Beyond IVF Laboratory Optimization: Addressing Patient Dropout – RBMO
Reproductive BioMedicine Online shared a post on LinkedIn about a paper by Demián Glujovsky et al.:
“Reproductive medicine has invested four decades in optimizing the IVF laboratory; artificial intelligence (AI) applied to prediction, selection, and automation represents the latest iteration of that investment.
This new Countercurrent contribution from Demián Glujovsky, Claudia Schwarzstein Azar, Julieta Glujovsky, and Agustín Ciapponi argues that IVF has concentrated innovation in the embryo laboratory while neglecting an outcome of comparable magnitude, that AI could be redirected to identify patients at risk of dropout; and that a four-step agenda is achievable with existing clinic data:
- Standardized definition
- Institutional measurement
- Quality reporting, and
- Patient-facing disclosure
The authors say that the field already has the data needed to begin, and what it lacks is not technology but agreement that the patient who stopped is part of the outcome.
Until that changes, IVF will continue concentrating its optimization inside the laboratory while leaving an equally tractable problem outside the frame.
All our Countercurrent Contributions are made free to read – access it now, ahead of press, from our December issue, 53.6.”
Title: An incomplete optimization: IVF directs AI at embryo laboratory while treatment discontinuation goes unmeasured.
Authors: Demián Glujovsky, Claudia Schwarzstein, Julieta Glujovsky, Agustín Ciapponi.
You can read the Full Article in Reproductive Biomedicine Online.
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