EQUAL CARE® Certification

Sufficient gender representation in comparison to prevalence in the population present in foundational research.

Sufficient gender representation in comparison to prevalence in the population. Information on the origin and quality of the training data. Transparency in data preprocessing and applied transformations. Investigation and documentation of possible biases in the data and their impact on the model.

Sufficient gender representation in comparison to prevalence in the population. Information on the origin and quality of the training data. Transparency in data preprocessing and applied transformations. Investigation and documentation of possible biases in the data and their impact on the model.

Assessment of the importance of gender-specific needs as input variables for the model's decisions are included.

Validation of the model’s performance as a whole and segregated by gender was done.

Explainability of the hyperparameters used, the training dataset, and the training process.

Provide explanations in a way that is understandable even to non-experts.

Costs associated with the application for Users, Healthcare Professionals and Organizations

Evidence of any possible side effects or absences of them.

Ensure that the algorithm meets relevant regulatory requirements and ethical guidelines.

Level of evidence for AI development, Level of evidence for AI validation