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How will 'Chat-IRB' impact research ethics review in LMICs?

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Large language models (LLMs) offer efficiency for research ethics committees (RECs) but face challenges in resource-limited settings. Addressing data quality, confidentiality, and unique ethical considerations is crucial for equitable AI integration in global research review.

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Area of Science:

  • Medical Ethics
  • Artificial Intelligence in Research
  • Global Health

Background:

  • Generative artificial intelligence (AI) and large language models (LLMs) are increasingly discussed in research, with their use in research ethics review being particularly debated.
  • A recent paper by Porsdam Mann et al. provides a balanced perspective on application-specific LLMs for Research Ethics Committees (RECs).

Purpose of the Study:

  • To evaluate the potential benefits and challenges of using LLMs in the ethics review of research protocols, especially in resource-constrained settings.
  • To highlight specific concerns for low- and middle-income countries (LMICs) regarding LLM implementation in RECs.

Main Methods:

  • The study discusses the implications of LLM use based on existing literature and expert considerations.
  • It analyzes challenges related to infrastructure, data, and ethical nuances in diverse global research contexts.

Main Results:

  • LLMs can enhance the speed and efficiency of REC processes globally.
  • Significant challenges exist in resource-limited settings, including data availability/quality, linguistic diversity, digital divide, and data confidentiality.
  • Nuanced ethical considerations unique to LMICs and the risk of automation bias require careful management.

Conclusions:

  • While LLMs offer potential advantages for RECs, their implementation in resource-constrained settings requires addressing substantial infrastructural and ethical challenges.
  • Future applications should focus on mitigating risks, ensuring data protection, and exploring LLMs for capacity development in research ethics review, particularly in LMICs.