Real-World Deployment of an Ethical AI-Assisted Tele-Ultrasound Platform for Endometriosis Diagnosis in the Brazilian Public Health System: The EndoConnect Alpha Case Study and Its Evolution into the NAM-Endora Framework (Preprint)

BACKGROUND Endometriosis affects 10 % of reproductive-age women globally, with diagnostic delays of 7–10 years in Brazil’s SUS. Digital platforms offer promise, but few are ethically designed with AI for low-resource settings. OBJECTIVE To report real-world deployment of EndoConnect Alpha in SUS primary care, evaluate usability/acceptability, and describe its evolution into the NAM-Endora ethical AI framework. METHODS Applied methodological study. EndoConnect Alpha (React.js + Firebase) deployed in 10 SUS units (Ceará). n = 60 (45 patients, 15 professionals). Instruments: SUS, TAM, engagement metrics, clinical-psychosocial outcomes. Ethics CAAE 82094924.8.0000.5049. INPI BR5120250005556-0. RESULTS SUS 88.9 ± 9.8 (excellent); TAM 91.4.57/5. Trail completion 79 %. Pain reduction 23 % (VAS p=0.02), therapy adherence +17 %, anxiety −14 %. Strong SUS-TAM correlation (ρ=0.76, p<0.001). CONCLUSIONS EndoConnect Alpha is feasible and impactful in SUS. NAM-Endora provides scalable ethical AI governance for LMICs. Multicenter validation planned. CLINICALTRIAL Not applicable (formative research)