Case Study
A synthetic case run through three clinical AI tools — ChatGPT, OpenEvidence, and Mari — to evaluate differential output from the same patient presentation. Two tools were given the patient's self-reported narrative. Mari completed a structured interview before generating a differential.
The patient
The case was designed with layered complexity: a plausible primary complaint, prior diagnoses that provide reasonable cover for the symptoms, and a family history that — with the right questions — points clearly toward the correct diagnosis.
42-year-old woman with Hashimoto's thyroiditis, vitiligo, and chronic iron-deficiency anemia presenting with nine months of progressive fatigue, night sweats attributed to perimenopause, nausea, and occasional mild abdominal discomfort. She was started on escitalopram for presumed depression with no improvement. She has lost 8 lbs, which she attributes to decreased appetite from nausea. Periods have become more irregular.
Family history: Mother with type 1 diabetes. Brother with celiac disease.
Method
ChatGPT and OpenEvidence were given the presenting complaint and background in natural language, as the patient would share it. Mari's structured interview was completed by a simulated patient as would occur before a real appointment.
Both ChatGPT and OpenEvidence returned clinically reasonable differentials. The correct diagnosis was not among them. Mari's differential was verified as correct by physicians and clinical AI tools.
History-taking
The structured interview surfaced three findings that did not appear in the patient's self-reported narrative. Each is clinically significant for primary adrenal insufficiency.
Hyperpigmentation — particularly of skin folds, scars, and mucous membranes — is a hallmark of primary adrenal insufficiency. Not volunteered. Asked directly.
Lightheadedness on standing indicates postural hypotension — a direct consequence of cortisol deficiency affecting vascular tone. Not volunteered. Asked directly.
The patient attributed her 8 lb loss to nausea. Mari reframed it alongside decreased appetite and fatigue together — a pattern consistent with adrenal insufficiency, not GI illness.
The patient already had two autoimmune conditions — Hashimoto's thyroiditis and vitiligo. Her mother had type 1 diabetes. Her brother had celiac disease. Autoimmune polyglandular syndrome clusters these conditions: a patient with two autoimmune diagnoses and a family history of others carries meaningful risk for primary adrenal insufficiency. The structured interview is designed to surface this pattern through targeted questioning.
Discussion
ChatGPT and OpenEvidence returned clinically reasonable differentials from the information they were given. The input in both cases was a patient's self-reported narrative — which is characteristically incomplete. Patients contextualize their symptoms through prior diagnoses, family patterns, and plausible explanations. In this case: perimenopause, escitalopram side effects, and a brother with celiac disease. That framing is understandable, and it shaped what each tool returned.
The three findings that changed the differential — skin darkening, orthostatic symptoms, and the weight loss pattern — were not volunteered. They were present. Eliciting them required targeted questions directed at the specific diagnostic possibilities implied by the patient's autoimmune history and family history.
"The difference was not a better algorithm. It was a better history."
Methodology
This is a synthetic test case — not a real patient. The presenting scenario was constructed to test Mari's history-taking against published clinical benchmarks for primary adrenal insufficiency. No real patient data was used.
ChatGPT and OpenEvidence were each given the patient's presenting complaint and background in natural language, as a patient would share it in a clinical encounter or online query. Mari's structured interview was completed by a simulated patient responding to Mari's questions in the patient portal — as would occur before a real appointment.
Mari's output — with primary adrenal insufficiency as the leading diagnosis — was reviewed and confirmed as correct by physicians and by the same clinical AI tools used in the comparison. This constitutes structured evaluation with physician verification. It is not a clinical trial or peer-reviewed study.
Level 3 — early quantitative evidence from a structured single-case evaluation with physician verification. Hekavor is building toward larger-scale clinical validation. This case is one data point, not a population study.
No EHR required. No long-term commitment.
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