Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models

Daniel Lopez-Martinez·June 24, 2024

Summary

The paper addresses the risks of off-label medical product promotion in generative AI models, particularly MLLMs, which can learn unapproved uses due to limited human oversight. The author proposes a method for detecting off-label promotion by analyzing AI outputs, using a case study with the MLLM GenAI. The method involves input standardization, named entity recognition, and checking for non-approved recommendations. The study finds that GenAI generates off-label indications in 15.4% of responses and achieves an 83.02% F1 score in off-label identification. It highlights the need for model developers to adhere to regulations and suggests post-hoc filtering for safer recommendations. The research also touches on the broader implications of AI in healthcare, including the potential benefits and concerns in areas like medical image analysis, drug labels, and disparities in resource-poor nations, emphasizing the importance of ethical considerations and regulation.

Key findings

2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of harmful medical product recommendations and off-label promotion in generative AI models . This problem is not entirely new but has gained significance due to the rapid advancements in generative artificial intelligence (GenAI) models, which have the potential to generate content and perform tasks beyond their original training . The paper proposes an approach to identify potentially harmful product recommendations and highlights the risks associated with unvetted recommendations from these models, emphasizing the importance of addressing public health concerns related to the use of such AI technologies in the medical field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the potential harms and regulatory breaches of generative AI models, specifically focusing on off-label recommendations in the medical field . The study utilizes an algorithm to detect instances of off-label promotion for pharmaceuticals and proposes the use of guardrails to monitor and filter responses generated by large language models to ensure they are safe and compliant . The research serves as a proof-of-concept work highlighting the need to address potential harms and regulatory issues associated with generative AI models in healthcare .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models" proposes several innovative ideas, methods, and models :

  • PaLM 2 Technical Report: The paper introduces the PaLM 2 technical report authored by Wu, Kelvin Xu, Yunhan Xu, and others in 2023 .
  • Model Card and Evaluations for Claude Models: It discusses the model card and evaluations for Claude models by Anthropic in 2023 .
  • Claude 3 Model Family: The paper presents the Claude 3 model family consisting of Opus, Sonnet, and Haiku, detailed in a technical report by Anthropic in 2024 .
  • Discovering Language Model Behaviors: It explores language model behaviors using Model-Written evaluations by Hume, Yuntao Bai, and others in 2022 .
  • AI-Assisted Red-Teaming: The paper introduces AART, an AI-Assisted Red-Teaming approach for new LLM-powered applications by Bhaktipriya Radharapu and team in 2023 .
  • Automated Detection of Off-Label Drug Use: It discusses automated detection of off-label drug use by Kenneth Jung, Paea LePendu, and others in 2014 .
  • Deciphering Clinical Abbreviations: The paper presents a privacy-protecting machine learning system for deciphering clinical abbreviations by Alvin Rajkomar and team in 2022 .
  • Med-BERT: It introduces Med-BERT, a model for disease prediction based on large-scale structured electronic health records by Laila Rasmy and colleagues in 2021 .
  • ChatGPT Applications in Healthcare Education: The study highlights the applications of ChatGPT in medical, dental, pharmacy, and public health education by Malik Sallam and team in 2023 .
  • Mayo Clinical Text Analysis System (cTAKES): The paper discusses the Mayo clinical text analysis and knowledge extraction system (cTAKES) by Guergana K Savova and team in 2010 .
  • Llama 2: Open Foundation and Fine-Tuned Chat Models: It introduces Llama 2, an open foundation and fine-tuned chat models by Hugo Touvron, Louis Martin, and others in 2023 .
  • Generalist Biomedical AI: The paper discusses progress towards generalist biomedical AI by Tu Tao, Azizi Shekoofeh, and others in 2024 .

These ideas, methods, and models contribute to the advancement of generative AI models in healthcare, particularly in addressing off-label promotion and ensuring safe and effective use of medical products. The paper "Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models" introduces a novel approach to identifying potentially harmful product recommendations generated by generative AI models, particularly focusing on off-label promotion . This approach offers several characteristics and advantages compared to previous methods, as detailed in the paper:

  • Identification of Off-Label Promotion: The proposed method aims to detect instances of off-label promotion by evaluating the output of a multimodal large language model (MLLM) that processes image and text inputs . This method involves sequential steps such as input standardization, named entity recognition, product and indication recognition, and off-label identification .

  • Post-Hoc Guardrails Implementation: The algorithm developed in the study can be utilized to introduce post-hoc guardrails that monitor and filter MLLM responses to make them harmless before presenting them to users . This feature enhances the safety and reliability of the generated recommendations by actively filtering out potentially harmful suggestions.

  • Proof-of-Concept Work: The paper acknowledges that the study is a proof-of-concept work that highlights potential harms and regulatory breaches related to generative AI models . Despite focusing on off-label promotion using Claude 3, the authors note the need for a more comprehensive evaluation across various MLLMs to draw conclusive insights .

  • Innovative Model Applications: The paper discusses the application of innovative models such as PaLM 2, Claude 3, and Llama 2 in the context of generative AI for healthcare tasks . These models demonstrate the advancement in modeling complex relationships from multimodal datasets and performing diverse medical tasks .

  • Ethical and Legal Considerations: The study emphasizes the ethical and legal challenges posed by generative AI models in healthcare, highlighting the importance of addressing public health risks and ensuring the safety and efficacy of medical product recommendations . This underscores the need for robust guardrails and monitoring mechanisms to mitigate potential harms.

By leveraging advanced methodologies and models, the proposed approach in the paper offers a comprehensive solution to detect and prevent off-label promotion in generative AI models, thereby enhancing the safety, reliability, and ethical standards of medical product recommendations generated by these models.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of generative AI models and medical product recommendations. Noteworthy researchers in this area include Kenneth Jung, Paea LePendu, William S Chen, Srini V Iyer, Ben Readhead, Joel T Dudley, Nigam H Shah, Faiza Khan Khattak, Serena Jeblee, Chloé Pou-Prom, Mohamed Abdalla, Christopher Meaney, Frank Rudzicz, Juyong Kim, Jeremy C Weiss, Pradeep Ravikumar, Tiffany H Kung, Morgan Cheatham, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, Victor Tseng, Yuki Kunitsu, Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang, among others .

The key to the solution mentioned in the paper revolves around guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models. These guardrails are designed to provide guidelines and restrictions to ensure that AI models do not make inappropriate or off-label medical product recommendations, thereby enhancing patient safety and regulatory compliance in healthcare settings .


How were the experiments in the paper designed?

The experiments in the paper were designed to focus on a shopping context where a user interacts with a MLLM model to find answers to product questions. In this setup, the user provides the model with a picture of a product label and asks a question about it, to which the model responds with a textual output . The study aimed to identify potentially harmful product recommendations by extracting FDA-approved indications from the FDALabel dataset and determining instances of off-label promotion based on the GenAI response . Additionally, the experiments implemented a red teaming approach based on human-generated templates populated with indications from the FDALabel database or disease names from ICD-10 to generate synthetic queries for evaluation .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the FDALabel database . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that require verification. The study utilized a red teaming approach based on human-generated templates to generate synthetic user queries, enabling the identification of instances of off-label promotion for a selection of pharmaceuticals . This method allows for the introduction of post-hoc guardrails to monitor and filter responses from the generative AI model, ensuring harmful recommendations are mitigated .

Furthermore, the experimental setup focused on a shopping context where users interacted with a large language model (LLM) to find answers to product-related questions, demonstrating the model's ability to generate textual outputs based on user queries . By extracting FDA-approved indications from the FDALabel dataset and zero-shot prompting a T5-large model to detect off-label recommendations, the study effectively evaluated the model's performance in identifying off-label drug uses .

Overall, the experiments conducted in the paper, along with the results obtained, offer substantial evidence to support the scientific hypotheses related to detecting off-label drug use and implementing safeguards to prevent harmful medical product recommendations in generative AI models . The methodologies employed in the study provide a robust framework for evaluating the performance and safety of AI models in the medical domain, contributing significantly to the advancement of AI-assisted healthcare technologies.


What are the contributions of this paper?

The paper makes several contributions:

  • It highlights potential harms and regulatory breaches related to generative AI models, specifically in the context of off-label promotion of pharmaceuticals .
  • The paper introduces an algorithm that identifies instances of off-label promotion for a selection of 35 pharmaceuticals, aiming to create guardrails that monitor and filter responses from large language models (LLMs) to ensure harmlessness .
  • The work serves as a proof-of-concept study, focusing on the detection of off-label recommendations using Claude 3 and other LLMs, with the need for further comprehensive evaluation to determine the prevalence of off-label recommendations .

What work can be continued in depth?

Further research in this area can delve deeper into the identification and monitoring of off-label drug promotion in generative AI models. By utilizing algorithms to detect instances of off-label promotion for pharmaceuticals, researchers can develop post-hoc guardrails to filter model responses and ensure they are harmless before being presented to users . Additionally, conducting a more comprehensive evaluation across various large language models (LLMs) beyond Claude 3 can provide insights into the prevalence of off-label recommendations and potential regulatory breaches . This continued work is crucial for addressing ethical and legal challenges, minimizing public health risks, and ensuring the safe and effective use of generative AI models in healthcare .


Introduction
Background
Emergence of generative AI, specifically MLLMs (Large Language Models) in healthcare
Limited human oversight in AI-generated medical content
Objective
To identify and mitigate off-label medical product promotion in AI models
Evaluate the proposed detection method for MLLMs
Method
Data Collection
Case study with MLLM: GenAI
Data generation: AI-generated medical responses
Data Preprocessing
Input standardization: Ensuring consistent input format for analysis
Named Entity Recognition (NER):
Identifying medical entities and concepts
Off-Label Criteria:
Defining the criteria for unapproved recommendations
Off-Label Detection
Analysis of AI outputs
Metrics: Off-label indication frequency and F1 score (83.02%)
Filtering algorithm: Post-hoc approach for safer recommendations
Results and Findings
Off-label promotion in GenAI: 15.4% of responses
Implications for model developers:
Adherence to regulations and guidelines
Case study implications:
Safeguarding patient care and information
Ethical and Regulatory Considerations
Medical Image Analysis
Balancing benefits and risks in diagnostic applications
Drug Labels and Disparities
Addressing potential biases and access issues in resource-poor nations
Ethical Frameworks for AI in Healthcare
Importance of transparency, accountability, and informed consent
Conclusion
The need for proactive measures to prevent off-label promotion
Future directions for research and model development
The role of collaboration between AI developers, regulators, and healthcare professionals
Basic info
papers
artificial intelligence
Advanced features
Insights
What percentage of responses from GenAI do the researchers find to contain off-label indications, and what is the F1 score for identifying these instances?
What recommendations does the paper make regarding model developers' adherence to regulations and the use of post-hoc filtering in AI for healthcare?
How does the author propose to detect off-label promotion in AI models, specifically using MLLMs like GenAI?
What are the risks associated with off-label medical product promotion in generative AI models discussed in the paper?

Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models

Daniel Lopez-Martinez·June 24, 2024

Summary

The paper addresses the risks of off-label medical product promotion in generative AI models, particularly MLLMs, which can learn unapproved uses due to limited human oversight. The author proposes a method for detecting off-label promotion by analyzing AI outputs, using a case study with the MLLM GenAI. The method involves input standardization, named entity recognition, and checking for non-approved recommendations. The study finds that GenAI generates off-label indications in 15.4% of responses and achieves an 83.02% F1 score in off-label identification. It highlights the need for model developers to adhere to regulations and suggests post-hoc filtering for safer recommendations. The research also touches on the broader implications of AI in healthcare, including the potential benefits and concerns in areas like medical image analysis, drug labels, and disparities in resource-poor nations, emphasizing the importance of ethical considerations and regulation.
Mind map
Importance of transparency, accountability, and informed consent
Addressing potential biases and access issues in resource-poor nations
Balancing benefits and risks in diagnostic applications
Filtering algorithm: Post-hoc approach for safer recommendations
Metrics: Off-label indication frequency and F1 score (83.02%)
Analysis of AI outputs
Defining the criteria for unapproved recommendations
Off-Label Criteria:
Identifying medical entities and concepts
Named Entity Recognition (NER):
Input standardization: Ensuring consistent input format for analysis
Data generation: AI-generated medical responses
Case study with MLLM: GenAI
Evaluate the proposed detection method for MLLMs
To identify and mitigate off-label medical product promotion in AI models
Limited human oversight in AI-generated medical content
Emergence of generative AI, specifically MLLMs (Large Language Models) in healthcare
The role of collaboration between AI developers, regulators, and healthcare professionals
Future directions for research and model development
The need for proactive measures to prevent off-label promotion
Ethical Frameworks for AI in Healthcare
Drug Labels and Disparities
Medical Image Analysis
Safeguarding patient care and information
Case study implications:
Adherence to regulations and guidelines
Implications for model developers:
Off-label promotion in GenAI: 15.4% of responses
Off-Label Detection
Data Preprocessing
Data Collection
Objective
Background
Conclusion
Ethical and Regulatory Considerations
Results and Findings
Method
Introduction
Outline
Introduction
Background
Emergence of generative AI, specifically MLLMs (Large Language Models) in healthcare
Limited human oversight in AI-generated medical content
Objective
To identify and mitigate off-label medical product promotion in AI models
Evaluate the proposed detection method for MLLMs
Method
Data Collection
Case study with MLLM: GenAI
Data generation: AI-generated medical responses
Data Preprocessing
Input standardization: Ensuring consistent input format for analysis
Named Entity Recognition (NER):
Identifying medical entities and concepts
Off-Label Criteria:
Defining the criteria for unapproved recommendations
Off-Label Detection
Analysis of AI outputs
Metrics: Off-label indication frequency and F1 score (83.02%)
Filtering algorithm: Post-hoc approach for safer recommendations
Results and Findings
Off-label promotion in GenAI: 15.4% of responses
Implications for model developers:
Adherence to regulations and guidelines
Case study implications:
Safeguarding patient care and information
Ethical and Regulatory Considerations
Medical Image Analysis
Balancing benefits and risks in diagnostic applications
Drug Labels and Disparities
Addressing potential biases and access issues in resource-poor nations
Ethical Frameworks for AI in Healthcare
Importance of transparency, accountability, and informed consent
Conclusion
The need for proactive measures to prevent off-label promotion
Future directions for research and model development
The role of collaboration between AI developers, regulators, and healthcare professionals
Key findings
2

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

The paper aims to address the issue of harmful medical product recommendations and off-label promotion in generative AI models . This problem is not entirely new but has gained significance due to the rapid advancements in generative artificial intelligence (GenAI) models, which have the potential to generate content and perform tasks beyond their original training . The paper proposes an approach to identify potentially harmful product recommendations and highlights the risks associated with unvetted recommendations from these models, emphasizing the importance of addressing public health concerns related to the use of such AI technologies in the medical field .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis related to the potential harms and regulatory breaches of generative AI models, specifically focusing on off-label recommendations in the medical field . The study utilizes an algorithm to detect instances of off-label promotion for pharmaceuticals and proposes the use of guardrails to monitor and filter responses generated by large language models to ensure they are safe and compliant . The research serves as a proof-of-concept work highlighting the need to address potential harms and regulatory issues associated with generative AI models in healthcare .


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models" proposes several innovative ideas, methods, and models :

  • PaLM 2 Technical Report: The paper introduces the PaLM 2 technical report authored by Wu, Kelvin Xu, Yunhan Xu, and others in 2023 .
  • Model Card and Evaluations for Claude Models: It discusses the model card and evaluations for Claude models by Anthropic in 2023 .
  • Claude 3 Model Family: The paper presents the Claude 3 model family consisting of Opus, Sonnet, and Haiku, detailed in a technical report by Anthropic in 2024 .
  • Discovering Language Model Behaviors: It explores language model behaviors using Model-Written evaluations by Hume, Yuntao Bai, and others in 2022 .
  • AI-Assisted Red-Teaming: The paper introduces AART, an AI-Assisted Red-Teaming approach for new LLM-powered applications by Bhaktipriya Radharapu and team in 2023 .
  • Automated Detection of Off-Label Drug Use: It discusses automated detection of off-label drug use by Kenneth Jung, Paea LePendu, and others in 2014 .
  • Deciphering Clinical Abbreviations: The paper presents a privacy-protecting machine learning system for deciphering clinical abbreviations by Alvin Rajkomar and team in 2022 .
  • Med-BERT: It introduces Med-BERT, a model for disease prediction based on large-scale structured electronic health records by Laila Rasmy and colleagues in 2021 .
  • ChatGPT Applications in Healthcare Education: The study highlights the applications of ChatGPT in medical, dental, pharmacy, and public health education by Malik Sallam and team in 2023 .
  • Mayo Clinical Text Analysis System (cTAKES): The paper discusses the Mayo clinical text analysis and knowledge extraction system (cTAKES) by Guergana K Savova and team in 2010 .
  • Llama 2: Open Foundation and Fine-Tuned Chat Models: It introduces Llama 2, an open foundation and fine-tuned chat models by Hugo Touvron, Louis Martin, and others in 2023 .
  • Generalist Biomedical AI: The paper discusses progress towards generalist biomedical AI by Tu Tao, Azizi Shekoofeh, and others in 2024 .

These ideas, methods, and models contribute to the advancement of generative AI models in healthcare, particularly in addressing off-label promotion and ensuring safe and effective use of medical products. The paper "Guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models" introduces a novel approach to identifying potentially harmful product recommendations generated by generative AI models, particularly focusing on off-label promotion . This approach offers several characteristics and advantages compared to previous methods, as detailed in the paper:

  • Identification of Off-Label Promotion: The proposed method aims to detect instances of off-label promotion by evaluating the output of a multimodal large language model (MLLM) that processes image and text inputs . This method involves sequential steps such as input standardization, named entity recognition, product and indication recognition, and off-label identification .

  • Post-Hoc Guardrails Implementation: The algorithm developed in the study can be utilized to introduce post-hoc guardrails that monitor and filter MLLM responses to make them harmless before presenting them to users . This feature enhances the safety and reliability of the generated recommendations by actively filtering out potentially harmful suggestions.

  • Proof-of-Concept Work: The paper acknowledges that the study is a proof-of-concept work that highlights potential harms and regulatory breaches related to generative AI models . Despite focusing on off-label promotion using Claude 3, the authors note the need for a more comprehensive evaluation across various MLLMs to draw conclusive insights .

  • Innovative Model Applications: The paper discusses the application of innovative models such as PaLM 2, Claude 3, and Llama 2 in the context of generative AI for healthcare tasks . These models demonstrate the advancement in modeling complex relationships from multimodal datasets and performing diverse medical tasks .

  • Ethical and Legal Considerations: The study emphasizes the ethical and legal challenges posed by generative AI models in healthcare, highlighting the importance of addressing public health risks and ensuring the safety and efficacy of medical product recommendations . This underscores the need for robust guardrails and monitoring mechanisms to mitigate potential harms.

By leveraging advanced methodologies and models, the proposed approach in the paper offers a comprehensive solution to detect and prevent off-label promotion in generative AI models, thereby enhancing the safety, reliability, and ethical standards of medical product recommendations generated by these models.


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

Several related research studies exist in the field of generative AI models and medical product recommendations. Noteworthy researchers in this area include Kenneth Jung, Paea LePendu, William S Chen, Srini V Iyer, Ben Readhead, Joel T Dudley, Nigam H Shah, Faiza Khan Khattak, Serena Jeblee, Chloé Pou-Prom, Mohamed Abdalla, Christopher Meaney, Frank Rudzicz, Juyong Kim, Jeremy C Weiss, Pradeep Ravikumar, Tiffany H Kung, Morgan Cheatham, Arielle Medenilla, Czarina Sillos, Lorie De Leon, Camille Elepaño, Maria Madriaga, Rimel Aggabao, Giezel Diaz-Candido, James Maningo, Victor Tseng, Yuki Kunitsu, Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, Jaewoo Kang, among others .

The key to the solution mentioned in the paper revolves around guardrails for avoiding harmful medical product recommendations and off-label promotion in generative AI models. These guardrails are designed to provide guidelines and restrictions to ensure that AI models do not make inappropriate or off-label medical product recommendations, thereby enhancing patient safety and regulatory compliance in healthcare settings .


How were the experiments in the paper designed?

The experiments in the paper were designed to focus on a shopping context where a user interacts with a MLLM model to find answers to product questions. In this setup, the user provides the model with a picture of a product label and asks a question about it, to which the model responds with a textual output . The study aimed to identify potentially harmful product recommendations by extracting FDA-approved indications from the FDALabel dataset and determining instances of off-label promotion based on the GenAI response . Additionally, the experiments implemented a red teaming approach based on human-generated templates populated with indications from the FDALabel database or disease names from ICD-10 to generate synthetic queries for evaluation .


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is the FDALabel database . The code for the study is not explicitly mentioned to be open source in the provided context.


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide valuable support for the scientific hypotheses that require verification. The study utilized a red teaming approach based on human-generated templates to generate synthetic user queries, enabling the identification of instances of off-label promotion for a selection of pharmaceuticals . This method allows for the introduction of post-hoc guardrails to monitor and filter responses from the generative AI model, ensuring harmful recommendations are mitigated .

Furthermore, the experimental setup focused on a shopping context where users interacted with a large language model (LLM) to find answers to product-related questions, demonstrating the model's ability to generate textual outputs based on user queries . By extracting FDA-approved indications from the FDALabel dataset and zero-shot prompting a T5-large model to detect off-label recommendations, the study effectively evaluated the model's performance in identifying off-label drug uses .

Overall, the experiments conducted in the paper, along with the results obtained, offer substantial evidence to support the scientific hypotheses related to detecting off-label drug use and implementing safeguards to prevent harmful medical product recommendations in generative AI models . The methodologies employed in the study provide a robust framework for evaluating the performance and safety of AI models in the medical domain, contributing significantly to the advancement of AI-assisted healthcare technologies.


What are the contributions of this paper?

The paper makes several contributions:

  • It highlights potential harms and regulatory breaches related to generative AI models, specifically in the context of off-label promotion of pharmaceuticals .
  • The paper introduces an algorithm that identifies instances of off-label promotion for a selection of 35 pharmaceuticals, aiming to create guardrails that monitor and filter responses from large language models (LLMs) to ensure harmlessness .
  • The work serves as a proof-of-concept study, focusing on the detection of off-label recommendations using Claude 3 and other LLMs, with the need for further comprehensive evaluation to determine the prevalence of off-label recommendations .

What work can be continued in depth?

Further research in this area can delve deeper into the identification and monitoring of off-label drug promotion in generative AI models. By utilizing algorithms to detect instances of off-label promotion for pharmaceuticals, researchers can develop post-hoc guardrails to filter model responses and ensure they are harmless before being presented to users . Additionally, conducting a more comprehensive evaluation across various large language models (LLMs) beyond Claude 3 can provide insights into the prevalence of off-label recommendations and potential regulatory breaches . This continued work is crucial for addressing ethical and legal challenges, minimizing public health risks, and ensuring the safe and effective use of generative AI models in healthcare .

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