Appendix A — AI principles proposed by select organizations

This list is adapted from Badal, Lee, and Esserman (2023), Table 1.

• Ethics and governance of artificial intelligence for health, World Health Organization
  • Human autonomy
  • Human well-being and safety and the public interest
  • Transparency, explainability, and intelligibility
  • Responsibility and accountability
  • Inclusiveness and equity
  • Responsive and sustainable
• Ministries of Health, Medical AI algorithm assessment checklist, FUTURE-AI (an international, multi-stakeholder consortium)
  • Fairness
  • Universality
  • Traceability
  • Usability
  • Robustness
  • Explainability
• Good Machine Learning Practice for Medical Device Development: Guiding Principles, fdFDA, Health Canada, United Kingdom’s Medicines and Healthcare products Regulatory Agency (MHRA)
  • Leverage multidisciplinary expertise in development
  • Implement good software engineering and security practices
  • Datasets are representative of intended population
  • Training and test sets are independent
  • Reference datasets are well developed
  • Optimize performance of Human-AI Team
  • Thorough clinical testing
  • Information accessible to users
  • Monitor deployed models and mitigate retraining risk
• Defining AMIA’s artificial intelligence principles, American Medical Informatics Association (AMIA)
  • Autonomy
  • Beneficence
  • Non-maleficence
  • Justice
  • Explainability
  • Interpretability
  • Fairness
  • Dependability
  • Auditability
  • Knowledge managemen

Badal, Kimberly, Carmen M Lee, and Laura J Esserman. 2023. “Guiding Principles for the Responsible Development of Artificial Intelligence Tools for Healthcare.” Communication & Medicine 3 (1): 47. https://doi.org/10.1038/s43856-023-00279-9.