In clinical practice, cardiologists use the Capabilities API to analyze coronarography studies by triggering automated processes for stenosis detection, lesion characterization, and SyntaxScore calculation. This real-time functionality allows them to streamline data interpretation, reduce diagnostic delays, and generate evidence-based treatment recommendations with high consistency.

Beyond diagnosis, the API plays a vital role in enhancing AI quality through physician feedback. Medical experts interact with AI-generated outputs—such as coronary artery segmentation or lesion localization—providing corrections that are captured and processed through the API. This feedback becomes part of a high-fidelity reference dataset, which is used to retrain and refine the deep learning models powering CadFlow. Through this mechanism, the API enables a continuous improvement loop in which clinical expertise directly informs algorithmic performance.

The API also supports advanced research applications. Medical and AI researchers gain access to shareable ground truth databases, consisting of curated and anonymized DICOM studies with validated annotations. These datasets, accessible through secure API endpoints, serve as a foundation for training new models, benchmarking algorithmic performance, and conducting reproducible studies across institutions. Additionally, the platform provides tools for manual and semi-automatic data annotation, enabling researchers to create domain-specific datasets tailored to their study needs.

For AI developers, the Capabilities API offers robust infrastructure to support integration of custom models, plugin development, and collaborative workflows. Developers can access patient datasets, embed their own algorithms into the CadFlow pipeline, and configure automated verification mechanisms for continuous deployment. This opens the platform to external contributors while ensuring compliance with medical data standards and institutional policies.

Altogether, the Capabilities API transforms CadFlow into more than a diagnostic assistant—it becomes a collaborative framework uniting clinical expertise, machine learning, and healthcare innovation in a single scalable system.

Leveraging Microsoft Azure for Scalable AI Services

Through Microsoft Founders Hub, Autosymed has gained access to technical support, cloud credits, and a suite of Azure-native services that have become integral to CadFlow’s architecture and performance.

Azure Functions (Serverless Computing) enable real-time transformation of medical feedback into datasets for training AI models. Azure functions processes cardiologist feedback and prepares it for retraining segmentation and stenosis detection models.

Azure Application Insights provides real-time performance monitoring and diagnostics for the Capabilities API, enabling data-driven optimization of both the user experience and AI algorithm performance.

Azure Identity Management (later extended with Keycloak for multi-cloud compatibility) ensures secure, role-based access to sensitive APIs through OAuth2 protocols.

Azure Kubernetes Services (AKS) & Helm Charts used for streamlining continuous integration and deployment (CI/CD) of microservices-based CadFlow components, improving system reliability and scalability.

These integrations support CadFlow’s Capabilities API platform, which exposes secure, modular services for hospital systems and medical researchers – covering everything from CRUD operations on DICOM images to feedback processing, report generation, and treatment recommendation.