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Large multimodal models: boon or burden for low- and middle-income countries
Rohit Malpani1, Keymanthri Moodley2
1World Health Organization, Research for Health Department, Avenue Appia 20, Geneva 1211, Switzerland.
International Health
|January 9, 2025
Summary
Large multimodal models, a type of generative artificial intelligence (AI), can advance universal health coverage. Proactive ethical considerations and governance are crucial for the responsible design and deployment of these powerful AI tools.
Area of Science:
- Artificial Intelligence
- Public Health
- Bioethics
Background:
- Generative artificial intelligence (AI), specifically large multimodal models (LMMs), presents opportunities for global health initiatives.
- Achieving universal health coverage (UHC) is a key global health objective.
- Ethical considerations in AI deployment are paramount for equitable access and outcomes.
Purpose of the Study:
- To explore the potential of LMMs in supporting universal health coverage.
- To identify and address the ethical challenges associated with LMMs in healthcare.
- To provide guidance on governing LMMs for effective and ethical use in public health.
Main Methods:
- Review of current AI capabilities in multimodal data processing.
- Analysis of ethical frameworks relevant to AI in healthcare.
- Synthesis of World Health Organization (WHO) recommendations on AI governance.
Main Results:
- LMMs offer significant potential to enhance healthcare delivery and accessibility.
- Key ethical risks include data privacy, algorithmic bias, and accountability.
- WHO guidance emphasizes a risk-benefit approach and robust governance structures.
Conclusions:
- Integrating LMMs into healthcare requires careful ethical navigation.
- Proactive governance is essential to harness LMM benefits for UHC.
- WHO guidance provides a framework for responsible AI innovation in global health.
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