Connect ChatGPT & Claude to clinical-trial findings that fit you
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Below are examples of how AI medical answers can go wrong when clinical-trial findings aren’t grounded in your personal context.
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AI Overgeneralizes
Personal precision risk
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AI Overlooks
Personal recall risk
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Safety
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A sleep-aid trial reports tolerability in adults 18–65 — but excluded patients over 80. AI applies the result to a 92-year-old at high fall risk.
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A query for “kava + anxiety” returns benefit trials, but misses adverse-event reports of panic episodes in patients with comorbid ADHD.
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Efficacy
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A GLP-1 agonist trial showed weight loss in patients with BMI ≥ 30 and type 2 diabetes. AI applies the same expected efficacy to a BMI 26 user without diabetes.
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A user asks about CBT for depression. A narrow query misses CBT trials for PTSD, anxiety, and chronic pain — adjacent conditions where the same intervention is tested.
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Our MCP tool addresses these risks by running deterministic, person-specific queries over granular clinical-trial findings.
Learn more about The Evidence-to-Person Fit Problem →
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