Evaluation and Risk Management Strategies for Developing AI-based Medical Image Products

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

Oveisi Shahrzad; Goudarzi Marjan; Moein Mohammad Shahram

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: International Journal of Reliability, Risk and Safety: Theory and Application Year:2024 | Volume:7 | Issue:2 Page(s): 15-27

Download Full-Text

View:

Download:

Cites:

Information Journal Paper

Title

Evaluation and Risk Management Strategies for Developing AI-based Medical Image Products

Author(s)

Oveisi Shahrzad | Goudarzi Marjan | Moein Mohammad Shahram | Issue Writer Certificate

Keywords

Artificial intelligence

Diagnostic Imaging

Deep Learning

Risk Management

Abstract

AI-based products, particularly in medical diagnosis, have become increasingly popular. However, with the rise of AI technologies, there is a critical need for quality assurance and risk assessment to ensure the reliability and impartiality of these systems. One crucial application of AI in the medical field is the diagnosis of diseases through imaging techniques, such as chest X-rays. Chest X-rays are commonly used by physicians to diagnose respiratory diseases quickly and cost-effectively. Yet, interpreting chest X-rays can be challenging, and errors in diagnosis can have severe consequences, especially for life-threatening conditions like pneumonia. Given the high mortality rate associated with pneumonia, accurate and timely diagnosis is essential. It is vital to prioritize quality assurance and risk assessment in the development and implementation of AI-based products, particularly in critical areas like medical diagnosis.In this paper, we utilized a deep CNN network to diagnose pneumonia from chest X-ray images. We also introduced two criteria, bias and transparency, to evaluate these products. For assessing these criteria, we provided methods based on checklists and quantitative assessment approaches for our data. We successfully implemented these solutions on our data and even achieved a robust model by applying data augmentation techniques, raising accuracy above 90 percent. Additionally, to validate our data, we used two tests: the pressure test and the crystal test, which yielded an accuracy of over 70 percent. We also completed all the checklist-based methods and were able to obtain validation for these data in medical products.

Multimedia

No record.

Cites

No record.

References

No record.

Cite

Related Journal Papers

No record.

Related Seminar Papers

No record.

Related Plans