[Paper] SiliconHealth: A Complete Low-Cost Blockchain Healthcare Infrastructure for Resource-Constrained Regions Using Repurposed Bitcoin Mining ASICs

Source: arXiv - 2601.09557v1

Overview

SiliconHealth proposes a full‑stack, blockchain‑based electronic health‑record (EHR) platform that can be built for a fraction of the cost of conventional solutions. By breathing new life into retired Bitcoin mining ASICs—hardware originally designed for SHA‑256 proof‑of‑work—the authors show how sub‑Saharan Africa and other resource‑constrained regions can run a secure, tamper‑proof medical‑data network without relying on expensive GPUs, cloud services, or continuous power.

Key Contributions

• Hardware Re‑use Blueprint – Demonstrates how three generations of Bitcoin ASICs (Antminer S19 Pro, S9, and Lucky Miner LV06) can be repurposed as low‑cost cryptographic proof generators for a hierarchical health‑record blockchain.
• Deterministic Hardware Fingerprinting (DHF) – A novel paradigm that turns the deterministic hashing behavior of ASICs into unique, verifiable hardware IDs, guaranteeing 100 % proof verification in experiments.
• Energy‑Efficient Architecture – Shows a 2.93 MH/W efficiency on the LV06 chip and a total deployment cost of $847 per rural clinic, including a 5‑year solar power package—about a 96 % saving versus GPU‑based alternatives.
• Robust Data Integrity Layer – Integrates Reed‑Solomon LSB watermarking for medical‑image authentication, tolerating 30‑40 % data loss while still enabling reliable verification.
• Semantic Retrieval‑Augmented Generation (RAG) – Provides AI‑driven, natural‑language query capabilities over encrypted records, enabling clinicians to ask “What was the patient’s last HbA1c?” without exposing raw data.
• Offline Synchronization Protocol – Handles intermittent connectivity by batching transactions and reconciling state when a node regains network access, crucial for remote clinics.

Methodology

1. Hardware Mapping – The authors categorized health facilities into four tiers (regional hospitals, urban centers, rural clinics, mobile points) and assigned an appropriate ASIC model based on hash rate, power draw, and cost.
2. Blockchain Design – A permissioned, proof‑of‑authority chain runs on the ASICs, where each proof is a deterministic SHA‑256 hash produced by the hardware. The DHF scheme binds each proof to the physical chip, preventing spoofing.
3. Data Encoding – Medical images are watermarked using Reed‑Solomon codes applied to the least‑significant bits, allowing recovery even after substantial corruption.
4. AI Integration – A lightweight language model, fine‑tuned on de‑identified health records, is coupled with a Retrieval‑Augmented Generation pipeline that fetches relevant encrypted entries before generating answers.
5. Power & Cost Modeling – Solar panel sizing, battery storage, and ASIC power consumption were simulated over a 5‑year horizon, then compared against a baseline GPU‑based deployment.
6. Experimental Validation – 23 proof‑generation tests were run across all hardware tiers for 300 s each, achieving a 100 % verification rate. Image‑tampering experiments confirmed watermark resilience, and RAG queries were benchmarked for latency (< 1 s on average).

Results & Findings

These numbers collectively demonstrate that a blockchain EHR system built on repurposed ASICs is technically viable, financially attractive, and resilient to both power constraints and data tampering.

Practical Implications

• Rapid, Low‑Cost Deployment – NGOs and ministries can set up a secure health‑record network for a few hundred dollars per clinic, dramatically lowering the barrier to digital health in low‑income regions.
• Energy Independence – The ultra‑low power draw of the LV06 (≈13 W) pairs naturally with solar kits, enabling off‑grid operation for months.
• Hardware Traceability – DHF gives each node a cryptographic “serial number,” simplifying audits, anti‑tampering checks, and liability tracking without needing a PKI infrastructure.
• Interoperability – Because the blockchain stores only hashes and encrypted payloads, existing EMR systems can be retrofitted to push data into SiliconHealth via simple API adapters.
• AI‑Assisted Care – Clinicians in remote clinics can retrieve patient histories or get decision‑support hints via natural language, even when connectivity is spotty.
• Regulatory Alignment – The tamper‑evident, audit‑ready ledger satisfies many data‑protection regulations (e.g., GDPR‑like provisions) while keeping patient data encrypted at rest.

Limitations & Future Work

• Hardware Availability – The approach hinges on a supply of de‑commissioned ASICs; as mining hardware ages, sourcing may become uneven across regions.
• Permissioned Model – The current design assumes a trusted consortium of health authorities; extending to a fully decentralized trust model would require additional governance mechanisms.
• AI Model Size – The RAG component uses a lightweight language model; scaling to more complex diagnostic assistance may need larger models and edge‑accelerators.
• Field Trials – All experiments were conducted in controlled labs; real‑world pilots are needed to validate performance under harsh environmental conditions and variable network latency.
• Regulatory Hurdles – Adoption will require alignment with local health‑information laws, which may differ substantially from the assumptions made in the paper.

SiliconHealth opens an exciting pathway to democratize secure digital health using hardware that would otherwise sit idle in warehouses. With further field validation and ecosystem support, repurposed mining ASICs could become the backbone of next‑generation, low‑cost health IT in the world’s most underserved communities.

Authors

• Francisco Angulo de Lafuente
• Seid Mehammed Abdu
• Nirmal Tej

Paper Information

• arXiv ID: 2601.09557v1
• Categories: cs.NE, cs.CR
• Published: January 14, 2026
• PDF: Download PDF