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1. Technical Specification 2. Conformance Testing 3. Performance Assessment

1. Technical Specifications

This page gives the links to the specification of Data Types specified by Technical Specification: AI for Health (MPAI-AIH) – Health Secure Platform (AIH-HSP) V1.0.

Acronym AIH Name JSON Acronym AIH Name JSON
AIH-AHD AIH Data X AIH-EPT EEG Processing Type X
AIH-DPR AIH Data Process X AIH-EHQ EHR Object X
AIH-HDP AIH Data Processing Types X AIH-FDL Federated Learn X
AIH-AHT AIH Taxonomies X AIH-GPT Genomic Processing Type X
AIH-ARD ARA Data X AIH-GNO Genomics Object X
AIH-ARQ Audit X AIH-HLD Health Data X
AIH-BMD Biometric Data X AIH-LCF Licence Confirm X
AIH-BCL Blockchain License X AIH-MIO Medical Image Object X
AIH-CDF Common Definitions X AIH-MPT Medical Image Processing Type X
AIH-DRQ De-ID and Anonym X AIH-MDL Model Licence X
AIH-ECQ ECG Object X AIH-AAP Register X
CPT ECG Processing Type X AIH-TKN Tokens X
AIH-EEQ EEG Object X AIH-UPR User Profile X

2     Conformance testing

The Conformance a Data instance conforms with AIH-HSP V1.0 is expressed by one of the two statements:

  1. “Data conforms with the relevant (Non-MPAI) standard” – for Data.
  2. “Data validates against the Data Type Schema” – for Data Object.

The latter statement implies that:

A Data instance Conforms with AIH-HSP V1.0 specified Data Type if:

  1.  Its JSON Object validates against its JSON Schema.
  2. Any included  JSON Object validates against its JSON Schema.
  3. All Data in the JSON Object:
    1. Have the specified Data Types.
    2. Conform with the Qualifiers signaled in their JSON Schemas. For example, if the data claims to be UNICODE, it should conform with what the Text Qualifier (MPAI-TFA V1.4) defines as UNICODE.

Note that at this stage the AIH-HSP V1.0 does specifies Conformance Testing for Data Types.

3     Performance Assessment

Performance is an umbrella term used to describe a variety of attributes – some specific of the application domain served by a specific Data Type. Therefore, Performance Assessment Specifications provide methods and procedures to measure how well a Data instance represents an original Data entity. Performance of an Implementation includes methods and procedures for all or a subset of the following characteristics:

  1. Quality– for example, how well a Scene Descriptors instance represent a scene.
  2. Bias: – for example, how dependent on specific features of the training data is the inference represented by the Data instance.
  3. Legality– for example, whether the Data instance was produced in a jurisdiction at a time by an AIM that complies with the relevant a regulation, e.g., the European AI Act.
  4. Ethics – for example, the data instance complies to a target ethical standard.

Note that at this stage the AIH-HSP V1.0 specifies Performance Assessment only of some Data Types.

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