Ethics and Governance of Artificial Intelligence for Health: Guidance on Large Multi-Modal Models

The new WHO guidance includes recommendations for governments, who have the primary responsibility to set standards for the development and deployment of LMMs, and their integration and use for public health and medical purposes. For example, governments should: - Invest in or provide not-for-profit or public infrastructure, including computing power and public data sets, accessible to developers in the public, private and not-for-profit sectors, that requires users to adhere to ethical principles and values in exchange for access. - Use laws, policies and regulations to ensure that LMMs and applications used in health care and medicine, irrespective of the risk or benefit associated with the AI technology, meet ethical obligations and human rights standards that affect, for example, a person’s dignity, autonomy or privacy. - Assign an existing or new regulatory agency to assess and approve LMMs and applications intended for use in health care or medicine – as resources permit. - Introduce mandatory post-release auditing and impact assessments, including for data protection and human rights, by independent third parties when an LMM is deployed on a large scale. The auditing and impact assessments should be published and should include outcomes and impacts disaggregated by the type of user, including for example by age, race or disability. The guidance also includes the following key recommendations for developers of LMMs, who should ensure that: - LMMs are designed not only by scientists and engineers. Potential users and all direct and indirect stakeholders, including medical providers, scientific researchers, health care professionals and patients, should be engaged from the early stages of AI development in structured, inclusive, transparent design and given opportunities to raise ethical issues, voice concerns and provide input for the AI application under consideration. - LMMs are designed to perform well-defined tasks with the necessary accuracy and reliability to improve the capacity of health systems and advance patient interests. Developers should also be able to predict and understand potential secondary outcomes