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Evaluating predictive models in reproductive medicine.

Carol Lynn Curchoe1, Adolfo Flores-Saiffe Farias2, Gerardo Mendizabal-Ruiz3

  • 1Colorado Center for Reproductive Medicine Orange County, Newport Beach, California.

Fertility and Sterility
|November 8, 2020
PubMed
Summary
This summary is machine-generated.

Evaluating artificial intelligence (AI) in reproductive medicine requires careful consideration of data quality and reporting transparency. Proper appraisal ensures AI

Keywords:
Artificial intelligenceartificial neural networksconvolutional neural networksdeep learningmachine learning

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Area of Science:

  • Reproductive Medicine
  • Medical Informatics
  • Artificial Intelligence

Background:

  • Predictive modeling using artificial intelligence (AI) is emerging in reproductive medicine.
  • Clinicians and researchers are developing expertise to evaluate AI studies.
  • AI applications offer potential benefits and risks in diagnosis, treatment, and prognosis.

Purpose of the Study:

  • To discuss the critical appraisal of AI models in reproductive medicine.
  • To highlight the importance of transparency and standardization in reporting AI models.
  • To enable assessment of bias and clinical utility of AI in reproductive medicine.

Main Methods:

  • The study discusses the evaluation of AI models in reproductive medicine.
  • It emphasizes the role of database quality, data characteristics, and AI methodology.
  • Image data considerations include capture, preprocessing, treatment, and accurate labeling.

Main Results:

  • AI model performance and clinical utility depend heavily on database quality and characteristics.
  • Inconsistent image processing or labeling leads to database inconsistencies and reduced AI accuracy.
  • Transparency and standardization are crucial for assessing AI bias and utility.

Conclusions:

  • Critical appraisal of AI models in reproductive medicine is essential.
  • Standardized reporting and transparency are key to evaluating AI's impact.
  • Ensuring data quality and consistent methodology maximizes AI's potential clinical utility.