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Evidence record 15881 · automatically gathered

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

Ensuring trust in AI systems is essential for the safe and ethical integration of machine learning systems into high-stakes domains such as digital health. Key dimensions, including robustness, explainability, fairness, accountability, and privacy, need to be addressed throughout the AI lifecycle, from problem formulation and data collection to model deployment and human interaction. While various contributions address different aspects of trustworthy AI, a focused synthesis on robustness and ex

Record details

Published: 3 August 2026
Source: arXiv
Category: Research
Topics: Bias & fairness · Privacy · Healthcare · Transparency
Retrieved: 4 August 2026

source-onlyevidence status

These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.

Shared topic: Bias & fairness · Shared topic: Privacy

Certified in Theory, Broken in Practice: Assumption Gaps in Cryptographic Model Certification

arXiv cs.CR (AI security) · 23 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Trustworthy Privacy-Preserving Multimodal Federated Learning for Personalised Breast Cancer Prediction

arXiv · 21 July 2026

Shared topic: Bias & fairness · Shared topic: Privacy

FairHealth: An Open-Source Python Library for Trustworthy Healthcare AI in Low-Resource Settings

arXiv · 5 May 2026

Shared topic: Bias & fairness · Shared topic: Healthcare

GoT-CD: Graph-of-Thoughts Causal Discovery and the Fragility of Post-hoc Path-Specific Fairness Audits

arXiv fairness query · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

Ethical use of artificial intelligence in education: proposed ethical competency framework for teachers

Frontiers in Artificial Intelligence · 3 August 2026

Shared topic: Bias & fairness · Shared topic: Privacy

When AI Does the Work, What Is Learning For? Post-Instrumental Learning and the Risk of Capacity Dissolution

arXiv cs.CY · 31 July 2026

How to cite this record

ethics.ai (3 August 2026), “Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability,” evidence record 15881, https://ethics.ai/record/15881 (originally published by arXiv).

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