IAS Gyan

Daily News Analysis


20th January, 2024 Health

Copyright infringement not intended

Picture Courtesy: www.downtoearth.org.in

Context: The World Health Organization (WHO) has released comprehensive guidance on the ethical use and governance of large multi-modal models (LMM) in healthcare.


  • The World Health Organization (WHO) has issued comprehensive guidelines focusing on the ethical deployment and governance of large multi-modal models (LMM) in the healthcare sector.
  • LMMs, a form of artificial intelligence, are capable of processing and generating diverse types of data, such as text and images. These models are increasingly utilized in healthcare for various purposes, including disease diagnosis, treatment development, and patient care.

The WHO's guidance identifies five major applications of LMMs in healthcare:

  • Diagnosis and Clinical Care: LMMs can assist doctors in diagnosing diseases by analyzing medical data like X-rays and blood tests. Additionally, they can provide patients with information about their medical condition and treatment options.
  • Patient-Guided Use: LMMs can aid patients in understanding their symptoms and treatment options. They can offer personalized advice and support, enhancing patient engagement and education.
  • Clerical and Administrative Tasks: Automation of tasks like appointment scheduling and paperwork through LMMs can help healthcare professionals allocate more time to patient care.
  • Medical and Nursing Education: LMMs can create simulated patient encounters to facilitate medical and nursing students' learning experiences, allowing them to practice diagnosing and treating diseases in a controlled environment.
  • Scientific Research and Drug Development: In the realm of research, LMMs are employed to analyze vast datasets, aiding in the identification of new drug targets and the development of innovative treatments.

Risks Associated with LMMs in Healthcare

  • Generation of False or Biased Information: LMMs may produce inaccurate or biased information due to biases present in the training data, potentially leading to misguided health decisions.
  • Perpetuation of Disparities: LMMs might perpetuate healthcare disparities based on factors such as race, ethnicity, gender, and age, reflecting the biases present in their training data.
  • Automation Bias: There is a risk of automation bias, where healthcare professionals and patients may overly trust the output of LMMs, leading to the oversight of potential errors.
  • Cybersecurity Risks: As LMMs rely on trustworthy algorithms, any compromise in these algorithms could result in the release of sensitive patient information, posing cybersecurity risks.


The WHO Recommendations for Mitigating Risks

  • Investment in Public Infrastructure: Encourages investment in public infrastructure, such as computing power and datasets, adhering to ethical principles.
  • Legal Framework: Suggests the use of laws and regulations to ensure that LMMs meet ethical obligations and human rights standards.
  • Regulatory Approval: Recommends assigning regulatory agencies to assess and approve LMMs for healthcare use.
  • Post-Release Audits: Calls for the implementation of mandatory post-release audits and impact assessments.
  • Stakeholder Engagement: Advises developers to engage a diverse range of stakeholders, including potential users and healthcare professionals, from the early stages of AI development.
  • Task Design: Emphasizes designing LMMs for well-defined tasks with the necessary accuracy while understanding potential secondary outcomes.


  • The WHO's guidance is recognized as a positive step toward ensuring the safe and ethical use of LMMs in healthcare. By adhering to these recommendations, it is anticipated that LMMs can contribute to improving the quality of care for all patients.

Must Read Articles:

Multimodal Artificial Intelligence: https://www.iasgyan.in/daily-current-affairs/multimodal-artificial-intelligence


Q. How is artificial intelligence being utilized to enhance and transform healthcare practices, and what specific applications or advancements have shown promise in improving patient outcomes and overall healthcare efficiency?