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AI Determines Sex of Person From Brain Scans - Neuroscience News (neurosciencenews.com)

submitted 9 months ago by Squire1039@lemm.ee to c/technology@lemmy.world

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This article describes a new study using AI to identify sex differences in the brain with over 90% accuracy.

Key findings:

An AI model successfully distinguished between male and female brains based on scans, suggesting inherent sex-based brain variations.
The model focused on specific brain networks like the default mode, striatum, and limbic networks, potentially linked to cognitive functions and behaviors.
These findings could lead to personalized medicine approaches by considering sex differences in developing treatments for brain disorders.

Additional points:

The study may help settle a long-standing debate about the existence of reliable sex differences in the brain.
Previous research failed to find consistent brain indicators of sex.
Researchers emphasize that the study doesn't explain the cause of these differences.
The research team plans to make the AI model publicly available for further research on brain-behavior connections.

Overall, the study highlights the potential of AI in uncovering previously undetectable brain differences with potential implications for personalized medicine.

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[–] LibertyLizard@slrpnk.net 36 points 9 months ago* (last edited 9 months ago) (2 children)

I have a suspicion that this is exactly what’s going on here and may be why past studies found no differences. AI is much better at quickly synthesizing complex patterns into coherent categories than humans are.

Also, 90% is not that good all things considered. The brain is almost certainly a complex mix of features that defy black and white categorization.

Hopefully we will be wise enough to not require trans people to prove their trans-ness scientifically. People have a right to do what they wish with their bodies and express their gender in a way that feels right to them, and should not be required to match some artificial physical diagnosis of what it means to be trans. Even if it turns out that most trans people do share certain brain structures or patterns. There will always be exceptions and that doesn’t mean we get to label someone’s identity as inauthentic.

[–] metallic_z3r0@infosec.pub 8 points 9 months ago (1 children)

Unlikely as it might be, maybe the 10% error rate is from gender queer people that haven't realized/faced it yet.

[–] LibertyLizard@slrpnk.net 12 points 9 months ago* (last edited 9 months ago) (1 children)

There are a lot of potential explanations. In essence they built a model to categorize brain features into male and female, and then tested this against their label of male or female on each brain. So this could result from problems with the model predictions—or just as easily from their “correct” labeling of each brain as male or female.

So a big question is how did they define male and female? By genetics? By reproductive anatomy? By self reported identity? This information was not in the article. All of these things are very likely correlated with things happening in the brain, but probably not perfectly. It’s worth noting that many definitions of sex do not consider gender identity at all—if such a definition was used, then a trans-man might be labeled female in their data, whether they have reckoned with their identity or not.

[–] knightly@pawb.social 7 points 9 months ago (1 children)

I looked into this, the study analyzed three pre-existing fMRI datasets.

I wasn't able to find any info on how these projects assessed sex/gender of participants.

[–] june@lemmy.world 1 points 9 months ago

Based on this, I’d assume they just used AGAB as that’s how medical professionals approach patients in their care.

[–] GenderNeutralBro@lemmy.sdf.org 6 points 9 months ago

Given any finite data set above a trivially small size/complexity, and an undefined set of criteria, the odds of meaningless patterns appearing are extremely high.

Machine learning algorithms are basically automated P-hackers when misused. Be skeptical of any conclusions drawn from ML that are not otherwise verifiable.