Added section about Special considerations for AI projects and deployment #7
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| - **Adaptability**: Design systems capable of deployment across various regions and adaptable to different public health contexts. | ||
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| ### 5. Monitoring and Maintenance | ||
| - **Performance Monitoring**: Continuously monitor models for performance degradation, especially in dynamic health environments. |
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Potential reference:
World Health Organization (WHO). Ethics and governance of artificial intelligence for health [Internet]. 2021 June 28; ISBN 9789240029200. Available from: https://iris.who.int/bitstream/handle/10665/341996/9789240029200-eng.pdf
"Even after an AI technology has been introduced into a health-care system, its impact should be evaluated continuously during its real-world use, as should the performance of an algorithm if it learns from data that are different from its training data." (p33)
| - **Regular Audits**: Conduct periodic audits to ensure models remain aligned with public health objectives and ethical standards. | ||
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| ### 6. Stakeholder Engagement and Collaboration | ||
| - **Inclusive Development**: Engage public health experts, community representatives, and end-users throughout the development process. |
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Potential reference:
Gurevich E, El Hassan B, El Morr C. Equity within AI systems: What can health leaders expect?. InHealthcare Management Forum 2023 Mar (Vol. 36, No. 2, pp. 119-124). Sage CA: Los Angeles, CA: SAGE Publications.
"The implementation of AI in projects must be a collaborative effort. It should include physicians, patients, and communities from diverse backgrounds of social, cultural, and economic contexts."
| - **Security Measures**: Implement robust encryption and access controls to protect sensitive health information. | ||
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| ### 2. Ethical AI Implementation | ||
| - **Bias Mitigation**: Assess and address potential biases that could affect vulnerable populations, ensuring equitable health outcomes. |
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Potential reference:
Raza S. Connecting Fairness in Machine Learning with Public Health Equity. arXiv preprint arXiv:2304.04761. 2023 Apr 8.
"The connection between fair ML and public health equity is critical, as the use of unbiased ML models can aid in the reduction of existing health disparities and promote equal access to healthcare services. We can effectively analyse and address the underlying factors that contribute to health inequities in various population groups by ensuring that ML algorithms are fair and unbiased. Fair ML models can be used to identify at-risk populations, optimise resource allocation, and tailor public health interventions to specific community needs." (p5)
| ### 6. Stakeholder Engagement and Collaboration | ||
| - **Inclusive Development**: Engage public health experts, community representatives, and end-users throughout the development process. | ||
| - **Communication**: Maintain transparency with stakeholders to explain AI decisions and build trust. | ||
| - **Training and Education**: Provide training for public health practitioners to effectively utilize AI tools, enhancing their impact. |
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Reference:
Kueper JK, Rosella LC, Booth RG, Davis BD, Nayani S, Smith MJ, Lizotte D. Inaugural Artificial Intelligence for Public Health Practice (AI4PHP) Retreat: Ontario, Canada.
"Despite excitement and perceived opportunities for AI to support public health, a key barrier is that many local public health units do not currently have the capacity or resources for dedicated AI-related work. The retreat ended with a discussion about future steps and how to foster public health leadership and presence in AI. "
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| ### 6. Stakeholder Engagement and Collaboration | ||
| - **Inclusive Development**: Engage public health experts, community representatives, and end-users throughout the development process. | ||
| - **Communication**: Maintain transparency with stakeholders to explain AI decisions and build trust. |
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Check out:
Information and Privacy Commissioner of Ontario (IPC). Artificial Intelligence in the public sector: Building trust now and for the future [Internet]. 2024 Feb 1; Available from: https://www.ipc.on.ca/artificial-intelligence-in-the-public-sector-building-trust-now-and-for-the-future/
| - **Alignment with Public Health Principles**: Ensure AI systems promote community well-being and adhere to ethical standards. | ||
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| ### 3. Model Lifecycle Management | ||
| - **Version Control**: Maintain systematic versioning of datasets, code, and models to track changes and updates. |
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I don't disagree, but note that data versioning is not something discussed in this documentation (yet).
| Implementing AI in public health demands attention to specific challenges and best practices: | ||
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| ### 1. Data Privacy and Security | ||
| - **Compliance**: Adhere to data protection regulations (e.g., PHIPA, HIPAA) and public health standards. |
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I may leave HIPAA out since it is for the US.
The public health standards IIRC do not outline data privacy or security explicitly, but instead discuss data-driven decision making within various standards.
We could add a reference however to adhere to any agency-specific policies on data management, privacy and security.
| ### 7. Legal and Regulatory Compliance | ||
| - **Regulatory Adherence**: Stay informed about and comply with relevant laws and regulations governing AI use in public health. | ||
| - **Ethical Guidelines**: Follow established ethical guidelines, such as those from the World Health Organization, to ensure responsible AI deployment. |
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This can perhaps be removed and integrated into section 1 and 2
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