Fake Intelligence: Massive Benchmark Forgery Uncovered in Medical Neural Networks

The high performance of specialized language models has turned out to be banal data fraud. On October 11, 2026, a study was published in Scientific Reports that destroys the reputation of the commercial B2B sector in AI: an analysis of Korean medical LLMs revealed that 96.7% of the records in the test dataset (3,333 out of 3,448) blindly duplicated files from the training set.

This phenomenon, known as data contamination, means that neural networks do not possess real clinical "thinking," but merely memorize the correct answers (overfitting). For the macroeconomics of GovTech and healthcare, this is a disaster. Integrators are selling clinics and governments "innovative" diagnostic algorithms that are absolutely useless outside of artificial laboratory conditions. The exposed vulnerability of benchmarks will inevitably trigger a wave of audits and lawsuits against developers, freezing billion-dollar budgets for AI implementation in critical medical infrastructure.

Source: Scientific Reports