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

1. Technical Specification

Technical Specification: AI for Health (MPAI-AIH) – Health Secure Platform (AIH-HSP) V1.0 assumes that Workflows will be implemented based on Technical Specification: AI Framework (MPAI-AIF) V2.1.

Table 1 provides the list of AIWs specified by AIH-HSP V1.0. The links to the pages dedicated to each AI Workflow includes its Function, Reference Model, Input/Output Data, Functions of AIMs in the AIW, Input/Output Data of AIMs, and links to the AIW-related AIW, AIMs, and JSON metadata.

Table 1 – AIWs of AIH-HSP V1.0

Acronym. Title
AIH-HBE Health Back End
MMC-HFE Health Front End

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 demonstrate a working Implementation of an AIW, not to provide a ready-to-use product.
  2. MPAI disclaims the suitability of the Software for any other purposes than those of the MPAI-AIH 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 this Reference Software, e.g., by accepting the  licences from third-party repositories.

Note that this version of AIH-HSP does not include Reference Software.

3. Conformance Testing

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

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, and the Performance Assessment Specification should provide methods to measure how well an AIW performs its function, using a metric that depends on the nature of the function, such as:

  1. Quality: the Performance of an Answer to Question Module AIW can measure how well the AIW answers a question related to an image.
  2. Bias: Performance of an Answer to Question Module AIW can measure the quality of responses in dependence of the type of images.
  3. Legal compliance: the Performance of an AIW can measure the compliance of the AIW to a regulation, e.g., the European AI Act.
  4. Ethical compliance: the Performance Assessment of an AIW can measure the compliance of an AIW to a target ethical standard.

Note that this version of AIH-HSP does not include AIW Performance Assessment methods.

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