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

Does the introduction explain the objective of the research presented in the preprint?

Yes

The introduction briefly explains trustworthy AI and existing frameworks. It also clearly justifies the focus on trustworthy components, robustness and explainability, of AI in healthcare with real world scenarios/applications.

Are the methods well-suited for this research?

Somewhat appropriate

Although authors provide a comprehensive review they do not describe how the review was conducted and evaluated for risk of bias.

Are the conclusions supported by the data?

Highly supported

Authors conclusions are supported by a thorough presentation of their synthesis with figures, tables and examples that comprehensively tackle the study objective.

Are the data presentations, including visualizations, well-suited to represent the data?

Highly appropriate and clear

Visuals presented are easy to comprehend/interpret and support the narrative. A key describing color should be included in Figure 3 to aid interpretability.

How clearly do the authors discuss, explain, and interpret their findings and potential next steps for the research?

Somewhat clearly

Authors thoroughly discuss and interpret their findings using examples, figures and summary tables. Authors should justify proposed taxonomy to better aid understanding and utility by researchers and practitioners.

Is the preprint likely to advance academic knowledge?

Somewhat likely

This preprint justifies the importance of robustness and explainability of AI applications in healthcare. It also gives an in-depth overview of methods, evaluation metrics and examples of robustness and explainability of AI applications in healthcare. Publication would have been stronger if authors identified gaps or recommended opportunities to advance robustness and explainability of AI applications in healthcare.

Would it benefit from language editing?

No

Would you recommend this preprint to others?

Yes, it’s of high quality

Is it ready for attention from an editor, publisher or broader audience?

Yes, after minor changes

1. Authors should consider including details on how comprehensive review was conducted and evaluated for risk of bias to aid transparency and comparability 2. Author should consider including a section identifying gaps/opportunities or providing recommendations to guide researchers and practitioners in developing robust and explainable AI solutions. One such recommendation could be for research to identify/confirm the multiplicative gains in performance and/or utility of AI applications when both robustness and explainability of AI applications are achieved.

Competing interests

The author declares that they have no competing interests.