Requirements for Software AI

December 4, 2019

1. Cybersecurity

  • Risk Management Plan

  • Cybersecurity Awareness Principle (encryption at rest, HTTPS,etc.)

  • Evidence of The Surveillance Process

  • Timely Detection and Management of Cybersecurity Threat

2. Software Version Control

 

3. Clarify if the software incorporates Machine Learning (ML) based algorithm. If yes, please provide the following information:

  • Input data (data type such as image, patient age, patient’s hospital records, physiological signals, etc.)

  • Rationale and justification for selection of the source and size of the training and validation date sets used.

  • Additional intervals for training data update cycle (e.g in months or years) within the projected useful life.

  • Briefly describe the machine learning system, the model and the algorithm used.

  • Provide the rationale and selection process of the particular ML model (e.g. based on cross-validation method).

  • The input data (data type and size) used to generate the corresponding output.

  • The source and size of training and validation data sets used, including rationale for selection.

  • The performance of the ML system output (e.g. accuracy, sensitivity, specificity), if any. If the output is in a range, please provide the output in range.

  • The test protocol and report for verification and validation of the machine learning system, including the acceptance limits.

  • The interval for training data update cycle (e.g. in months or years), if applicable.

  • The procedure or plan implemented to trace the software version for subsequent iterations.

  • Product Owner’s intended clinical workflow. [The clinical workflow should include the any human intervention (if any) recommended by the Product Owner.]

 

 

 

 

 

 

 

 

 

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