ACREVS
The Clinical Reflex Interface: A Hybrid Edge-AI Architecture
Democratizing healthcare access through safe, edge-scrubbed, and clinically-guarded AI pipelines. Mankind First.
The Clinical Problem
- Information Overload: Unguided LLMs feed patients unfiltered medical/legal jargon, leading to extreme anxiety and adversarial doctor-patient interactions.
- Physician Burnout: General Practitioners spend over 40% of consultation time on administrative sorting and defensive documentation instead of patient care.
The ACREVS Solution
- For Patients: A calming, localized interface that provides structured clinical pathways (e.g., GP → Specialist) instead of raw data dumps.
- For Experts: A clinical co-pilot that asynchronously scrubs PII on the edge and structures patient telemetry into actionable SOAP formats.
Edge-to-Cloud Architecture
ACREVS acts as the secure toll-plaza for hyperscale AI:
- Non-Blocking Edge-AI: Client-side ephemeral PII scrubbing via Web Worker Proxies. Identity never leaves the local proxy.
- The Center Bubble: Asynchronous cross-platform bridge ensuring sub-second verification.
- Hyperscale API Federation: Securely routing anonymized clinical telemetry to massive foundational models (e.g., Google Cloud Vertex AI, Med-PaLM) for semantic structuring, driving clean API billing without compromising patient privacy.
Controlled Clinical PoC Access
Due to clinical safety protocols and active patent-pending architectural validation, functional walkthroughs and technical telemetry recordings are shared exclusively under bilateral research disclosure. Currently preparing for Closed Clinical Beta in Australia (iOS TestFlight & Android APK).
Dr. Manish Sharma, FRACGP
Founder & Lead Clinical Architect, ACREVS
Practicing General Practitioner (Australia) & Clinical AI Researcher
Lead Author: "The AI-And The Patient: Mitigating Risks and Harnessing the Constructive Potential of Large Language Models in General Practice"
Note: ACREVS is an independent clinical research initiative and Proof-of-Concept (PoC). External models and ecosystem interfaces (e.g., Med-PaLM, Vertex AI) are cited strictly as architectural target integrations within this study, not as official endorsements or live institutional partnerships.