Link: https://www.nature.com/articles/s41746-023-00873-0
Title: The Imperative for Regulatory Oversight of Large Language Models (or Generative AI) in Healthcare
Author(s): Bertalan Meskó & Eric J. Topol
Word count: 2,222
Estimated average read time: 10 minutes
Summary: This article emphasizes the need for regulatory oversight of large language models (LLMs) in healthcare. LLMs, such as GPT-4 and Bard, have the potential to revolutionize healthcare, but they also pose risks that must be addressed. The authors argue for differentiated regulation of LLMs in comparison to other AI-based medical technologies due to their unique characteristics and challenges.
The article discusses the scale, complexity, hardware requirements, broad applicability, real-time adaptation, societal impact, and data privacy concerns associated with LLMs. It highlights the need for a tailored regulatory approach that considers these factors. The authors also provide insights into the current regulatory landscape, particularly in the context of the United States' Food and Drug Administration (FDA), which has been adapting its framework to address AI and machine learning technologies in medical devices.
The authors propose practical recommendations for regulators, including the creation of a new regulatory category for LLMs, guidelines for deployment, consideration of future iterations with advanced capabilities, and focusing on regulating the companies developing LLMs rather than each individual model.
Evaluation for Applicability to Applications Development: This article provides valuable insights into the challenges and considerations regarding regulatory oversight of large language models in healthcare. While it specifically focuses on healthcare, the principles and recommendations discussed can be applicable to application development using large language models or generative AI systems in various domains.
Developers working on applications utilizing large language models should consider the potential risks and ethical concerns associated with these models. They should be aware of the need for regulatory compliance and the importance of transparency, fairness, data privacy, and accountability in their applications.
The proposed recommendations for regulators can also serve as a guide for developers, helping them shape their strategies for responsible and compliant development of applications using large language models. Understanding the regulatory landscape and actively addressing potential risks and challenges can lead to successful deployment and use of these models in different applications.
