PAI Oncology Trial FL

Practical tools for Physical AI Federated Learning in oncology clinical trials, by Claude Code Opus 4.6.

v1.1.0 Paper Release: Kawchak K. "Federated Learning Physical AI Oncology Trials Unification." Zenodo. 2026. DOI: 10.5281/zenodo.18795507. PDF and LaTeX source available in paper/.

Responsible-Use Notice: This repository provides research-grade tooling for engineers building federated learning systems for oncology clinical trials. All modules are intended for research and development use only. Independent clinical validation, IRB approval, and clearance are required before clinical use.

v1.0.1 Documentation Update: Comprehensive documentation refresh for v1.0.1. 235 Python modules, ~86,800 LOC, 31 example scripts, 82 test files, 5 CLI tools, 105+ Markdown docs. Triple AI peer review (3 Codex reviews + 3 Claude Code fix cycles) Full release details in V1_RELEASE.md. DOI: 10.5281/zenodo.17774559

AI Peer Review Process — Trust Benchmark

The v0.9.4 through v0.9.9 releases constitute a triple AI peer review process where Codex (OpenAI) performed independent senior engineering reviews and Claude Code (Anthropic) implemented all recommended fixes. This dual-manufacturer approach reduces single-AI bias and establishes a verifiable trust chain for AI-generated code.

  Triple AI Peer Review Pipeline (v0.9.4 - v0.9.9)
  =================================================

  +-----------------+     +-----------------+     +-----------------+
  |  CYCLE 1        |     |  CYCLE 2        |     |  CYCLE 3        |
  |  v0.9.4 / 0.9.5 |     |  v0.9.6 / 0.9.7 |     |  v0.9.8 / 0.9.9 |
  +-----------------+     +-----------------+     +-----------------+
         |                       |                       |
         v                       v                       v
  +-------------+         +-------------+         +-------------+
  | Codex       |         | Codex       |         | Codex       |
  | (OpenAI)    |         | (OpenAI)    |         | (OpenAI)    |
  | Reviews     |         | Reviews     |         | Reviews     |
  | codebase    |         | codebase    |         | codebase    |
  +------+------+         +------+------+         +------+------+
         |                       |                       |
         | 12 recommendations    | 9 recommendations     | 10 recommendations
         v                       v                       v
  +-------------+         +-------------+         +-------------+
  | Claude Code |         | Claude Code |         | Claude Code |
  | (Anthropic) |         | (Anthropic) |         | (Anthropic) |
  | Implements  |         | Implements  |         | Implements  |
  | all fixes   |         | all fixes   |         | all fixes   |
  +------+------+         +------+------+         +------+------+
         |                       |                       |
         | 12/12 resolved        | 9/9 resolved          | 10/10 resolved
         v                       v                       v
  +---------------------------------------------------------------+
  |                  31/31 Recommendations Resolved (100%)         |
  |  CI/Process --> Compliance/Security --> Hardening/Secrets      |
  |  Each cycle addresses progressively deeper risk layers         |
  +---------------------------------------------------------------+
         |
         v
  +---------------------------------------------------------------+
  |                     v1.0.0 Stable Release                      |
  |  92 total issues resolved (61 audit + 31 peer review)          |
  |  235 Python files | ~86,800 LOC | 82 test files                |
  +---------------------------------------------------------------+

  Key: Codex = independent reviewer (OpenAI)
       Claude Code = primary developer and fixer (Anthropic)
       Dual-manufacturer review reduces single-AI bias

What the Triple AI Peer Review Proves

AI code generation is reliable at scale. Claude Code built 235 Python files (~86,800 LOC) with zero critical regressions and passing CI across 3 Python versions.
Cross-manufacturer AI peer review works. Codex identified 31 actionable recommendations across 3 cycles. Claude Code implemented all 31 at a 100% resolution rate.
Dual-manufacturer review reduces bias. Using Codex (OpenAI) to review Claude (Anthropic) code ensures neither manufacturer's blind spots persist.
AI review operates at speeds impractical for humans. All 6 review/fix releases (v0.9.4–v0.9.9) were completed on 2026-02-19. A comparable human review of 86,800 LOC with 31 recommendations would require weeks of calendar time.
The correction trajectory converges. Cycle 1 addressed infrastructure. Cycle 2 addressed compliance and security. Cycle 3 addressed hardening. Each cycle addressed progressively deeper issues, demonstrating systematic risk elimination.

This AI peer review process marks an important trust benchmark: AI-driven code generation, review, and fixes can now operate at a pace and thoroughness that exceeds traditional human-only processes. The peer review evidence is documented in peer-review/, and referenced in DOI 10.5281/zenodo.17774559.

Quick Start

# Clone
git clone https://github.com/kevinkawchak/pai-oncology-trial-fl.git
cd pai-oncology-trial-fl

# Install
pip install -e ".[dev]"

# Verify
python scripts/verify_installation.py

Repository Structure

pai-oncology-trial-fl/
├── federated/                        # Core federated learning framework
│   ├── coordinator.py                #   FedAvg / FedProx / SCAFFOLD coordinator
│   ├── client.py                     #   Simulated hospital nodes
│   ├── model.py                      #   Numpy-based MLP model
│   ├── secure_aggregation.py         #   Mask-based secure aggregation
│   ├── differential_privacy.py       #   Gaussian mechanism DP
│   ├── data_ingestion.py             #   Data generation & partitioning
│   ├── data_harmonization.py         #   DICOM/FHIR vocabulary mapping & normalisation
│   └── site_enrollment.py            #   Multi-site enrollment management
├── physical_ai/                      # Physical AI integration
│   ├── digital_twin.py               #   Patient digital twins (exponential/logistic/Gompertz)
│   ├── robotic_integration.py        #   Surgical robot interface
│   ├── sensor_fusion.py              #   Multi-modal sensor fusion
│   ├── simulation_bridge.py          #   Cross-platform simulation (URDF/MJCF/SDF/USD)
│   ├── framework_detection.py        #   Simulation framework detection & pipeline
│   └── surgical_tasks.py             #   Surgical task definitions & clinical thresholds
├── privacy/                          # Privacy framework (HIPAA Safe Harbor)
│   ├── phi_detector.py               #   PHI detection (18 HIPAA IDs) — legacy
│   ├── deidentification.py           #   De-identification pipeline — legacy
│   ├── consent_manager.py            #   Consent & DUA management
│   ├── audit_logger.py               #   Audit logging with integrity hashing
│   ├── access_control.py             #   Role-based access control — legacy
│   ├── breach_response.py            #   Breach detection & incident response — legacy
│   ├── dua_templates/                #   DUA templates (markdown)
│   ├── phi-pii-management/           #   Advanced PHI/PII detection (v0.6.0)
│   │   └── phi_detector.py           #     All 18 Safe Harbor IDs, DICOM scanning, Presidio
│   ├── de-identification/            #   Production de-identification pipeline (v0.6.0)
│   │   └── deidentification_pipeline.py  #  HMAC-SHA256, date shifting, generalization
│   ├── access-control/               #   RBAC with 21 CFR Part 11 audit trail (v0.6.0)
│   │   └── access_control_manager.py #     Time-limited access, fail-closed expiration
│   ├── breach-response/              #   Breach response protocol (v0.6.0)
│   │   └── breach_response_protocol.py #   Four-factor risk assessment, 60-day timeline
│   └── dua-templates-generator/      #   DUA template generator (v0.6.0)
│       └── dua_generator.py          #     5 DUA types, security tiers, retention policies
├── regulatory/                       # Regulatory compliance framework
│   ├── compliance_checker.py         #   HIPAA/GDPR/FDA checks — legacy
│   ├── fda_submission.py             #   FDA submission tracking — legacy
│   ├── templates/                    #   IRB & consent templates
│   ├── fda-compliance/               #   FDA submission tracker (v0.6.0)
│   │   └── fda_submission_tracker.py #     510(k)/De Novo/PMA/Breakthrough, PCCP, QMSR
│   ├── irb-management/               #   IRB protocol manager (v0.6.0)
│   │   └── irb_protocol_manager.py   #     Protocol lifecycle, amendments, consent versioning
│   ├── ich-gcp/                      #   ICH E6(R3) compliance checker (v0.6.0)
│   │   └── gcp_compliance_checker.py #     13 GCP principles, compliance scoring
│   ├── regulatory-intelligence/      #   Multi-jurisdiction tracker (v0.6.0)
│   │   └── regulatory_tracker.py     #     FDA/EMA/PMDA/TGA/Health Canada monitoring
│   └── human-oversight/              #   Human oversight QMS (v0.6.0)
│       └── HUMAN_OVERSIGHT_QMS.md    #     CRF risk tiers, AE boundaries, safety gates
├── unification/                      # Cross-framework unification layer
│   ├── simulation_physics/           #   Isaac ↔ MuJoCo bridge, parameter mapping
│   ├── agentic_generative_ai/        #   Unified agent interface (CrewAI/LangGraph/AutoGen)
│   ├── surgical_robotics/            #   Surgical robotics unification
│   ├── cross_platform_tools/         #   Framework detector, model converter, policy exporter
│   ├── standards_protocols/          #   Data formats, communication, safety standards
│   └── integration_workflows/        #   Workflow templates
├── q1-2026-standards/                # Q1 2026 standards objectives
│   ├── objective-1-model-conversion/ #   Cross-framework model conversion pipeline
│   ├── objective-2-model-registry/   #   Federated model registry & validation
│   ├── objective-3-benchmarking/     #   Cross-platform benchmark runner
│   └── implementation-guide/         #   Timeline & compliance checklist
├── frameworks/                       # Framework integration guides
│   ├── nvidia-isaac/                 #   NVIDIA Isaac Sim/Lab integration
│   ├── mujoco/                       #   MuJoCo integration
│   ├── gazebo/                       #   Gazebo + ROS 2 integration
│   └── pybullet/                     #   PyBullet integration
├── supervised-learning/              # Supervised learning for oncology
├── reinforcement-learning/           # RL for surgical robotics & treatment
├── self-supervised-learning/         # SSL for medical imaging
├── generative-ai/                    # Generative AI & agentic workflows
├── digital-twins/                    # Primary domain: patient digital twins
│   ├── patient-modeling/             #   Patient physiology engine & twin factory
│   ├── treatment-simulation/         #   Chemo/radiation/immunotherapy simulation
│   └── clinical-integration/         #   EHR/PACS/LIS integration & federated bridge
├── tools/                            # CLI tools for oncology workflows
│   ├── dicom-inspector/              #   DICOM image inspection & validation
│   ├── dose-calculator/              #   Radiation & chemotherapy dose calculator
│   ├── trial-site-monitor/           #   Multi-site trial monitoring (GREEN/YELLOW/RED)
│   ├── sim-job-runner/               #   Cross-framework simulation job launcher
│   └── deployment-readiness/         #   Deployment readiness checklist validation
├── clinical-analytics/               # Federated clinical trial analytics (v0.9.0)
│   ├── analytics_orchestrator.py     #   Pipeline orchestration & workflow management
│   ├── pkpd_engine.py                #   Population PK/PD compartmental modeling
│   ├── risk_stratification.py        #   Clinical risk scoring & adaptive enrichment
│   ├── trial_data_manager.py         #   Data lifecycle, quality checks, versioning
│   ├── clinical_interoperability.py  #   ICD-10/SNOMED/LOINC/CDISC mapping
│   ├── survival_analysis.py          #   Kaplan-Meier, Cox PH, C-index, RMST
│   ├── consortium_reporting.py       #   DSMB reports, SHA-256 integrity hashing
│   └── examples-clinical-analytics/  #   6 progressive examples
├── regulatory-submissions/           # Regulatory submission platform (v0.9.1)
│   ├── submission_orchestrator.py    #   Submission lifecycle & workflow management
│   ├── ectd_compiler.py              #   eCTD module compilation (FDA TCG)
│   ├── compliance_validator.py       #   Multi-regulation validation (9 frameworks)
│   ├── document_generator.py         #   CSR, SAP, PCCP, ISO 14971, IEC 62304
│   ├── regulatory_intelligence.py    #   7-jurisdiction guidance tracking
│   ├── submission_analytics.py       #   KPIs, trends, FDA MDUFA benchmarking
│   └── examples-regulatory-submissions/  # 6 progressive examples
├── configs/                          # Training & deployment configuration
│   └── training_config.yaml          #   PPO/SAC + federated + safety constraints
├── peer-review/                      # AI peer review documentation (6 docs)
│   ├── v0.9.4-senior-peer-review.md  #   Codex review cycle 1 (12 recommendations)
│   ├── v0.9.5-peer-review-fixes.md   #   Claude fix cycle 1 (12/12 resolved)
│   ├── v0.9.6-senior-peer-review-recommendations.md  # Codex review cycle 2
│   ├── v0.9.7-peer-review-fixes.md   #   Claude fix cycle 2 (9/9 resolved)
│   ├── v0.9.8-senior-peer-review-recommendations.md  # Codex review cycle 3
│   └── v0.9.9-peer-review-fixes.md   #   Claude fix cycle 3 (10/10 resolved)
├── utils/                            # Shared utility modules (peer review)
│   ├── crypto.py                     #   Environment-driven HMAC key management
│   ├── time.py                       #   UTC timestamp helpers
│   ├── log_sanitizer.py              #   PHI-safe log sanitization
│   ├── config.py                     #   Centralized configuration dataclasses
│   └── error_codes.py                #   Structured error codes (18 codes)
├── scripts/                          # Utility scripts
│   ├── verify_installation.py        #   Dependency verification
│   ├── check_version_alignment.py    #   Version consistency check (CI)
│   ├── security_scan.py              #   Static security pattern scanner
│   ├── release_metrics.py            #   Repository metrics generator
│   └── deploy.sh                     #   Docker deployment
├── tests/                            # Comprehensive test suite (82 files)
├── examples/                         # Core example scripts (5 numbered)
│   ├── 01_federated_training_workflow.py
│   ├── 02_digital_twin_planning.py
│   ├── 03_cross_framework_validation.py
│   ├── 04_agentic_clinical_workflow.py
│   └── 05_outcome_prediction_pipeline.py
├── examples-physical-ai/             # Physical AI examples (6 numbered)
│   ├── 01_realtime_safety_monitoring.py
│   ├── 02_multi_sensor_fusion.py
│   ├── 03_ros2_surgical_deployment.py
│   ├── 04_hand_eye_calibration.py
│   ├── 05_shared_autonomy_teleoperation.py
│   └── 06_robotic_sample_handling.py
├── agentic-ai/examples-agentic-ai/   # Agentic AI examples (6 numbered)
│   ├── 01_mcp_oncology_server.py
│   ├── 02_react_treatment_planner.py
│   ├── 03_realtime_adaptive_monitoring_agent.py
│   ├── 04_autonomous_simulation_orchestrator.py
│   ├── 05_safety_constrained_agent_executor.py
│   └── 06_oncology_rag_compliance_agent.py
├── docs/                             # Documentation & notebooks
├── .github/                          # CI/CD, issue & PR templates
│   ├── workflows/ci.yml              #   Lint + format + YAML + test
│   ├── PULL_REQUEST_TEMPLATE.md
│   └── ISSUE_TEMPLATE/
├── CODE_OF_CONDUCT.md
├── CONTRIBUTING.md
├── SECURITY.md
├── SUPPORT.md
├── CHANGELOG.md
├── CITATION.cff
├── V1_RELEASE.md                     # v1.0.0 stable release documentation
├── DEVELOPMENT_PROPOSALS.md          # 3 future extension proposals
├── LICENSE
├── requirements.txt
├── pyproject.toml
├── ruff.toml
├── paper/                            # Academic paper (v1.1.0)
│   ├── Federated Learning Physical AI Oncology Trials Unification.pdf
│   ├── Latex Source Code.zip          #   LaTeX source (main.tex, arxiv.sty, references.bib, README)
│   ├── main.tex                       #   LaTeX source
│   ├── arxiv.sty                      #   Modified arxiv style
│   └── references.bib                 #   Bibliography (26 references)
├── posts.md
└── prompts.md

Core Technologies

Framework	Version	Last Update	Use Case	Unification Status
NumPy	≥1.24.0	2023-06-17	Core computation, federated MLP	Integrated
SciPy	≥1.11.0	2023-06-25	Statistical analysis, optimization	Integrated
PyTorch	≥2.5.0	2024-10-23	Deep learning, model training	Bridge available
NVIDIA Isaac Sim	4.2.0	2024-11-15	High-fidelity surgical simulation	Bridge available
MuJoCo	≥3.1.0	2024-01-10	Physics simulation, contact modeling	Bridge available
Gazebo	Harmonic	2024-09-20	ROS 2 integration, sensor simulation	Guide available
PyBullet	≥3.2.6	2023-06-15	Rapid prototyping, lightweight sim	Guide available
MONAI	≥1.3.0	2024-01-17	Medical image analysis	Optional
LangChain	≥0.3.0	2024-09-13	Agentic AI workflows	Interface available
LangGraph	≥0.2.0	2024-08-28	Stateful agent graphs	Interface available
CrewAI	≥0.80.0	2024-11-20	Multi-agent orchestration	Interface available
ONNX	≥1.15.0	2024-01-23	Model export, cross-platform deploy	Converter available

Unification Framework

The unification/ layer provides cross-framework interoperability for oncology clinical trial systems:

Simulation Physics — Bidirectional state conversion between Isaac Sim and MuJoCo, physics parameter mapping across frameworks, enabling seamless sim-to-sim transfer for surgical robotics validation.
Agentic Generative AI — Unified agent interface supporting CrewAI, LangGraph, AutoGen, and custom backends with tool format conversion (MCP, OpenAI, Anthropic) for clinical workflow automation.
Surgical Robotics — Standards-based integration layer for robotic surgical systems across simulation and physical platforms.
Cross-Platform Tools — Framework detection, model format conversion, policy export, and validation suite for ensuring reproducibility across heterogeneous environments.
Standards & Protocols — Data format specifications, communication protocols, and safety standards aligned with IEC 80601-2-77 and ISO 14971.

Key Capabilities

1. Federated Learning

Multi-site model training with FedAvg, FedProx, and SCAFFOLD aggregation strategies. Includes convergence detection, differential privacy (Gaussian mechanism with budget tracking), secure aggregation (mask-based protocol), and site enrollment lifecycle management.

2. Physical AI Integration

Patient digital twins with exponential, logistic, and Gompertz tumor growth models. Chemotherapy, radiation, immunotherapy, and combination therapy simulation with Monte-Carlo uncertainty quantification. Surgical robot interface with telemetry and clinical threshold evaluation.

3. Privacy Framework

Comprehensive HIPAA Safe Harbor compliance covering all 18 identifiers per 45 CFR 164.514(b)(2). Advanced PHI/PII detection with regex, DICOM tag scanning, and optional Presidio NER integration. HMAC-SHA256 pseudonymization with cryptographically random salt (os.urandom). Date shifting with consistent patient-level offsets.

4. Regulatory Infrastructure

Multi-pathway FDA submission tracking (510(k), De Novo, PMA, Breakthrough, Pre-Submission) referencing FDA AI/ML Device Guidance (Jan 2025), PCCP Guidance (Aug 2025), and QMSR (Feb 2026). IRB protocol lifecycle management with amendment tracking, consent versioning, and AI/ML disclosure requirements per ICH E6(R3) published September 2025.

5. Cross-Framework Unification

Framework-agnostic simulation bridge (URDF/MJCF/SDF/USD), cross-platform model conversion, unified agent interface for multiple LLM backends, and physics parameter mapping across simulation engines.

6. Standards & Benchmarking

Q1 2026 standards objectives for model format conversion, federated model registry with validation, and cross-platform benchmark runner for reproducible performance evaluation.

Dependencies

Core (required)

numpy>=1.24.0 — Array computation
scipy>=1.11.0 — Scientific computing
scikit-learn>=1.3.0 — ML algorithms
pandas>=2.0.0 — Data manipulation
cryptography>=41.0.0 — Encryption
pyyaml>=6.0 — Configuration parsing

Framework-Specific (optional)

torch>=2.5.0 — Deep learning
monai>=1.3.0 — Medical imaging
pydicom>=2.4.0 — DICOM parsing
mujoco>=3.1.0 — Physics simulation

Agentic AI (optional)

langchain>=0.3.0 — LLM chains
langgraph>=0.2.0 — Agent graphs
crewai>=0.80.0 — Multi-agent
anthropic>=0.39.0 — Claude API
openai>=1.50.0 — OpenAI API

Visualization (optional)

matplotlib>=3.8.0 — Plotting
plotly>=5.18.0 — Interactive charts

Deployment (optional)

onnx>=1.15.0 — Model export
onnxruntime>=1.17.0 — Inference

Testing / Development

pytest>=7.4.0 — Testing
ruff>=0.4.0 — Linting

Actively Maintained Repositories

Repository	Last Commit	URL
PyTorch	2024-10-23	https://github.com/pytorch/pytorch
MONAI	2024-01-17	https://github.com/Project-MONAI/MONAI
MuJoCo	2024-01-10	https://github.com/google-deepmind/mujoco
LangChain	2024-09-13	https://github.com/langchain-ai/langchain
LangGraph	2024-08-28	https://github.com/langchain-ai/langgraph
CrewAI	2024-11-20	https://github.com/crewAIInc/crewAI
Anthropic SDK	2024-11-06	https://github.com/anthropics/anthropic-sdk-python
Ruff	2024-04-01	https://github.com/astral-sh/ruff

Multi-Organization Cooperation

Organization Type	Integration Points
Academic Medical Centers	Federated training nodes, digital twin data, IRB coordination
Community Hospitals	Site enrollment, data harmonization, local model training
Pharmaceutical Companies	Treatment simulation, regulatory submission, compliance validation
Robotics Manufacturers	Simulation framework integration, surgical task definitions
Regulatory Bodies (FDA)	Submission tracking, 510(k)/De Novo/PMA workflow, compliance checks
Standards Organizations	IEC 80601-2-77, ISO 14971, ICH E6(R3) alignment

Usage Guide

1. Generate and Partition Data

from federated.data_ingestion import generate_synthetic_oncology_data, DataPartitioner

X, y = generate_synthetic_oncology_data(n_samples=1000, n_features=30, n_classes=2)
partitioner = DataPartitioner(num_sites=3, strategy="iid")
sites = partitioner.partition(X, y)

2. Run Federated Training

from federated.coordinator import FederationCoordinator
from federated.client import FederatedClient
from federated.model import ModelConfig

config = ModelConfig(input_dim=30, hidden_dims=[64, 32], output_dim=2)
coordinator = FederationCoordinator(
    model_config=config, num_rounds=10, strategy="fedprox", mu=0.01,
)
global_params = coordinator.initialize()

clients = [FederatedClient(s.site_id, config) for s in sites]
for c, s in zip(clients, sites):
    c.set_data(s.x_train, s.y_train)

for round_num in range(10):
    updates = [c.train_local(global_params, epochs=5, lr=0.01, mu=0.01) or c.get_parameters() for c in clients]
    counts = [c.get_sample_count() for c in clients]
    result = coordinator.run_round(updates, client_sample_counts=counts)
    global_params = coordinator.get_global_parameters()
    if result.converged:
        break

3. Patient Digital Twin Simulation

from physical_ai.digital_twin import PatientDigitalTwin, TumorModel

twin = PatientDigitalTwin(
    "patient_001",
    tumor=TumorModel(volume_cm3=3.5, chemo_sensitivity=0.6, growth_model="gompertz"),
    biomarkers={"pdl1": 0.7, "ki67": 0.3},
)
result = twin.simulate_treatment("chemotherapy", dose_mg=75, cycles=4)

4. Verify Installation

python scripts/verify_installation.py

Citation

@software{kawchak2026pai,
  title = {PAI Oncology Trial FL},
  author = {Kawchak, Kevin},
  year = {2026},
  url = {https://github.com/kevinkawchak/pai-oncology-trial-fl},
  version = {1.1.1}
}

AI Peer Review Process

@article{kawchak2026aipeerreview,
  title = {AI Peer Review Process for Code Generation and Verification},
  doi = {10.5281/zenodo.17774559}
}

Contributing

Contributions are welcome. See CONTRIBUTING.md for requirements:

Recency — Dependencies and references within 3 months of latest stable release.
Oncology Relevance — All contributions must relate to oncology clinical trial workflows.
Reproducibility — Include seed configuration, hardware specs, and version pinning.
Cross-Platform Compatibility — Test across Python 3.10, 3.11, and 3.12.

License

MIT License. See LICENSE for details.

Name		Name	Last commit message	Last commit date
Latest commit History 72 Commits
.github		.github
agentic-ai/examples-agentic-ai		agentic-ai/examples-agentic-ai
clinical-analytics		clinical-analytics
configs		configs
constraints		constraints
digital-twins		digital-twins
docs		docs
examples-physical-ai		examples-physical-ai
examples		examples
federated		federated
frameworks		frameworks
generative-ai		generative-ai
images		images
paper		paper
peer-review		peer-review
physical_ai		physical_ai
privacy		privacy
q1-2026-standards		q1-2026-standards
regulatory-submissions		regulatory-submissions
regulatory		regulatory
reinforcement-learning		reinforcement-learning
releases.md		releases.md
scripts		scripts
self-supervised-learning		self-supervised-learning
supervised-learning		supervised-learning
tests		tests
tools		tools
unification		unification
utils		utils
.gitignore		.gitignore
.pre-commit-config.yaml		.pre-commit-config.yaml
CHANGELOG.md		CHANGELOG.md
CITATION.cff		CITATION.cff
CODE_OF_CONDUCT.md		CODE_OF_CONDUCT.md
CONTRIBUTING.md		CONTRIBUTING.md
DEVELOPMENT_PROPOSALS.md		DEVELOPMENT_PROPOSALS.md
Dockerfile		Dockerfile
LICENSE		LICENSE
README.md		README.md
SECURITY.md		SECURITY.md
SECURITY_AUDIT.md		SECURITY_AUDIT.md
SUPPORT.md		SUPPORT.md
V1_RELEASE.md		V1_RELEASE.md
docker-compose.yml		docker-compose.yml
environment.yml		environment.yml
posts.md		posts.md
prompts.md		prompts.md
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
ruff.toml		ruff.toml

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PAI Oncology Trial FL

AI Peer Review Process — Trust Benchmark

What the Triple AI Peer Review Proves

Quick Start

Repository Structure

Core Technologies

Unification Framework

Key Capabilities

1. Federated Learning

2. Physical AI Integration

3. Privacy Framework

4. Regulatory Infrastructure

5. Cross-Framework Unification

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1. Generate and Partition Data

2. Run Federated Training

3. Patient Digital Twin Simulation

4. Verify Installation

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