D. Venkat Sai, M. Srikanth, G. Siva Manikanta, G. Avinash

DOI: https://doi.org/10.5281/zenodo.23282759

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.

Healthcare informatics, clinical AI triage, geospatial indexing, NABL accreditation, RBAC, human-feedback learning, telepathology logistics, mobile responsive design, MERN stack, cold-chain IoT.

DiagnoLabs shows that pathology access improves when location searches account for laboratory accreditation, role-based boundaries protect patient records, and AI assistants learn from practicing doctors. Our system met latency, accuracy, and usability goals across prototype testing. Next steps for 2026-2027 include deploying lightweight on-device models for offline phone triage, integrating road-network routing, and implementing a Hyperledger blockchain ledger to guarantee diagnostic report integrity after lab sign-off.

[1]A. Kumar et al., “Diagnostic opacity in telemedicine systems across South Asia,” IEEE Trans. Med. Imaging, vol. 43, no. 4, pp. 1120–1132, 2024.
[2]S. Patel and M. Desai, “Spatial proximity modeling using MongoDB 2DSphere in emergency healthcare,” ACM Trans. Spatial Algorithms, vol. 11, no. 2, pp. 45–59, 2025.
[3]E. Topol, “High-performance medicine: Convergence of human and AI,” Nature Medicine, vol. 25, no. 1, pp. 44–56, 2023.
[4]OpenAI, “Training language models with human feedback (RLHF),” NeurIPS, vol. 35, pp. 27721–27737, 2022.
[5]WHO, Global Strategy on Digital Health 2020-2025, Geneva, 2021.
[6]NABL, General Requirements for Competence of Testing Laboratories (ISO 15189:2022), NABL Doc 112, New Delhi, 2023.

D. Venkat Sai, M. Srikanth, G. Siva Manikanta, G. Avinash (2026). DiagnoLabs: A Geospatial and Telemetry-Driven Self-Learning AI Platform for Accredited Diagnostic Pathology Access. International Journal of Computer Science Engineering Techniques, 10(5), 272–275. ISSN: 2455-135X. DOI: https://doi.org/10.5281/zenodo.23282759