Large Language Models In Healthcare

Context: 

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

WHO Releases  on The Ethics and Governance Of AI For Health

• WHO called for a collaborative approach involving governments, technology companies, healthcare providers, patients and civil society, in all stages of LMM development and deployment.

Large Language Models (LLMS)

• About: Large Language Models (LLMs) are a type of artificial intelligence (AI) algorithm that uses deep learning techniques and massively large data sets to perform a variety of natural language processing (NLP) tasks to understand, summarize, generate and predict new content.
  • They  are also referred to as neural networks (NNs), as they are in effect  computing systems inspired by the human brain.

• Applications Large Language Models In Healthcare: 

  • Diagnosis and clinical care, such as responding to patients’ written queries.
  • Patient-guided use for investigating symptoms and treatments
  • Clerical and administrative tasks in electronic health records
  • Medical and nursing education with simulated patient encounters
  • Scientific research and drug development.

Who Guidance on The Ethics and Governance Of AI For Health

• Core Principles: 

• • Protect autonomy
  • promote human well-being, human safety, and the public interest;
  • ensure transparency, explainability, and intelligibility;
  • foster responsibility and accountability; 
  • ensure inclusiveness and equity;
    promote AI that is responsive and sustainable.

• Concerns and Risks: 

  • Data bias: The data  used to train AI may be biased, generating misleading or inaccurate information that could pose risks to health, equity and inclusiveness
  • Automation Bias: Large language models (LLM) generate responses that can appear authoritative and plausible to an end user; however, these responses may be completely incorrect or contain serious errors, especially for health-related responses.
  • Infringement on the Right to Privacy: The  data gathered may be without requiring consent of the owner and the protection of  sensitive data (including health data) that a user provides to an application is also not guaranteed.
  • Disinformation: It can be misused to generate and disseminate highly convincing disinformation in the form of text, audio or video content that is difficult for the public to differentiate from reliable health content.

• Key Recommendations: 

• • Stakeholder Approach: Developers engage in stakeholders, including potential users and healthcare professionals, from the early stages of AI development. It also recommends designing LMMs for well-defined tasks with necessary accuracy and understanding potential secondary outcomes.
  • Investing in public infrastructure, like computing power and public datasets, that adhere to ethical principles
  • Using laws and regulations to ensure LMMs meet ethical obligations and human rights standards
  • Assigning regulatory agencies to assess and approve LMMs for healthcare use
  • Introducing mandatory post-release audits and impact assessments
  • Policy-makers should ensure patient safety and protection while technology firms work to commercialize LLMs.

Also Read: 

• Role of Artificial Intelligence in Healthcare
• New Delhi AI Declaration

News Source: Down to Earth

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