What quality metrics were used in the medical startup? Why those specific thresholds?

In medical startups, model quality metrics are often used that reflect the balance between sensitivity (recall) and specificity, as well as precision and recall. For example, metrics such as AUC-ROC, F1-score, sensitivity, and specificity help evaluate how well the model correctly identifies patients and avoids misdiagnoses.

Threshold values are chosen based on clinical significance: for instance, high sensitivity is important to not miss patients, even if this leads to some false positives. The threshold can be set so that sensitivity is at least 90%, and specificity is maximized at this level. This helps minimize risks for patients and increase trust in the system.

Example: if a model diagnoses cancer, it is better to have fewer missed cases (high sensitivity), even if it means additional checks for some healthy individuals (false positives).