Understand
Translate a scientific question into explicit analysis intent, data requirements and constraints.
Research vision
Make scientific software work together.
The vision is a scientific ecosystem in which researchers and AI systems can discover trusted software, execute explicit operations, inspect results and preserve the evidence without hiding how the work was performed.
The vision
Six principles for scientific AI that can be trusted.
The initiative is built around a simple progression: understand the scientific question, connect the right tools, prove what happened, preserve the evidence, respect the measurement boundary and build broader interoperability only when the underlying operations are explicit and inspectable.
Connect
Expose specialized bioimaging capabilities through typed and discoverable interfaces.
Prove
Record parameters, tool versions, inputs, outputs and validation evidence so a workflow can be inspected.
Preserve
Keep provenance attached to computation so datasets, configurations and results remain traceable across a research workflow.
Measure
Keep segmentation, quantification and scientific measurement grounded in explicit computational tools and validation procedures.
Interoperate
Build broader agent and tool interoperability only after scientific operations are explicit, inspectable and reproducible.
Scientific boundary
AI orchestrates. Scientific software measures.
The language model is positioned as a planning and interface component. It does not become the measurement engine.
Agent layer
What an agent can do
Interpret a research question, identify suitable operations, coordinate a workflow and explain the resulting evidence.
Scientific layer
What an agent must not replace
The scientific software that performs segmentation, measurement, visualization and other domain specific computation.
The sequence
Evidence comes before abstraction.
A rigorous scientific workflow establishes measurable operations before those operations are exposed through a broader interoperability layer.
01
Scientific question
Define the problem and its measurable claims.
02
Controlled experiment
Fix data roles, configuration and evaluation protocol.
03
Failure evidence
Diagnose where and why a method fails.
04
Robustness
Evaluate interventions against a frozen baseline.
05
Interoperability
Expose proven operations through explicit interfaces.
The standard
Science first. Software second. Claims last.
Every capability should be measurable, reproducible and inspectable. The architecture can become broader as the evidence becomes stronger.