DiagnoLabs: A Geospatial and Telemetry-Driven Self-Learning AI Platform for Accredited Diagnostic Pathology Access

Abstract

Finding a trustworthy pathology lab across India is surprisingly difficult outside metropolitan pockets. Field statistics show over four-fifths of local diagnostic centers run without valid NABL accreditation, meaning testing equipment, sample handling, and calibrators go unverified. Patients routinely pick whichever facility is physically closest or cheapest, without knowing whether their blood work will be accurate. We built DiagnoLabs to solve this disconnect by putting verified diagnostics ahead of doctor consultations. The platform combines five parts: a distance-ranking formula that discounts travel distance for NABL-accredited centers; a 14-role access control structure that keeps patient data strictly partitioned; a clinical copilot that suggests relevant tests from symptoms and updates its guidance using doctor corrections; an IoT-based sample tracking setup using doorstep OTP verification and continuous Bluetooth box temperature logging; and a lightweight MERN web interface. Benchmarks over 1,200 search trials and 500 clinical test histories showed a median lab query speed of 64 ms, 99.98% distance accuracy inside a 50 m threshold, and a decrease in AI suggestion errors from 12.4% down to 3.1% after physician corrections were recorded. In this work, we outline our design, the mathematical weighting model, security checks, and field observations.