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Evaluating the Use of Generative Artificial Intelligence in Health Technology Assessment: Opportunities, Limitations, and Policy Considerations

Q: How can HTA agencies and policymakers ensure the responsible and equitable integration of generative AI tools, particularly in addressing the needs of underserved populations?

To ensure the responsible and equitable integration of generative AI tools in health technology assessment (HTA), agencies and policymakers must adopt a multi-faceted approach that prioritizes inclusivity and fairness. First, it is essential to develop clear guidance that outlines the appropriate use of large language models (LLMs) and other generative AI technologies in HTA processes. This guidance should include specific examples of acceptable and unacceptable applications, particularly in relation to underserved populations. Second, HTA agencies should actively engage with diverse stakeholders, including representatives from marginalized communities, to understand their unique needs and perspectives. This engagement can help identify potential biases in AI models that may arise from historical data underrepresentation. By incorporating feedback from these communities, agencies can work towards creating more equitable AI systems that do not perpetuate existing disparities in healthcare access and outcomes. Third, investing in training for the HTA workforce is crucial. This training should focus on the ethical implications of using generative AI, emphasizing the importance of health equity in decision-making processes. Additionally, agencies should implement monitoring and evaluation frameworks to assess the impact of generative AI tools on different population groups, ensuring that any adverse effects are promptly addressed. Finally, collaboration with other regulatory bodies and multi-stakeholder groups can facilitate the harmonization of standards and processes, promoting transparency and accountability in the use of generative AI in HTA. By prioritizing these strategies, HTA agencies can foster a responsible and equitable integration of generative AI tools that effectively addresses the needs of underserved populations.

Core Concepts

Generative AI technologies, including large language models, have the potential to transform evidence generation methods used in health technology assessments, but their use requires careful evaluation and human oversight due to limitations in scientific validity, bias, and regulatory/ethical considerations.

Abstract

This article provides an overview of the applications and limitations of using generative AI, including large language models (LLMs), in the context of health technology assessment (HTA).

The authors first review the history and development of AI, highlighting the emergence of generative AI and foundation models as a transformative shift in the field. They then examine the potential applications of generative AI in three key areas of HTA:

Literature reviews and evidence synthesis: Generative AI can assist in automating aspects of systematic literature reviews, such as proposing search terms, screening abstracts, extracting data, and generating code for meta-analyses. However, limitations include the potential for inaccuracies, fabrications, and challenges in ensuring reproducibility.
Real-world evidence (RWE) generation: Generative AI can facilitate the automation of processes and analysis of large collections of real-world data, including unstructured clinical notes and imaging. Limitations include the risk of inaccuracies, biases, and privacy concerns.
Health economic modeling: Generative AI can aid in the development of health economic models, from conceptualization to validation. While it can improve efficiency, significant human expertise is still required to ensure accuracy and reliability.

The authors then discuss the broader limitations of using generative AI, including challenges related to scientific validity and reliability, bias and equity concerns, and regulatory and ethical considerations. They emphasize the importance of human oversight and the fast-evolving nature of these tools.

The article also provides an overview of the current policy landscape, highlighting the efforts of governments, regulatory bodies, and multi-stakeholder groups to develop guidance and frameworks for the responsible use of AI, including generative AI, in healthcare. The authors suggest that HTA agencies should establish clear guidance, harmonize standards, and invest in training to responsibly integrate generative AI into their assessment processes.

In conclusion, while generative AI holds promise for HTA applications, its use requires careful evaluation and human oversight to address the current limitations and ensure the responsible and effective integration of these technologies.

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Stats

Generative AI has the potential to assist in automating aspects of systematic literature reviews, including proposing search terms, screening abstracts, extracting data, and generating code for meta-analyses.
Generative AI can facilitate the automation of processes and analysis of large collections of real-world data, including unstructured clinical notes and imaging.
Generative AI can aid in the development of health economic models, from conceptualization to validation, but significant human expertise is still required to ensure accuracy and reliability.

Quotes

"Generative AI employs sophisticated ML models, particularly a class of deep neural networks, that can generate language, images, and code in response to free text prompts provided by users."
"Despite their potential, current generative AI applications are in their early stages and present limitations, including issues of scientific validity and reliability, risk of bias and impact on equity, and regulatory and ethical considerations."
"To ensure the responsible use and implementation of these tools, both developers and users of research must fully understand their limitations, including challenges related to scientific validity and reliability, risks of bias, potential impacts on equity, and critical regulatory and ethical considerations."

Key Insights Distilled From

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

by Rachael Fleu... at arxiv.org 09-24-2024

https://arxiv.org/pdf/2407.11054.pdf

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

Deeper Inquiries

How can HTA agencies and policymakers ensure the responsible and equitable integration of generative AI tools, particularly in addressing the needs of underserved populations?

To ensure the responsible and equitable integration of generative AI tools in health technology assessment (HTA), agencies and policymakers must adopt a multi-faceted approach that prioritizes inclusivity and fairness. First, it is essential to develop clear guidance that outlines the appropriate use of large language models (LLMs) and other generative AI technologies in HTA processes. This guidance should include specific examples of acceptable and unacceptable applications, particularly in relation to underserved populations.
Second, HTA agencies should actively engage with diverse stakeholders, including representatives from marginalized communities, to understand their unique needs and perspectives. This engagement can help identify potential biases in AI models that may arise from historical data underrepresentation. By incorporating feedback from these communities, agencies can work towards creating more equitable AI systems that do not perpetuate existing disparities in healthcare access and outcomes.
Third, investing in training for the HTA workforce is crucial. This training should focus on the ethical implications of using generative AI, emphasizing the importance of health equity in decision-making processes. Additionally, agencies should implement monitoring and evaluation frameworks to assess the impact of generative AI tools on different population groups, ensuring that any adverse effects are promptly addressed.
Finally, collaboration with other regulatory bodies and multi-stakeholder groups can facilitate the harmonization of standards and processes, promoting transparency and accountability in the use of generative AI in HTA. By prioritizing these strategies, HTA agencies can foster a responsible and equitable integration of generative AI tools that effectively addresses the needs of underserved populations.

What are the potential long-term implications of widespread adoption of generative AI in healthcare, and how can we mitigate unintended consequences?

The widespread adoption of generative AI in healthcare has the potential to transform various aspects of the system, including health technology assessment, clinical decision-making, and patient care. However, this transformation comes with several long-term implications that must be carefully considered.
One significant implication is the risk of exacerbating existing health disparities. If generative AI models are trained on biased datasets, they may produce outputs that favor certain populations over others, leading to inequitable healthcare delivery. To mitigate this risk, it is essential to ensure that training datasets are diverse and representative of the populations they serve. Implementing strategies such as data augmentation and federated learning can help create more balanced datasets and reduce bias in AI outputs.
Another potential consequence is the challenge of maintaining scientific validity and reliability. As generative AI tools become more integrated into healthcare processes, there is a risk that reliance on these technologies may overshadow the importance of human expertise and oversight. To address this, it is crucial to establish robust validation and verification protocols for AI-generated outputs, ensuring that human experts remain accountable for the quality and accuracy of results.
Additionally, the ethical implications of using generative AI in healthcare must be carefully navigated. Issues related to data privacy, informed consent, and the potential for AI to inadvertently reinforce stereotypes or biases need to be addressed proactively. Policymakers should develop comprehensive regulatory frameworks that prioritize ethical considerations and protect patient rights.
In summary, while the adoption of generative AI in healthcare presents exciting opportunities, it is vital to implement strategies that mitigate unintended consequences. By focusing on equity, scientific rigor, and ethical standards, stakeholders can harness the benefits of generative AI while minimizing potential risks.

How might the use of generative AI in HTA submissions impact the decision-making process and the overall healthcare system, and what measures should be taken to maintain transparency and accountability?

The integration of generative AI in health technology assessment (HTA) submissions has the potential to significantly impact the decision-making process and the overall healthcare system. By automating aspects of evidence synthesis, data extraction, and economic modeling, generative AI can enhance the efficiency and accuracy of HTA processes. However, this integration also raises important concerns regarding transparency and accountability.
One major impact of using generative AI in HTA submissions is the potential for improved evidence generation. AI tools can analyze vast amounts of real-world data and literature more quickly than human reviewers, leading to faster decision-making and potentially more timely access to innovative therapies for patients. However, this speed must not come at the expense of thoroughness and accuracy. Therefore, it is essential to implement rigorous validation processes to ensure that AI-generated outputs are reliable and scientifically sound.
To maintain transparency, HTA agencies should establish clear guidelines for reporting the use of generative AI in submissions. This includes documenting the methodologies employed, the data sources used, and the rationale behind AI-generated conclusions. By promoting transparency in the AI application process, stakeholders can foster trust in the results and ensure that decision-makers are fully informed about the strengths and limitations of the evidence presented.
Accountability is another critical aspect that must be addressed. As generative AI tools become more prevalent, it is vital to clarify the roles and responsibilities of human experts in the decision-making process. HTA agencies should emphasize that while AI can augment human capabilities, ultimate accountability for the quality and accuracy of assessments lies with human researchers and decision-makers. This can be reinforced through training programs that educate HTA professionals on the ethical implications of using AI and the importance of maintaining oversight.
In conclusion, while the use of generative AI in HTA submissions can enhance the efficiency and effectiveness of the decision-making process, it is crucial to implement measures that ensure transparency and accountability. By establishing clear guidelines, promoting rigorous validation, and emphasizing human oversight, HTA agencies can harness the benefits of generative AI while safeguarding the integrity of the healthcare system.