Artificial Intelligence Health Platform

Artificial Intelligence Health Platform is the infrastructure that enables the development, deployment, monitoring, and management of artificial intelligence/machine learning models on health data.

The platform aims to use models on clinical (image, text, signal) and operational (demand, resource) data in a secure, traceable, and KVKK compliant manner.

Other names / abbreviation: Healthcare AI Platform.

What is it used for?

The artificial intelligence health platform consolidates scattered AI initiatives under a single manageable framework, manages the model lifecycle (development, validation, monitoring), and supports data security/explainability principles.

Key features

• Model lifecycle: Development, deployment, and version management.
• Monitoring: Tracking model performance and data drift.
• Data governance: KVKK compliance, access, and privacy controls.
• Integration: Connection with clinical and operational systems.

Integration and standards

The Artificial Intelligence Health Platform does not operate in isolation; it typically exchanges data with the following systems and processes:

• HBYS / EMR
• PACS and data sources
• Data warehouse

Frequently referenced standards and regulations:

• FHIR / HL7
• KVKK
• Explainability principles

Benefits provided

• Manageability of AI initiatives
• Model performance monitoring
• Data security and governance

Selection and evaluation criteria

Key criteria considered when evaluating an artificial intelligence health platform:

• Model monitoring/governance
• Data security and KVKK compliance
• Integration capabilities
• Explainability

Points to consider

• Models should be evaluated with clinical validation; data drift should be monitored.
• Final decisions rest with the relevant clinical/management teams.