Generative Artificial Intelligence (AI): WHO issues new Guidance on governance of LMMs

WHO has issued new guidance on the ethics and governance of large multi-modal models (LMMs) for its appropriate use to promote and protect the health of populations. LMMs is a type of fast-growing generative artificial intelligence (AI) technology that has five broad applications for health in 

1. Diagnosis and clinical care, such as responding to patients’ written queries; 

2. Patient-guided use, such as for investigating symptoms and treatment; 

3. Clerical and administrative tasks, such as documenting and summarizing patient visits within electronic health records; 

4. Medical and nursing education, including providing trainees with simulated patient encounters, and; 

5. Scientific research and drug development, including to identify new compounds. 

However, these applications in healthcare run the risks of producing false, inaccurate, biased, or incomplete statements, which could harm people using such information in making health decisions. Furthermore, LMMs may be trained on data that are of poor quality or biased, whether by race, ethnicity, ancestry, sex, gender identity, or age. There are also broader risks to health systems, such as accessibility and affordability of the best performing LMMs. LMMs can also encourage ‘automation bias’ by health care professionals and patients, whereby errors are overlooked that would otherwise have been identified or difficult choices are improperly delegated to a LMM. LMMs, like other forms of AI, are also vulnerable to cybersecurity risks that could endanger patient information or the trustworthiness of these algorithms and the provision of health care more broadly. 

Therefore, to create safe and effective LMMs, WHO has made recommendations for governments and developers of LMMs. 

Governments 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. 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. 

· 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. 



WHO 2024. Ethics and governance of artificial intelligence for health: guidance on large multi-modal models. Available at 


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