Eliminating Bias in Artificial Intelligence (AI) Generated Predictions and Plans for Resource Allocation and Management in Hospitals

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dc.contributor.authorIyekekpolo E.N.
dc.contributor.authorEkoko E. C.
dc.contributor.authorOsa E.
dc.contributor.authorIkhu-Omoregbee D.O.
dc.contributor.authorOrukpe P.E.
dc.date.accessioned2024-11-25T16:27:11Z
dc.date.available2024-11-25T16:27:11Z
dc.date.issued2024-11-25
dc.descriptionScholarly article
dc.description.abstractIn this paper, we succinctly examine and proffer solutions to the problem of bias likely to be encountered in the use of Artificial Intelligence for making predictions and plans on the resourcrequired for efficient operation of a hospital. The major types of biases are presented along with their likely negative effect(s) on hospital resource planning and allocation, then broad strategies and solutions are proffered to counter their negative impact on resource predictions and plans. The solutions proffered are important to the effective management of resources in health-care facilities and can lead to better delivery of health care.
dc.identifier.citationIyekekpolo E.N., Ekoko E. C., Osa E., Ikhu-Omoregbee D.O. & Orukpe P.E. (2024). “Eliminating Bias in Artificial Intelligence (AI) Generated Predictions and Plans for Resource Allocation and Management in Hospitals". In Proceedings of the International Conference on Artificial Intelligence and Robotics (MIRG-ICAIR 2024) pp. 29-36, MIRG
dc.identifier.isbn978-978-771-680-9
dc.identifier.urihttps://ir.unilag.edu.ng/handle/123456789/13049
dc.language.isoen
dc.publisherMIRG
dc.relation.ispartofseriesMIRG-ICAIR 2024
dc.titleEliminating Bias in Artificial Intelligence (AI) Generated Predictions and Plans for Resource Allocation and Management in Hospitals
dc.typeArticle
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