Machine Learning & Artificial Intelligence · Medical AI & Healthcare Imaging

Validation, interpretation, and robustness of medical artificial intelligence models

Public introduction · full guide in Eikos

Introduction

What this guide covers

A comprehensive technical reference on statistical foundations, clinical evaluation protocols, mathematical explainability, and bias mitigation methodologies in high-criticality diagnostic and prognostic settings.

Overview

Topics covered

  • Introduction
  • Medical Artificial Intelligence: Validation, Interpretation and Robustness of Models
  • Medical AI and Peculiarities of the Clinical Environment
  • 1.1 Shift of the Disease Spectrum (Spectrum Shift)
  • 1.2 Spectrum Bias
  • 1.3 Extreme Class Imbalance

Related concepts

Main connections

  • Diagnostic test evaluation & ROC
  • Model interpretability (SHAP / LIME)
  • Epidemiological measures & risk ratios
  • Mean & expected value
  • Survival analysis & Kaplan-Meier
  • Missing data & multiple imputation

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

P(𝐗|Y=1)Terciario≠P(𝐗|Y=1)Primaria
View LaTeX sourceP(\boldsymbol{X} | Y = 1)_{\text{Terciario}} \neq P(\boldsymbol{X} | Y = 1)_{\text{Primaria}}