Machine Learning - Theory | Research taters • 1y ago • 100%

Curious Replay for Model-based Adaptation

**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.

Machine Learning - Theory | Research taters • 1y ago • 100%

The imperative for regulatory oversight of large language models (or generative AI) in healthcare

www.nature.com

**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 highlights the need for regulatory oversight of large language models (LLMs), such as GPT-4 and Bard, in healthcare settings. LLMs have the potential to transform healthcare by facilitating clinical documentation, summarizing research papers, and assisting with diagnoses and treatment plans. However, these models come with significant risks, including unreliable outputs, biased information, and privacy concerns. The authors argue that LLMs should be regulated differently from other AI-based medical technologies due to their unique characteristics, including their scale, complexity, broad applicability, real-time adaptation, and potential societal impact. They emphasize the importance of addressing issues such as transparency, accountability, fairness, and data privacy in the regulatory framework. The article also discusses the challenges of regulating LLMs, including the need for a new regulatory category, consideration of future iterations with advanced capabilities, and the integration of LLMs into already approved medical technologies. The authors propose practical recommendations for regulators to bring this vision to reality, including creating a new regulatory category, providing guidance for deployment of LLMs, covering different types of interactions (text, sound, video), and focusing on companies developing LLMs rather than regulating each iteration individually. Evaluation for Applicability to Applications Development: This article provides valuable insights into the regulatory challenges and considerations related to large language models in healthcare. While it primarily focuses on the medical field, the principles and recommendations discussed can be applicable to applications development using large language models or generative AI systems in various domains. Developers working on applications that utilize large language models should be aware of the potential risks and ethical concerns associated with these models. They should also consider the need for regulatory compliance and the importance of transparency, fairness, data privacy, and accountability in their applications. Additionally, developers may find the proposed practical recommendations for regulators helpful in shaping their own strategies for responsible and compliant development of applications using large language models. Understanding the regulatory landscape and being proactive in addressing potential risks and challenges can lead to the successful deployment and use of these models in various applications.

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