The AI result lifecycle

An imaging model is not integrated merely because an inference process can read a file and produce a plausible mask. In a workflow, the service must select the intended source, return an object that the surrounding systems can use, and leave enough provenance to reconstruct what happened. A practical mental model has four responsibilities: retrieve, infer, return, and prove. These are not a universal deployment architecture, but they are useful checkpoints for any architecture.

Retrieve the intended source

The service first needs the right study and the right series. A routing rule might select examinations when they arrive, or a work item might identify the source objects to process. Retrieval can use DIMSE or DICOMweb. The transport is not the selection logic: a perfectly successful C-FIND, QIDO-RS query, or WADO-RS response can still provide the wrong series if the matching keys or routing rule were wrong.

This is why a localizer, scout, derived reconstruction, or an unexpected phase can be a meaningful failure even when the model itself is behaving exactly as trained. The input contract includes the study and series identity, acquisition context, geometry, and representation that the inference expects. A service should be able to show which source objects it selected, not merely that an input file was available.

Infer, return, and link

The inference step transforms the selected source into a derived result: perhaps a segmentation, a coded finding, a measurement, or another DICOM object. The return step then places that result where the imaging workflow can retrieve it, commonly through C-STORE or STOW-RS, and associates it with the source study and relevant series or instances. The exact object type and association rules depend on the intended downstream use and the applicable DICOM information-object definition.

The return object is not a cosmetic detail. A DICOM SEG can represent a segmentation with references to source images and spatial information. A DICOM SR can represent coded findings or measurements in a structured content tree with source references. Those structures can support later query, computation, linkage, or audit when the receiving systems implement the relevant capabilities. They do not guarantee that every viewer will display every object, and they do not replace the need to record how the model produced the result.

A PNG or PDF can be an appropriate human-facing rendering. By itself, however, a rendered picture does not retain the machine-readable segmentation labels, physical geometry, coded semantics, or source references that a downstream process may need. If those details are discarded, a later user cannot generally reconstruct a fully specified segmentation or measurement from the picture alone. Choosing a display representation instead of a structured object is therefore a decision about the future workflow, not only about presentation.

Prove what happened

Provenance is the part of the lifecycle that makes a result inspectable after the pipeline has moved on. A deployment-specific recommended record can connect the output to the source objects and record the model identity and version, the selected inputs, the processing time, and the relevant configuration or site context. The result’s own object identity and the workflow event that returned it matter as well. The exact record may be distributed between DICOM objects, AIW-I work items, an audit store, and locally validated application logs, but the relationship must be recoverable. This is a deployment recommendation, not an IHE conformance statement or a substitute for applicable profile, actor, transaction, option, and DICOM object requirements; local deployment records may go beyond those requirements.

Without that relationship, a later discrepancy has too many plausible causes. The wrong series may have been selected, a preprocessing step may have changed the measurement grid, a model may have been updated, or the output may have been attached to a different study. A numeric result without input and processing provenance is difficult to audit even when its value looks reasonable.

The same discipline is useful in a research pipeline. Preserving source references, processing metadata, and stable output identities supports later quality checks; it does not by itself demonstrate that a clinical deployment exists or that a particular integration is safe. The deployment claim still requires evidence about the surrounding workflow.

The lifecycle can now be stated as a causal chain: select the source, process that source, return a linked object, and preserve enough evidence to explain the result. Each hand-off can fail independently. The next section examines what happens when the model and storage operation are correct but the result still does not reach the person or system that needs it.