WHO outlines considerations for regulation of artificial intelligence for
health
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   The World Health Organization (WHO) has released a new publication
   listing key regulatory considerations on artificial intelligence (AI)
   for health. The publication emphasizes the importance of establishing
   AI systems’ safety and effectiveness, rapidly making appropriate
   systems available to those who need them, and fostering dialogue among
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   progress in analytic techniques – whether machine learning, logic-based
   or statistical – AI tools could transform the health sector. WHO
   recognizes the potential of AI in enhancing health outcomes by
   strengthening clinical trials; improving medical diagnosis, treatment,
   self-care and person-centred care; and supplementing health care
   professionals’ knowledge, skills and competencies. For example, AI
   could be beneficial in settings with a lack of medical specialists,
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   However, AI technologies – including large language models – are being
   rapidly deployed, sometimes without a full understanding of how they
   may perform, which could either benefit or harm end-users, including
   health-care professionals and patients. When using health data, AI
   systems could have access to sensitive personal information,
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   “Artificial intelligence holds great promise for health, but also comes
   with serious challenges, including unethical data collection,
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   Tedros Adhanom Ghebreyesus, WHO Director-General. “This new guidance
   will support countries to regulate AI effectively, to harness its
   potential, whether in treating cancer or detecting tuberculosis, while
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   In response to growing country needs to responsibly manage the rapid
   rise of AI health technologies, the publication outlines six areas for
   regulation of AI for health.
     * To foster trust, the publication stresses the importance of
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     * Externally validating data and being clear about the intended use
       of AI helps assure safety and facilitate regulation.
     * A commitment to data quality, such as through rigorously evaluating
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   AI systems are complex and depend not only on the code they are built
   with but also on the data they are trained on, which come from clinical
   settings and user interactions – for example. Better regulation can
   help manage the risks of AI amplifying biases in training data.

   For example, it can be difficult for AI models to accurately represent
   the diversity of populations, leading to biases, inaccuracies or even
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   and regulatory authorities can follow to develop new guidance or adapt
   existing guidance on AI at national or regional levels.

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   Regulatory considerations on artificial intelligence for health
     * Regions