Avalilação PREreview Estruturada de 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 clearly explains what the objective of the research is. What's the meaning of the objective and how the parameters of the objective are important for AI to integrate into Digital Health effectively. By using examples and images, the importance of the parameters of the objective is explained and the drawbacks of not fulfilling the objective of the research in integrating AI into Digital Health. Mentioning the loop holes in previous review articles, it explains clearly the objective of this research.

Are the methods well-suited for this research?

Somewhat appropriate

Reviewing previous reviews and finding the loop holes in them, ensuring that does loop holes are resolved in the current research. I think, the effective comparing of how the previous conclusions of the reviews were, and how now better results are seen in this research proves the fact that methods were well suited. Explaining Robustness and explainability through various methods of evaluation metrics of Trust, fidelity, proximity, validity, etc, was effective. I think a little more work could be done in the methods section.

Are the conclusions supported by the data?

Somewhat supported

The conclusion sums up the whole idea of the research. It does support the data provided to explain the objectives of the research. Explaining what robustness and explainability means and how it can be achieved in current AI models is well explained. Somewhat more explanation could have been given on the future of AI in digital health to sum up the conclusion effectively.

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

Highly appropriate and clear

The data presentations are excellent, using figures and tables to explain each and every part of the research was very thoughtful. It helps in summing up the data, so that it can be visualized better and understood effectively instead of just reading plain text.

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

Very clearly

The authors have clearly explained the objectives of the research by giving information on the past reviews and the loop holes in it. And, how their current research has proven effective in resolving the loop holes. By using figures and tables, the findings of their data can be interpreted easily, thus, proving their point of carrying out the research. Explaining the advancements of current LLM models in healthcare, also talking about each and every domain of health and the issues in it and how they can be solved through their research was effective. Thus, giving a view point for the next steps of AI in Digital Health.

Is the preprint likely to advance academic knowledge?

Highly likely

Yep, I highly agree that this preprint is beneficial for further development and research of AI in Digital health, as I have already mentioned how above.

Would it benefit from language editing?

No

No such language editing is needed.

Would you recommend this preprint to others?

Yes, it’s of high quality

The preprint is very useful to understand Trustworthy AI and how such AI models can be developed further in the future so that they integrate effectively in Healthcare alongside Healthcare professionals.

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

Yes, as it is

Yes, I think the preprint is ready.

Competing interests

The author declares that they have no competing interests.

Use of Artificial Intelligence (AI)

The author declares that they did not use generative AI to come up with new ideas for their review.