Publications by authors named "Michael Anis Mihdi Afnan"

The last decade has seen an explosion of machine learning applications in healthcare, with mixed and sometimes harmful results despite much promise and associated hype. A significant reason for the reversal in the reported benefit of these applications is the premature implementation of machine learning algorithms in clinical practice. This paper argues the critical need for 'data solidarity' for machine learning for embryo selection.

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Artificial intelligence (AI) techniques are starting to be used in IVF, in particular for selecting which embryos to transfer to the woman. AI has the potential to process complex data sets, to be better at identifying subtle but important patterns, and to be more objective than humans when evaluating embryos. However, a current review of the literature shows much work is still needed before AI can be ethically implemented for this purpose.

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Introduction: Exit block is the most significant cause of poor patient flow and crowding in the emergency department (ED). One proposed strategy to reduce exit block is early admission predictions by triage nurses to allow proactive bed management. We report a systematic review and meta-analysis of the accuracy of nurse prediction of admission at triage.

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Background: There has been increased interest in the use of biomaterials that resorb completely leaving only the patient's native tissue. Synthetic materials are advantageous for tissue repair because they are highly customisable. The infection rate of using resorbable natural materials in paediatric surgery has recently been outlined, but there has not yet been a review of the use of synthetic resorbable materials in paediatric surgery.

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