Medical AI integration and evaluation

C&C WaltonTech builds the software that connects medical AI to clinical work. We help teams define the task, connect the right information, evaluate model behavior, and design an interface that makes the result useful.

A model is one part of a clinical application. The surrounding system must preserve the meaning of the data, control access, support human review, and make failures visible.

Start with the clinical workflow

We begin with the person using the system and the decision they need to make. For a record review tool, this means defining which records it can read, how it points back to evidence, and what the reviewer does when information is missing. For an imaging workflow, it means understanding the source images and how the output fits into case review.

• Clinical systems integration. Connect EHR workflows, HL7 and FHIR data, and DICOM imaging while preserving clinical context and access boundaries.
• Medical AI applications. Build language model and imaging workflows with reviewable outputs, clear source information, and task-specific evaluation.
• Product engineering. Develop clinical interfaces, backend services, cloud infrastructure, and monitoring around the workflow.
• Medical visualization. Turn complex information into an experience that supports individual case review. Explore Neuroviz and stroke case planning.

Evaluate the behavior you need

A useful evaluation starts with a specific question: what would count as a correct, incomplete, or unsafe output for this task? We work from representative cases and explicit criteria, then examine failures as well as aggregate performance.

Before implementation, teams should specify the intended users, available inputs, acceptable response time, and when the system must defer to a person. After a model or prompt changes, the same cases can help identify regressions. Results should retain the model version and evaluation conditions so comparisons remain interpretable.

Public benchmarks provide useful context. Local evaluation asks a different question: whether a system performs the intended task with the data, users, and constraints of a particular workflow. Read about ARISE, MAST, and medical AI research.

From an idea to a working application

1. Define the need. Map the current workflow, intended users, data sources, and a measurable outcome.
2. Build and evaluate. Implement a focused application and review its behavior against agreed cases and criteria.
3. Integrate and monitor. Connect the application to the surrounding systems and establish a process for reviewing failures and changes.

Discuss your medical AI project

Bring a workflow, a prototype, or an evaluation problem. We can help identify what needs to be built and how to assess it. Contact Chase Walton or explore our projects.