Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist.

Area of Science:

• Reproductive Medicine
• Artificial Intelligence in Healthcare
• Biostatistics

Background:

• In vitro fertilization (IVF) is effective but underutilized.
• Accurate, understandable IVF prognostication is crucial for patient consideration.
• Existing prognostic methods may not meet the need for personalized predictions.

Purpose of the Study:

• To review machine learning (ML) models for IVF prognostication.
• To discuss the development, validation, and deployment of ML models for point-of-care use.
• To explore the integration of ML with human expertise to improve fertility care access.

Main Methods:

• Literature review of ML-based IVF prognostic models.
• Analysis of data and model pipelines for scaled ML implementation.
• Consideration of clinical ML implementation factors for point-of-care success.

Main Results:

• ML offers personalized prognostication using pre-treatment data.
• Specialized expertise is required for ML model development and deployment.
• Point-of-care implementation requires careful consideration of clinical factors.

Conclusions:

• ML-based IVF prognostics can enhance patient understanding and treatment decisions.
• Combining human expertise with ML can expand fertility care access.
• Leveraging ML can achieve significant health, social, and economic benefits in reproductive medicine.