Whether you think AI is on the cusp of replacing millions of jobs, or an overblown Google searchdesigned to agreewith you,one thing is sure: people whose job it is to analyze complex medical data might want to pay attention...

For years, biomedical research has had a problem: too much data, not enough people who know how to wrangle it - or simply that it tookmonths to do so. Modern health studies generate oceans of molecular information - gene expression, DNA methylation, microbiome profiles. Turning that into useful predictions about disease risk or pregnancy outcomes typically requires teams of data scientists,months of coding, and endless debugging.

Now, according to a new study inCell Reports Medicine,some AI systems can do much of that work in minutes -and in at least one case,they did it better than humans.

Researchers at UC San Francisco and Wayne State University took eight large language models - the same class of AI that powers systems like ChatGPT - and dropped them into a serious biomedical competition.The team used data from three previous international DREAM Challenges,where more than 100 research teams had built predictive models tackling reproductive health questions such as:

Can you predict gestational age from blood gene expression?

Can you estimate the biological age of the placenta from DNA methylation?

Can you detect risk of preterm birth from vaginal microbiome data?

So this ismodern AI creating modeling code in Python vs. human-coded predictive models, not humans manually processing the data (to be clear).

One dataset included around360,000 molecular features. Another required parsing genomic data from public repositories. In the original competitions,human teams spent up to three months developing and tuning their models.

The AI systems were given a carefully written prompt describing the dataset and the task.Then they had to generate executable R or Python code from scratch. Researchers ran that code and measured how well the resulting models performed on unseen test data.

Source: ZeroHedge News