Anti-leakage evaluation for reproducible medical AI

In medical AI, a strong number can hide a weak experimental question. This note frames anti-leakage evaluation as a prerequisite for reproducible and trustworthy systems.

The practical direction is to document data manifests, split data by the right real-world constraints, and report failure modes instead of only the best metric.

Initial notes on temporal shortcuts, information leakage, and more honest evaluation protocols for medical AI systems.

Raul Pacheco Rodriguez

Why I am separating this research blog from FP32

This English version keeps the core idea of the original note: FP32 can remain a broader editorial space, while this site can host work that is closer to research, papers, systems, and professional updates.

The distinction matters because not every idea belongs in the same publication context. Some notes are exploratory, some are technical logs, and some are part of a longer academic trajectory.

A short note on why this site should host research-adjacent writing while FP32 remains an independent editorial lab.

A practical tutorial-style article about building a personal link hub with HTML and CSS instead of depending entirely on a third-party tool.

This is an editable English migration of a legacy article from the original blog. It keeps the central intent of the post while making it fit the newer research-oriented structure of the site.