WHO issues guidelines on ethics and governance of AI for health, specifically multimodal AI like ChatGPT

LMMs have been adopted faster than any consumer application in history, with several platforms like ChatGPT, Bard, and Bert making an appearance in 2023.

BySumit Jha

Published Jan 20, 2024 | 11:00 AM Updated Jan 20, 2024 | 11:00 AM

LMMs are unique in their mimicry of human communication and ability to carry out tasks they were not explicitly programmed to perform. (WHO)

Artificial Intelligence (AI), which entails the creation of computer systems capable of emulating human-like tasks such as learning, problem-solving, and decision-making, has seamlessly woven itself into the fabric of our daily existence. Its far-reaching impact extends to crucial domains like healthcare infrastructure.

A pivotal force within the realm of AI is the deployment of large multimodal models (LMMs).

LMMs can accept one or more types of data inputs, such as text, videos, and images, and generate diverse outputs not limited to the type of data inputed. LMMs are unique in their mimicry of human communication.

LMMs have been adopted faster than any consumer application in history, with several platforms like ChatGPT, Bard, and Bert entering the public consciousness in 2023.

In the healthcare sector, LMMs have emerged as indispensable tools for patient treatment and research.

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WHO guidelines

Recognising the burgeoning interest among professionals in harnessing LMMs for their work, the World Health Organization (WHO) has issued new guidelines on the ethics and governance of LMMs.

These guidelines address the ethical quandaries surrounding the rapid advancement of generative AI technology, particularly in its applications within the healthcare domain.

“Generative AI technologies have the potential to improve healthcare but only if those who develop, regulate, and use these technologies identify and fully account for the associated risks,” said Dr Jeremy Farrar, WHO Chief Scientist, in a statement.

“We need transparent information and policies to manage the design, development, and use of LMMs to achieve better health outcomes and overcome persisting health inequities,” he added.

The guidance document outlines over 40 recommendations for consideration by governments, technology companies, and healthcare providers to ensure the appropriate use of LMMs to promote and protect the health of populations.

“This document addresses the growing use of LMMs (including large language models like ChatGPT), which, for use in healthcare and medicine, are trained with highly diverse datasets, extending beyond text, and include biosensor, genomic, epigenomic, proteomic, imaging, clinical, social and environmental data. Therefore, LMMs can accept more than one type of input and generate outputs that are not limited to the type of data entered. LMMs are envisioned for diverse applications in healthcare and drug development,” as per the guidance document.

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Potential benefits, risks

The new WHO guidance outlines five broad applications of LMMs for health:

  • Diagnosis and clinical care, such as responding to patients’ written queries
  • Patient-guided use, such as for investigating symptoms and treatment
  • Clerical and administrative tasks, such as documenting and summarising patient visits within electronic health records
  • Medical and nursing education, including providing trainees with simulated patient encounters
  • Scientific research and drug development, including to identify new compounds

While LMMs are starting to be used for specific health-related purposes, there are also documented 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.

The guidance also details broader risks to health systems, such as accessibility and affordability of the best-performing LMMs. LMMS can also encourage “automation bias” by healthcare 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 healthcare more broadly.

To create safe and effective LMMs, WHO underlines the need for engagement of various stakeholders: governments, technology companies, healthcare providers, patients, and civil society, in all stages of development and deployment of such technologies, including their oversight and regulation.

“Governments from all countries must cooperatively lead efforts to effectively regulate the development and use of AI technologies, such as LMMs,” said Dr Alain Labrique, WHO Director for Digital Health and Innovation in the Science Division.

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Key recommendations

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 healthcare 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 healthcare 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, healthcare 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.