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The WHO has released guidelines for ethical use of Large Multi-Modal Models (LMMs) in healthcare, emphasizing risk evaluation, collaborative development, and regulation. Highlighting applications in diagnosis, patient care, and research, it calls for transparency, equity, and ethical governance to mitigate risks like bias and misinformation, urging global cooperation for effective AI regulation.

The World Health Organization (WHO) has issued comprehensive guidelines for the ethical use and governance of Large Multi-Modal Models (LMM) in healthcare. These advanced generative AI technologies, such as ChatGPT, Bard, and Bert, have rapidly transformed healthcare delivery and medical research by processing diverse data inputs like text, videos, and images. Despite their potential benefits, WHO emphasizes the critical need to carefully evaluate the risks associated with LMM adoption.

Applications of LMMs in Healthcare

The WHO document identifies five key applications of LMMs in healthcare, including diagnosis and clinical care, patient-guided use, administrative tasks, medical education, and scientific research. However, risks such as the generation of false or biased statements and issues related to data quality and bias are noted.

Recommendations and Concerns

WHO urges a collaborative approach involving governments, technology companies, healthcare providers, patients, and civil society in all stages of LMM development. Key recommendations include investing in public infrastructure, using regulations to ensure ethical obligations, and introducing mandatory post-release audits. The organization also stresses the importance of global cooperation to effectively regulate AI technologies.

Ethical Principles and Governance

WHO’s guidance builds upon six core principles: protecting autonomy, promoting human well-being, ensuring transparency, fostering responsibility, ensuring inclusiveness and equity, and promoting responsive and sustainable AI. The guidelines highlight the necessity of ethical considerations and governance in AI for health, addressing concerns such as biased data, misleading information, and potential misuse of LMMs.

InnoHEALTH magazine digital team

Author InnoHEALTH magazine digital team

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