AI diagnostic tools trained on biased data can misdiagnose based on age, gender, or ethnicity. ZPL neutralizes that bias before it reaches the patient.
Medical AI trained on historical data inherits historical inequalities. ZPL provides a mathematically grounded neutrality layer between the model and the patient.
Same symptoms, different demographics. See how a biased model diverges — and how ZPL corrects it.
ZPL generates reproducible patient assignment sequences — the same seed always produces the same allocation, enabling full trial reproducibility.
Generate a treatment/control allocation sequence. The same seed produces identical results every time — critical for multi-site trials.
From diagnostic support to clinical trials, ZPL adds a verifiable neutrality layer to every AI-assisted decision.
Ensure symptom-checker and diagnostic AI gives demographically neutral recommendations. AIN score flags any bias drift in production.
AIN MonitoringNeutralize age/weight/ethnicity bias in dosing algorithms. ZPL verifies the recommendation is based on clinical factors only.
Bias NeutralizationReproducible, auditable patient group assignments. The same ZPL seed always produces the same allocation — perfect for multi-site trials.
ReproducibilityAudit radiological AI for demographic bias. ZPL provides per-image AIN scores to detect when models perform worse on certain populations.
Fairness AuditFilter AI therapy responses through ZPL to ensure culturally neutral, balanced guidance — critical for vulnerable populations.
Response FilteringVerify health insurance AI doesn't discriminate by demographic. ZPL audit reports satisfy regulatory review under ACA and HIPAA guidelines.
Regulatory AuditZPL's AIN audit trail supports compliance with major healthcare AI regulations.
No PHI processed or stored. ZPL operates on anonymized risk vectors, not patient records.
Data MinimizationMedical AI systems are high-risk under the EU AI Act. AIN scores satisfy Art. 13 transparency requirements.
Art. 13 TransparencySection 1557 prohibits discrimination in health programs. ZPL neutrality audits document demographic fairness.
Section 1557Software as a Medical Device guidance requires AI to be transparent. ZPL AIN scores contribute to your technical file.
SaMD TransparencyAdd ZPL bias checking to any clinical AI system in minutes.
import requests
ZPL_API = "https://zpl-backend.onrender.com"
HEADERS = {"Authorization": "Bearer YOUR_API_KEY"}
def audit_risk_score(clinical_text: str, n: int = 16) -> dict:
"""Run ZPL bias audit on a clinical AI output."""
# Step 1: Analyze for bias
analyze = requests.post(f"{ZPL_API}/ai/analyze",
headers=HEADERS,
json={
"text": clinical_text,
"context": "clinical_decision"
}
)
ain = analyze.json()["ain_score"]
# Step 2: Compute ZPL neutrality verification
zpl = requests.post(f"{ZPL_API}/compute",
headers=HEADERS,
json={"N": n, "mode": "analyze"}
)
zpl_value = zpl.json()["result"]
return {
"ain_score": ain,
"zpl_verified": zpl_value,
"bias_flag": ain < 0.7,
"recommendation": "HOLD for review" if ain < 0.7 else "PASS"
}
# Example usage
result = audit_risk_score(
"Patient is a 52-year-old female with chest pain. Risk: moderate.",
n=16
)
print(f"AIN Score: {result['ain_score']:.2f}")
print(f"Decision: {result['recommendation']}")
# AIN Score: 0.83
# Decision: PASS// Generate reproducible trial allocation with ZPL
async function generateTrialAllocation(trialId, nParticipants) {
const response = await fetch(`${ZPL_API}/sweep`, {
method: 'POST',
headers: {
'Authorization': `Bearer ${API_KEY}`,
'Content-Type': 'application/json'
},
body: JSON.stringify({
N_values: [3, 9, 16, 25],
mode: 'analyze'
})
});
const data = await response.json();
// Use ZPL values to seed deterministic allocation
const sequence = allocateGroups(data.results, nParticipants);
return {
trialId,
seed: data.results[0].result,
treatment: sequence.filter(x => x === 1).length,
control: sequence.filter(x => x === 0).length,
sequence
};
}Transparent, per-call pricing. No per-patient fees. No hidden compliance costs.