Trustworthy AI in Digital Health: A Comprehensive Review of Robustness and Explainability
AI Summary
This review examines trustworthy AI for digital health, with emphasis on robustness and explainability across the AI lifecycle. It situates these topics alongside fairness, accountability, and privacy, and discusses application-specific concerns in intensive care, neonatal health, and metabolic health. The paper surveys methods for robustness under data scarcity and distribution shifts, plus explainability techniques including feature attribution, gradient-based interpretation, and counterfactual explanations. It also discusses trustworthy AI in the era of LLMs and evaluation concepts such as validity, fidelity, and diversity.
Why it's worth reading
As healthcare AI moves toward clinical deployment, robustness and explanation quality become operational requirements. This review offers a timely map of methods, application risks, and evaluation criteria, while its preprint status calls for careful verification.
Deep Read
1. What happened
Original facts: arXiv paper 2608.02238 was published on 2026-08-03 and reviews trustworthy AI in digital health, emphasizing robustness and explainability. Its scope also includes fairness, accountability, and privacy across the lifecycle from problem formulation and data collection to deployment and human interaction.
2. Core technology
Original facts: The review covers robustness methods for data scarcity and distribution shifts, alongside explainability methods such as feature attribution, gradient-based interpretation, and counterfactual explanations. It also discusses trustworthy AI in the LLM era and evaluation concepts including validity, fidelity, and diversity.
3. Key evidence and numbers
Original facts: The abstract describes three main parts: trustworthy-AI pillars and challenges; application-specific considerations in intensive care, neonatal health, and metabolic health; and recent robustness and explainability techniques. It gives no unified benchmark, effect-size estimate, or meta-analysis statistic, so the abstract does not establish that one method class outperforms another.
4. Why it matters
Analysis: Healthcare models can fail because hospitals, devices, patient populations, and time periods change, even when conventional test accuracy looks strong. A lifecycle- and application-oriented framework is useful because it treats trust as more than a single score or a visualization attached after training.
5. Practical impact
Analysis: Teams building healthcare AI can use the review as a planning checklist: define likely shifts and failure modes, evaluate explanation validity and fidelity separately, and integrate human review into clinical workflows. Engineers can also distinguish model robustness tests, explanation evaluation, and post-deployment monitoring instead of reporting only aggregate accuracy or AUROC.
6. Limitations and uncertainty
Original facts: The supplied abstract does not specify the systematic-search protocol, inclusion criteria, evidence grading, or unified comparisons across methods. Analysis: Clinical tasks differ substantially in risk, label quality, and decision cost, so cross-domain recommendations may not transfer directly. Unverified inference: The full paper may provide a more detailed evaluation framework, but the abstract alone does not establish solutions for explanation faithfulness, causal validity, or LLM safety in medicine.
7. Original sources
- arXiv abstract page
- Paper ID:
arXiv:2608.02238 - Publication timestamp supplied with the item:
2026-08-03T13:51:12.000Z