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IINTS-AF SDK

IINTS-AF is an open-source research SDK for building reproducible diabetes-technology experiments. It combines virtual patients, glucose and insulin scenarios, candidate algorithms, deterministic safety checks, data-quality tools, and research reports in one workflow.

It is designed for simulation, education, benchmarking, and pre-clinical software research.

Research boundary

IINTS-AF is not a medical device. It must not be used for insulin dosing, diagnosis, treatment decisions, or real-time patient care.

Start In The Right Place

Your goal Start here You will learn to
Learn the SDK from the beginning Learning Path install, run, inspect, and validate an experiment
Get one result quickly First Run verify the installation and create a demo bundle
Use the desktop interface Desktop App run workflows and inspect outputs without memorising CLI commands
Work with data or local AI Workflow Hub choose the correct data, AI, study, or reporting route
Understand the scientific assumptions Scientific Workflow separate implementation, evidence, calibration, and limitations
Contribute code Developer Portal navigate the architecture and run the required checks

How The SDK Works

flowchart LR
    A["Patient and scenario"] --> B["Simulation engine"]
    B --> C["Candidate algorithm"]
    C --> D["Deterministic safety checks"]
    D --> E["Run bundle"]
    E --> F["Validation, reports, and optional AI review"]

The important separation is:

  1. The simulator computes the virtual physiological state.
  2. The candidate algorithm proposes an experimental action.
  3. The safety layer applies fixed, reviewable limits.
  4. The evidence layer records inputs, outputs, versions, and checks.
  5. The optional AI layer explains validated results; it is not the source of numerical truth.

Read Core Concepts for the vocabulary used throughout the documentation.

First Installation

IINTS-AF supports Python 3.10 through 3.14.

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install "iints-sdk-python35[full,mdmp]"

iints doctor --smoke-run
iints demo quick --output-dir results/first_run

On Windows PowerShell, activate the environment with:

.venv\Scripts\Activate.ps1

See Installation for source installs, optional research dependencies, and platform-specific help.

What You Can Build

  • deterministic virtual-patient simulations
  • repeatable scenario and algorithm comparisons
  • glucose-data quality and MDMP certification artifacts
  • AGP-style research reports, study summaries, and evidence bundles
  • local Ollama-assisted explanations of completed runs
  • glucose forecasting experiments with explicit evaluation gates
  • bench-only Raspberry Pi, Pico, UNO Q, Jetson, and FPGA workflows
  • interactive structural-biology and genomics research demonstrations

Each advanced feature has its own limitations. The documentation distinguishes between implemented behavior, scientific inspiration, empirical calibration, and clinical validation. Those terms are not interchangeable.

A Good First Session

  1. Complete the First Run.
  2. Read Core Concepts.
  3. Learn how to Understand A Run.
  4. Complete the First Workflow.
  5. Choose a specialised route from the Workflow Hub.

For the project website, visit iints.org. For source code and issues, use GitHub.