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1. Technical Specification 2. Reference Software 3. Conformance Testing 4. Performance Assessment

1. Technical Specifications

Table 1 provides the links to the specifications and the JSON schemas of all AIMs specified by Technical Specification: AI for Health (MPAI-AIH) – Health Secure Platform (AIH-HSP) V1.0.

Table 1 – Specifications and JSON syntax of AIMs used by MPAI-MMC V2.3

AIMs Name JSON AIMs Name JSON
AIH-ADP AIH Data Processing X AIH-DIA De-Identification and Anonymisation X
AIH-ARA Anomaly and Risk Alert X AIH-HDM Health Data Multiplexing X
AIH-ADT Auditing X AIH-HDP Health Data Processing X
AIH-AAC Authentication and Access Control X AIH-HFF Health Federated Learning X
AIH-DSA Data Storage and Access X

2. Reference Software

As a rule, MPAI provides Reference Software implementing the AI Modules released with the BSD-3-Clause licence and the following disclaimers:

  1. The purpose of the Reference Software is to provide a working Implementation of an AIM, not a ready-to-use product.
  2. MPAI disclaims the suitability of the Reference Software for any other purposes than those of the MPAI-MMC Standard, and does not guarantee that it offers the best performance and that it is secure.
  3. Users shall verify that they have the right to use any third-party software required by the Reference Software, e.g., by accepting the  licences from third-party repositories.

Note that at this stage only part of the MPAI-MMC AIMs have a Reference Software Implementation.

3. Conformance Testing

An implementation of an AI Module conforms with MPAI-AIHif it accepts as input and produces as output Data and/or Data Objects (combination of Data of a certain Data Type and its Qualifier) conforming with those specified by MPAI-AIH.

The Conformance of an instance of a Data is to be expressed by a sentence like “Data validates against the Data Type Schema”. This means that:

  • Any Data Sub-Type is as indicated in the Qualifier.
  • The Data Format is indicated by the Qualifier.
  • Any File and/or Stream have the Formats indicated by the Qualifier.
  • Any Attribute of the Data is of the type or validates against the Schema specified in the Qualifier.

The method to Test the Conformance of a Data or Data Object instance is specified in the Data Types chapter.

4. Performance Assessment

Performance is a multidimensional entity because it can have various connotations. Therefore, the Performance Assessment Specification should provide methods to measure how well an AIM performs its function, using a metric that depends on the nature of the function, such as:

  1. Quality: Performance Assessment measures how well an AIM performs its function, using a metric that depends on the nature of the function.
  2. Bias: Performance Assessment measures how well an AIM performs its function, using a metric that depends on a bias related to certain attributes of the AIM.
  3. Legal compliance: Performance Assessment measures how well an AIM performs its function, using a metric that assesses its accordance with a certain legal standard
  4. Ethical compliance: the Performance Assessment of an AIM can measure the compliance of an AIM to a target ethical standard.

The current AIH-XRV V1.0 Standard does not provide AIM Performance Assessment methods.

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