Distribution of participants’ support for understanding the decision-making process of AI algorithms used in medical imaging.

<p>The figure illustrates that nineteen participants consider it crucial for medical practitioners to understand the rationale of AI decisions in medical imaging systems, while only five participants view this aspect as unimportant.</p>

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Bibliographic Details
Main Author:	Izegbua E. Ihongbe (19825859) (author)
Other Authors:	Shereen Fouad (4633552) (author), Taha F. Mahmoud (19825862) (author), Arvind Rajasekaran (19825865) (author), Bahadar Bhatia (19825868) (author)
Published:	2024
Subjects:	Cell Biology Biotechnology Science Policy Space Science Biological Sciences not elsewhere classified local interpretable model gaining acceptance within ensure better alignment employed two well agnostic explanations ), achieved accuracy rates visual explainability results driven explainability requirements known xai methods complex dl methods enabled diagnostic system chest radiology imaging radiology imaging modal explainability support diagnostic chest x chest radiography xlink "> undergoing evaluation treatment decisions technical hurdles remarkable increase regarding preferences practical aspects positive perception participants expressed medical professionals making process mainly due increase awareness inclusive design healthcare sector explainable ai deep learning ct scans crucial need clinical usability clinical relevance centered evaluation awareness regarding although concerns also identified ai decision
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Distribution of participants’ support for understanding the decision-making process of AI algorithms used in medical imaging.

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