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Guiding AI’s Role in Healthcare
The potential benefits of LMMs in healthcare are vast. They can be used for diagnosis and clinical care, patient-guided use, clerical and administrative tasks, medical and nursing education, and scientific research and drug development. However, there are also risks associated with the use of LMMs. They can produce false, inaccurate, biased, or incomplete statements. These could harm individuals relying on this information for their health decisions. LMMs may also be trained on poor quality or biased data, leading to potential disparities in healthcare outcomes.
The guidance also highlights broader risks to health systems, such as the accessibility and affordability of the best-performing LMMs. There is a concern that healthcare professionals and patients may develop “automation bias,” relying too heavily on LMMs and overlooking errors or improperly delegating difficult choices to these technologies. Additionally, LMMs are vulnerable to cybersecurity risks, which could compromise patient information and the trustworthiness of these algorithms.
Ensuring a multi-stakeholder framework of AI
To ensure the safe and effective use of LMMs, the WHO emphasizes the need for engagement from various stakeholders, including governments, technology companies, healthcare providers, patients, and civil society. Governments have a primary responsibility to set standards for the development and deployment of LMMs, including their oversight and regulation. The guidance recommends that governments invest in public infrastructure and provide access to computing power and public data sets while adhering to ethical principles and values. WHO’s Guidance of Ethics and Governance AI Usage can be considered as an important framework of universal applicability.
Developers of LMMs should involve potential users and stakeholders, including medical providers, researchers, healthcare professionals, and patients, in the design process. LMMs should be designed to perform well-defined tasks with accuracy and reliability, improving the capacity of health systems.