Clinical Trial Evidence Graph for Your AI No B.S. Med gives your AI a deterministic MCP tool to improve recall and precision when surfacing personalized trial evidence.

Occasionally, medical AI safety hinges on users asking the perfect question. For example:

Personalization Precision
“Did the cited evidence’s enrolled population actually apply to this user?”
Examples:
A statin trial shows LDL reduction — but excluded pregnant women; AI recommends it to a 32-year-old trying to conceive.
A sleep-aid trial reports benefit in adults 18–65 — but excluded patients over 80; AI applies the result to a 92-year-old at high fall risk.
MCP Tool
  • Refine on P Population — narrow toward findings whose enrolled population matches the user
  • Applicability scoring — structured fit between user state and each trial’s eligibility criteria
  • FHIR ingestion — Conditions, Medications, Observations resolved to canonical identifiers
Recall
“Did we surface every relevant piece of evidence for this user?”
Examples:
A query for “kava + anxiety” returns benefit trials, but misses adverse-event reports of panic episodes in patients with comorbid ADHD.
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 also tested.
MCP Tool
  • Expand on I Intervention · C Comparator · O Outcome — via mechanism hubs, condition hubs, and causal links
  • Expand to nearby SNOMED CT concepts — sibling and parent conditions where adjacent evidence lives
  • Facts extracted from NIH-funded manuscripts, which are not fully available to ChatGPT because of copyright restrictions
Hallucination
“Did the agent cite real papers and summarize them faithfully?”
Examples:
AI cites the DPP trial for a 47% diabetes-prevention figure — but DPP reported 58%; right paper, wrong number.
AI claims an SSRI trial showed benefit in patients over 75 — but the trial only enrolled adults 18–65; the subgroup result was never measured.
MCP Tool
  • Audit each claim with reported clinical study facts and a reproducible query plan
  • Citation reconciliation — every cited PMC ID checked against the corpus before the addendum is shown
  • Structured slots, not paraphrase — verifiable against original tables and figures, no free-text rewrites to drift from

Epistemic burden: The Medical AI user shouldn't bear the burden of knowing exactly what needs to be asked.

Our solution gives your AI a tool to run deterministic graph queries to expand and refine the initial line of questioning over clinical trial finding components:
Population
Intervention
Comparator
Outcome

Want to integrate?

Email ops@nobsmed.com →

Prototype in ChatGPT →

Opens the No B.S. Med Custom GPT with your question prefilled. Free ChatGPT account works.

or try one of these:

Show RCTs of non-metformin interventions for prediabetes Trial evidence for weight loss interventions in prediabetes Compare behavioral therapy interventions for stress urinary incontinence
Browse all medical evidence FAQs →