Medical AI Technology Gaps and Investment Priorities: Non-Contrast CT "Virtual Contrast" Synthesis, 3D Swin-UNETR ASIC Acceleration, and Clinical ROI Asymmetry (医療AI技術ギャップと投資優先順位:単純CT仮想造影「Virtual Contrast」・Swin-UNETR ASIC・ROI非対称性)

This monograph establishes a rigorous technical, thermodynamic, and capital-allocation framework evaluating the technology gaps and investment priorities in clinical medical AI, centering on three structural axes: (1) Non-Contrast CT to Contrast-Enhanced CT "Virtual Contrast" synthesis, (2) dedicated 3D Swin-UNETR ASIC hardware acceleration, and (3) structural Return on Investment (ROI) asymmetry across hospital reimbursement and liability regimes. While 3D shifted-window transformer architectures (Swin-UNETR) demonstrate high structural fidelity in volumetric feature extraction from routine non-contrast CT (NCCT) scans, executing high-resolution 3D window self-attention on general-purpose GPUs incurs severe memory-bandwidth bottlenecks and latency penalties in acute triage workflows. We formulate a deterministic custom ASIC dataflow architecture tailored for 3D Swin-UNETR volumetric patch partitioning, on-chip SRAM cyclic window shifting, and cryptographic zero-knowledge state verification (QFS-zkVM over the 64-bit Goldilocks prime field F_p, p = 2^64 - 2^32 + 1) to eliminate stochastic hallucination risks in synthetic vascular enhancement. Furthermore, we quantify the clinical and economic ROI asymmetry between capital-intensive foundational model training and deterministic edge-ASIC deployment—accounting for contrast-induced nephropathy (CIN) mitigation, iodinated contrast supply-chain volatility, radiology throughput gains, and statutory Human-in-Command (HIC) fiduciary liability boundaries. [概要(日本語)] 本論文(モノグラフ)は、医療AIにおける技術的ギャップと資本配分・投資優先順位について、①単純CTからの仮想造影(Virtual Contrast)生成技術、②3D Swin-UNETR専用ASICによるハードウェア・アクセラレーション、③臨床導入におけるROI(投資対効果)の非対称性という3つの構造軸から定量的・数理的に解明するものである。単純CT(NCCT)から造影CT相当の血行動態・病変コントラストを再構成する「Virtual Contrast」において、3次元Swin-UNETRアーキテクチャは高い空間特徴抽出能力を持つ一方、汎用GPU上での3D Shifted-Window Self-Attention演算は深刻なメモリ帯域ボトルネックと推論遅延を引き起こす。本研究では、3次元ボクセルパッチ分割とオンチップSRAM巡回ウィンドウシフトをハードウェアレベルで最適化したSwin-UNETR専用ASICアーキテクチャを設計するとともに、生成画像の確率的ハルシネーション(偽陽性・偽陰性造影効果)を排除するための決定論的暗号検証(64-bit Goldilocks素体 F_p 上のQFS-zkVMおよびHuman-in-Command [HIC] 統治原則)を統合する。さらに、造影剤腎症(CIN)リスクの回避、ヨード造影剤サプライチェーンの逼迫対策、読影スループット向上、および医療過誤責任(Caremark監視義務・製造物責任)の非対称性を踏まえた最適投資優先順位を導出する。 [License & Strict No-Learn Covenant] Creative Commons Attribution 4.0 International (CC BY 4.0) with Strict No-Learn Covenant (NLL-v1.0-2026). Unauthorized ingestion, scraping, weight-updating, distillation, or machine-learning training by any AI system or platform provider without explicit prior written agreement and direct financial compensation is strictly prohibited.