Connect ChatGPT & Claude to clinical-trial findings that fit you
via MCP
Off-the-shelf AI medical answers can be risky in four ways when they don't match clinical-trial details to your personal context.
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Personal Precision Risk
AI cited findings that don't apply to your personal issues
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Personal Recall Risk
AI missed findings that apply to your personal issues
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Safety
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“Did the cited safety finding actually apply to my situation?”
Example scenarios:
A statin clinical trial shows safe LDL reduction — but excluded pregnant women; AI recommends it to a 32-year-old trying to conceive.
A sleep-aid clinical 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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“Are there safety findings about my situation we missed?”
Example scenario:
A query for “kava + anxiety” returns benefit clinical trials, but misses adverse-event reports of panic episodes in patients with comorbid ADHD.
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Efficacy
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“Did the cited efficacy finding actually apply to my situation?”
Example scenario:
A GLP-1 agonist clinical 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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“Are there efficacy findings about my situation we missed?”
Example scenario:
A user asks about CBT for depression; a narrow query misses CBT clinical trials for PTSD, anxiety, and chronic pain — adjacent conditions where the same intervention is also tested.
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Our MCP tool addresses these risks by running deterministic, person-specific queries over granular clinical-trial findings.