Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

ISO/TR 24291:2021 - Health informatics - Applications of machine learning technologies in imaging and other medical applications



This document lists examples of and defines categories of use cases for machine learning in medicine for clinical practice. The developments and applications of machine learning technologies for artificial intelligence consist of 

  • 1) data collection and curation, 
  • 2) pre-processing, 
  • 3) model training and validation, and 
  • 4) medicine depending on various kinds of specialty including radiology, pathology, emergency medicine, dermatology, ophthalmology, anaesthesia, surgery, etc., and clinical settings including repeated detection and/or diagnosis, real-time monitoring, and treatment prediction. 

This document covers categories applications of medicine in (4). It also defines the clinical usages and necessities of the artificial intelligence in medicine. (1) to (3) are not the scope of this document This document also excludes:

  • basic research and other scientific areas,
  • use cases related to artificial intelligence methods other than machine learning (for example, symbolic artificial intelligence, expert systems), and
  • non-human results such as veterinary medicine. 

© British Standards Institution 2023

The information about this standard has been compiled by the AI Standards Hub, an initiative dedicated to knowledge sharing, capacity building, research, and international collaboration in the field of AI standards. You can find more information and interactive community features related to this standard by visiting the Hub’s AI standards database here. To access the standard directly, please visit the developing organisation’s website.

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Tags:

  • Data collection
  • Data processing

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Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.