity recognition.ACM Transactions on Embedded Computing Systems, 22(5s):1–26, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Cim: A novel clustering-based energy-efficient data impu- tation method for human activity recognition.ACM Transactions on Embedded Computing Systems, 22(5s):1–26, 2023

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Observation 8bcc7fbc-81dd-41d9-af23-0b380ece61d3 · outbound

This paper cites OpenAI o1 System Card.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability OpenAI o1 System Card

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Observation 3cd8e532-7f0f-49e6-8681-d9f6ff3d7d91 · outbound

This paper cites Requirements for trustworthy artificial intelligence–a review.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Requirements for trustworthy artificial intelligence–a review

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:15.356539Z

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Observation 854219e8-04a4-478a-aa53-f7dd86692a88 · outbound

This paper cites Trustworthy artificial intelligence: a review.ACM computing surveys (CSUR), 55(2):1–38, 2022.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Trustworthy artificial intelligence: a review.ACM computing surveys (CSUR), 55(2):1–38, 2022

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:15.513749Z

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Observation e3b63d89-ae8d-415f-8171-3c1fede91a2b · outbound

This paper cites Identifying medical diagnoses and treatable diseases by image-based deep learning.cell, 172(5):1122– 1131, 2018.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Identifying medical diagnoses and treatable diseases by image-based deep learning.cell, 172(5):1122– 1131, 2018

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Observation 84724487-2fa8-4a31-9374-7c7932675b98 · outbound

This paper cites The promise and perils of artificial intelligence in advancing participatory science and health equity in public health.JMIR Public Health and Surveillance, 11(1):e65699, 2025.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability The promise and perils of artificial intelligence in advancing participatory science and health equity in public health.JMIR Public Health and Surveillance, 11(1):e65699, 2025

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Observation 7d3ee582-4ede-4866-ab67-a2880ced0eef · outbound

This paper cites Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Large language models are zero-shot reasoners.Advances in neural information processing systems, 35:22199–22213, 2022

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Observation c3aca629-9a17-4f56-bf3a-a99917cec774 · outbound

This paper cites Establishing and evaluating trustworthy ai: overview and research challenges.Frontiers in Big Data, 7:1467222, 2024.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Establishing and evaluating trustworthy ai: overview and research challenges.Frontiers in Big Data, 7:1467222, 2024

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Observation 35929166-045a-46d5-a8ca-869b6b55daf7 · outbound

This paper cites Self-supervised learning in medicine and healthcare.Nature Biomedical Engineering, 6(12):1346–1352, 2022.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Self-supervised learning in medicine and healthcare.Nature Biomedical Engineering, 6(12):1346–1352, 2022

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Observation 826c3506-eb07-4e22-92f3-b74efea871e0 · outbound

This paper cites Trustworthy ai in the age of pervasive computing and big data.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Trustworthy ai in the age of pervasive computing and big data

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Observation c63e989f-f6b0-44fc-ad84-9c84529550ba · outbound

This paper cites dr llm, what do i have?.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability dr llm, what do i have?

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Observation ea2a4ba5-4bab-42c4-8790-0e81e857f365 · outbound

This paper cites Retrieval- augmented generation for knowledge-intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Retrieval- augmented generation for knowledge-intensive nlp tasks.Advances in Neural Information Processing Systems, 33:9459–9474, 2020

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Observation e01185c5-c9d1-4e4c-82e9-aedd5c7c09fd · outbound

This paper cites Trustworthy ai: From principles to practices.ACM Computing Surveys, 55(9):1–46, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Trustworthy ai: From principles to practices.ACM Computing Surveys, 55(9):1–46, 2023

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Observation ab3dca9a-2c7f-495a-bb53-87687251101b · outbound

This paper cites YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability YOLOv6: A Single-Stage Object Detection Framework for Industrial Applications

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Observation be92703e-2677-4fef-9191-03d9eb973a63 · outbound

This paper cites Early prediction of alzheimer’s disease and related dementias using real-world electronic health records.Alzheimer’s & Dementia, 19(8):3506–3518, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Early prediction of alzheimer’s disease and related dementias using real-world electronic health records.Alzheimer’s & Dementia, 19(8):3506–3518, 2023

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Observation 696810ae-570e-42c5-9a46-45de81543bda · outbound

This paper cites Llms for relational reasoning: How far are we? InProceedings of the 1st International Workshop on Large Language Models for Code, pages 119–126, 2024.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Llms for relational reasoning: How far are we? InProceedings of the 1st International Workshop on Large Language Models for Code, pages 119–126, 2024

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Observation 0d5ec4b6-e78b-420a-b80b-2d430e5ba00a · outbound

This paper cites Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Improving LLM Reasoning through Scaling Inference Computation with Collaborative Verification

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Observation ab61dac6-8463-454b-80d2-2f3b800d43f0 · outbound

This paper cites Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment

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Observation 4b586f01-dd7d-4a02-8334-6a1e02efe000 · outbound

This paper cites Shap: A unified approach to interpreting model predictions.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Shap: A unified approach to interpreting model predictions

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Observation 6f785a3e-e15f-4708-9498-0a7ff8bf898e · outbound

This paper cites Clegg, Andrea Cavallaro, and Hamed Haddadi.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Clegg, Andrea Cavallaro, and Hamed Haddadi

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Observation 9f11365f-45c8-419d-a565-d499a9ecb589 · outbound

This paper cites Clegg, Andrea Cavallaro, and Hamed Haddadi.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Clegg, Andrea Cavallaro, and Hamed Haddadi

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:17.453163Z

Source-reported events for the cited work

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Observation 6fcdc625-60de-4825-8adf-11c15c89a359 · outbound

This paper cites Racette, Dorothy D.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Racette, Dorothy D

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Observation e70aebf4-e5bc-4e08-9f89-bdb807aa9287 · outbound

This paper cites AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability AIMI: Leveraging Future Knowledge and Personalization in Sparse Event Forecasting for Treatment Adherence

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:17.635979Z

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Observation 32ba2cd5-094c-41da-9a27-b21dd8dabf08 · outbound

This paper cites Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Use of What-if Scenarios to Help Explain Artificial Intelligence Models for Neonatal Health

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Source-reported events for the cited work

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Observation 406ad538-c2b0-44ec-a803-bd9a0083883e · outbound

This paper cites Neonatal risk modeling and prediction.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Neonatal risk modeling and prediction

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Source-reported events for the cited work

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Observation dabab563-ce6b-4828-9c93-2f4b7cfa4117 · outbound

This paper cites Mul- timodal time-series activity forecasting for adaptive lifestyle intervention design.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Mul- timodal time-series activity forecasting for adaptive lifestyle intervention design

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Observation 45a3da25-0d71-4ea0-b297-91f348ae35b3 · outbound

This paper cites Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Multimodal Physical Activity Forecasting in Free-Living Clinical Settings: Hunting Opportunities for Just-in-Time Interventions

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Observation e9fd9537-60fc-43da-ac6e-a97fd92a8f10 · outbound

This paper cites Designing deep neural networks robust to sensor failure in mobile health environments.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Designing deep neural networks robust to sensor failure in mobile health environments

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:17.968156Z

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Observation 11ff52d8-79f5-4259-a9d4-5d4167d1f5f7 · outbound

This paper cites Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Sensors and healthcare 5.0: transformative shift in virtual care through emerging digital health technologies

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Source-reported events for the cited work

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Observation 59325db4-2738-40a4-bd32-22bb64ca5a84 · outbound

This paper cites A comprehensive study on fidelity metrics for xai.Information Processing & Management, 62(1):103900, 2025.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A comprehensive study on fidelity metrics for xai.Information Processing & Management, 62(1):103900, 2025

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Observation 98f94498-b6a7-4a07-9d62-ba44feba887e · outbound

This paper cites Labelmerger: Learning activities in uncontrolled environments.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Labelmerger: Learning activities in uncontrolled environments

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Observation 8740fce6-7de1-4f0a-8eb5-69771e4657d5 · outbound

This paper cites A multidisciplinary survey and framework for design and evaluation of explainable ai systems.ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3-4):1–45, 2021.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability A multidisciplinary survey and framework for design and evaluation of explainable ai systems.ACM Transactions on Interactive Intelligent Systems (TiiS), 11(3-4):1–45, 2021

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:18.234613Z

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Observation f5db1d20-58af-4081-b806-e24e0f21ddad · outbound

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Unresolved cited work

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Observation d917fa6c-f623-4bd8-aa22-bce7b0eaa715 · outbound

This paper cites Predicting sepsis using deep learning across international sites: a retrospective development and validation study.EClinicalMedicine, 62, 2023.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Predicting sepsis using deep learning across international sites: a retrospective development and validation study.EClinicalMedicine, 62, 2023

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Observation 13ff2069-7a48-4448-b439-a10e819e815d · outbound

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:18.610984Z

Source-reported events for the cited work

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Observation de086e3a-bce5-4b28-a876-3bb378fbd9e6 · outbound

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Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Unresolved cited work

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:18.731200Z

Source-reported events for the cited work

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Observation 63ec5e33-38ad-4112-8907-897b2b53c456 · outbound

This paper cites Explaining machine learning classifiers through diverse counterfactual explanations.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Explaining machine learning classifiers through diverse counterfactual explanations

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Observation c92fd196-8dca-4e57-a9aa-78e4c50e99f7 · outbound

This paper cites Rnas-cl: Robust neural architecture search by cross-layer knowledge distillation.International Journal of Computer Vision, 132(12):5698–5717, 2024.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Rnas-cl: Robust neural architecture search by cross-layer knowledge distillation.International Journal of Computer Vision, 132(12):5698–5717, 2024

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Resolution: unresolved; no resolver link, observed 2026-08-04T10:56:18.921457Z

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Observation d6744630-e9a2-4b81-bbcb-00a3eeefdea7 · outbound

This paper cites Multimodal deep learning.

Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability Multimodal deep learning

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