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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-FDL Federated Learn X
AIH-DPR AIH Data Process X AIH-GPT Genomic Processing Type X
AIH-HDP AIH Data Processing Types X AIH-GNO Genomics Omics Object X
AIH-AHT AIH Taxonomies X AIH-HLD Health Data X
AIH-ARD ARA Data X AIH-LCF Licence Confirm X
AIH-ARQ Audit X AIH-MIO Medical Image Object X
AIH-ECO Behavioural Signal Object X AIH-MPT Medical Image Processing Type X
AIH-BMD Biometric Data X AIH-MDL Model Licence X
AIH-BCL Blockchain Licence X AIH-NSQ Neurophysiological Signal Object X
AIH-CDF Common Definitions X AIH-NSQ Physiological Signal Object X
AIH-DIA De-ID and Anonym X AIH-REG Register X
AIH-CPT ECG Processing Type X AIH-TKN Tokens X
AIH-EPT EEG Processing Type X AIH-UPR User Profile X
AIH-EHQ EHR Object 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 cldata 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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