Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects-Reference-Cited by-同舟云学术

Explainable Machine Learning (XML) for Multimedia-Based Healthcare Systems: Opportunities, Challenges, Ethical and Future Prospects

Published:2023 Issue: Volume: Page:21-46
ISSN:
Container-title:Explainable Machine Learning for Multimedia Based Healthcare Applications
language:
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Author:

Awotunde Joseph Bamidele^ORCID,Imoize Agbotiname Lucky^ORCID,Adeniyi Abidemi Emmanuel^ORCID,Abiodun Kazeem Moses^ORCID,Ayo Emmanuel Femi^ORCID,Kavitha K. V. N.^ORCID,Ajamu Gbemisola Janet^ORCID,Ogundokun Roseline Oluwaseun^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-38036-5_2

Reference131 articles.

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3. Abiodun, K. M., Awotunde, J. B., Aremu, D. R., & Adeniyi, E. A. (2022). Explainable AI for fighting COVID-19 pandemic: Opportunities, challenges, and future prospects. In Computational intelligence for COVID-19 and future pandemics (pp. 315–332).

4. Teach, R. L., & Shortliffe, E. H. (1981). An analysis of physician attitudes regarding computer-based clinical consultation systems. Computers and Biomedical Research, 14(6), 542–558.

5. Ye, L. R., & Johnson, P. E. (1995). The impact of explanation facilities on user acceptance of expert systems advice. MIS Quarterly, 19, 157–172.

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