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How AI Can Help Fix Our Brains

Psychiatrist Awais Aftab on why the best mental health technology works with human complexity, not against it.

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TL;DR: Today we’re releasing a new episode of our podcast AI & I. Dan Shipper goes in depth with Awais Aftab, psychiatrist, professor, and writer. Watch on X or YouTube, or listen on Spotify or Apple Podcasts. Here’s a link to the episode transcript.

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The same rigid thinking that held AI back for years is failing millions with mental health conditions—but AI’s progress offers a radical solution.

For years, computer scientists tried to teach machines using fixed rules, but that approach crumbled against the messy reality of the real world. Psychiatry faces the same trap today, forcing the infinite variations of human suffering into neat diagnostic boxes—reducing someone's unique experience of depression or anxiety to items on a checklist.

AI's breakthrough came from embracing deep learning—letting computers discover their own patterns from examples, rather than forcing predetermined rules. Now that same approach could transform mental health care: AI systems that recognize how symptoms cluster differently in each person and catch disorders before they fully develop.

In this episode of AI & I, Dan Shipper—whose interest in this topic arose from his own experience living with obsessive-compulsive disorder (OCD)—explores this transformation with Awais Aftab, who has been questioning psychiatry’s rigid categories from inside the field. Aftab is a clinical assistant professor at Case Western Reserve University, editor of Conversations in Critical Psychiatry—an Oxford University Press volume that tackles philosophical and critical perspectives in psychiatry—and author of the Substack newsletter Psychiatry at the Margins. You can check out their full conversation here:

If you want a quick summary, here are some of the themes they touch on:

How AI could map the landscape of mental disorders

Dan thinks that psychiatry is facing the same problem that early machine learning researchers did. For example, the rules they used to train a computer to recognize the letter “A” might’ve broken down because the letter can vary dramatically in shape, size, and style depending on handwriting or font.

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