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Deep learning models for schizophrenia diagnosis often use implausible data

By PulseAugur Editorial · [1 sources] ·

A recent study found that only two out of seven deep learning models used for diagnosing schizophrenia base their assessments on plausible brain information. The remaining models, despite achieving similar classification performance, often rely on implausible data, raising concerns about their clinical utility and the interpretability of their findings. AI

IMPACT Highlights the need for greater scrutiny and interpretability in AI models used for critical medical diagnoses.

RANK_REASON The cluster reports on a scientific study published in Nature Machine that evaluates the reliability of deep learning models for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Mastodon — fosstodon.org →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Deep learning models for schizophrenia diagnosis often use implausible data

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The cluster reports on a scientific study published in Nature Machine that evaluates the reliability of deep learning models for medical diagnosis. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Mastodon — fosstodon.org TIER_1 English(EN) · [email protected] ·

    🤖 Only 2 out of 7 deep learning models base schizophrenia diagnosis on plausible brain information Deep learning models for schizophrenia diagnosis often rely o

    🤖 Only 2 out of 7 deep learning models base schizophrenia diagnosis on plausible brain information Deep learning models for schizophrenia diagnosis often rely on implausible brain information despite similar classification performance. A pivotal study published in Nature Machine.…

    LINKS synestesia.uk/…/only-2-out-of-7-deep-lear… synestesia.uk/…/only-2-ou

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