Ensuring Trustworthiness in Generative AI for Clinical Evidence Summarization

แนวคิดหลัก

Developing accountable, fair, and inclusive generative AI models is crucial for trustworthy clinical evidence synthesis to improve healthcare decisions.

บทคัดย่อ

The article discusses the challenges and recommendations for achieving trustworthy generative AI in the context of clinical evidence synthesis. It highlights the following key points:

Accountability:

• Generative AI models can produce factually incorrect outputs due to differences between the included clinical trials and the meta-analysis summary.
• Comprehensive training data, reliable meta-analysis implementation, and advanced evaluation metrics are needed to ensure the accountability of AI-generated summaries.
• Retrieval-augmented generation can help mitigate parametric knowledge bias in the models.

Causality:

• Generative AI holds promise in assisting causal inference tasks, but its capacity for true causal reasoning remains an open research question.

Transparency:

• The complexity of large language models poses challenges in understanding how summaries are generated, highlighting the need for transparent and interpretable AI systems.
• Involving diverse stakeholders and developing models with baked-in transparency structures are crucial for achieving trustworthy evidence synthesis.

Fairness:

• Generative AI can propagate biases present in the training data, which is particularly concerning in the healthcare domain.
• Strategies to mitigate biases, such as prompt engineering and fairness assessments, are necessary to ensure fair and inclusive evidence synthesis.

Generalizability:

• Domain-specific language models and techniques for processing long inputs are important for improving the generalizability of generative AI in evidence synthesis.

Data Privacy and Governance:

• Careful consideration of patient privacy and data governance is essential when utilizing patient information in generative AI models.

Patient Safety:

• Reliable and resilient generative AI systems, integrated with human experts, are crucial to ensure patient safety when using AI-generated evidence summaries.

Lawfulness and Regulation:

• Generative AI for evidence synthesis must adhere to relevant laws and regulations to safeguard patients, clinicians, and AI developers.

สถิติ

"Evidence-based medicine promises to improve the quality of healthcare by empowering clinical decisions and practices with the best available evidence."
"The rapid growth of clinical evidence, which can be obtained from various sources, poses a challenge in collecting, appraising, and synthesizing the evidential information."
"Systematic reviews are designed to offer a statistical synthesis of the results of eligible clinical studies, rather than merely replicating them verbatim."
"Comprehensive training data is critical for developing accountable models to summarize clinical evidence."
"Reliable implementation of automatic meta-analysis workflow is critical to assure the correctness of statistically synthesized effect measures and their corresponding accuracy."
"LLMs may still "exhibit unpredictable failure modes [in causal reference]"."
"Modern LLMs are typically based on neural models, which consist of multiple layers of interconnected neurons, and the relationship between input and output can be highly complex."
"The presence of biases in the LLM's training data can also perpetuate or amplify these biases in its analysis and summaries."
"Machine learning (ML) models may perform poorly on under-represented groups" in real-world evidence.
"Even though there is a trend toward increasing the maximum length of input tokens, the extended context window may still need to be adequately long to encompass all clinical trials or notes involved in an evidence summary."

คำพูด

"Faithful or factual AI refers to systems that generate content that is factually accurate so that it is exchangeable."
"Intrinsic hallucinations refer to cases where "the generated output contradicts the input source." By contrast, extrinsic hallucinations occur when the generated output can "neither be supported nor be contradicted by the source.""
"Parametric knowledge bias is a conspicuous problem [27]. This occurs when models depend on their intrinsic parametric knowledge, which is built up during training, rather than the information provided in the input source when generating summaries."
"In healthcare, it is critical that the systems are transparent due to their proximity to human lives and that patients understand how clinicians use these recommendations."
"Even if individual-level records cannot be recovered from an LLM, it may also be possible that a patient can be detected as a contributor to the training data [63]."

ข้อมูลเชิงลึกที่สำคัญจาก

Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness

by Gongbo Zhang... ที่ arxiv.org 04-02-2024

https://arxiv.org/pdf/2311.11211.pdf

สอบถามเพิ่มเติม

How can generative AI be leveraged to assist in the systematic review process beyond just summarizing clinical trials, such as in the screening, data extraction, and quality assessment stages?

Generative AI can play a crucial role in various stages of the systematic review process beyond just summarizing clinical trials. In the screening stage, AI algorithms can be utilized to automate the initial screening of a large volume of studies based on predefined criteria. Natural language processing (NLP) models can quickly analyze abstracts or full texts to determine their relevance to the research question, thereby expediting the screening process. This can significantly reduce the burden on human reviewers and improve efficiency.
In the data extraction stage, generative AI can assist in extracting relevant information from the selected studies. By training models to identify and extract key data points such as study design, participant characteristics, interventions, outcomes, and results, AI can streamline the data extraction process. This can help ensure accuracy and consistency in data extraction across multiple studies, reducing the likelihood of errors.
Furthermore, in the quality assessment stage, generative AI can aid in evaluating the methodological quality of included studies. AI models can be trained to assess the risk of bias in clinical trials based on predefined criteria or guidelines. By automating this process, AI can help standardize the quality assessment process and identify potential sources of bias more efficiently than manual assessments.
Overall, leveraging generative AI in the systematic review process can enhance the speed, accuracy, and consistency of screening, data extraction, and quality assessment tasks. By automating these labor-intensive processes, AI can free up researchers' time to focus on higher-level tasks such as data interpretation and synthesis, ultimately improving the quality and reliability of systematic reviews.

What are the potential ethical and legal implications of using generative AI to make high-stakes healthcare decisions, and how can these be addressed?

The use of generative AI in making high-stakes healthcare decisions raises several ethical and legal implications that need to be carefully considered and addressed. One major concern is the potential for bias in AI algorithms, which can lead to unfair or discriminatory outcomes, especially if the training data is biased or unrepresentative of the population. Biases in AI systems can result in unequal access to healthcare services, misdiagnoses, or inappropriate treatment recommendations.
Another ethical consideration is the lack of transparency and interpretability in AI decision-making processes. Generative AI models, particularly large language models, operate as black boxes, making it challenging to understand how they arrive at their conclusions. This lack of transparency can undermine trust in AI systems and raise concerns about accountability and responsibility for decisions made by these models.
From a legal standpoint, using generative AI in healthcare decision-making may raise issues related to data privacy and security. Patient data used to train AI models must be handled in compliance with data protection regulations to ensure patient confidentiality and prevent unauthorized access or misuse of sensitive information. Additionally, healthcare providers and organizations using AI systems must adhere to legal requirements regarding informed consent, data sharing, and data protection.
To address these ethical and legal implications, it is essential to prioritize fairness, transparency, accountability, and data privacy in the development and deployment of generative AI systems in healthcare. This can be achieved through rigorous testing and validation of AI algorithms to detect and mitigate biases, implementing explainable AI techniques to enhance transparency, establishing clear guidelines for data governance and privacy protection, and ensuring ongoing monitoring and evaluation of AI systems to uphold ethical standards and legal compliance.

How can the development of trustworthy generative AI for clinical evidence synthesis be integrated with broader efforts to improve the transparency and accountability of AI systems in healthcare?

The development of trustworthy generative AI for clinical evidence synthesis can be integrated with broader efforts to improve the transparency and accountability of AI systems in healthcare through several key strategies:

Standardization and Regulation: Establishing industry standards and regulatory frameworks for AI development and deployment in healthcare can promote transparency and accountability. Compliance with standards such as the General Data Protection Regulation (GDPR) and adherence to ethical guidelines can ensure that AI systems are developed and used responsibly.

Explainable AI: Incorporating explainable AI techniques into generative AI models can enhance transparency by providing insights into how decisions are made. By making AI systems more interpretable, stakeholders can better understand the reasoning behind AI-generated outputs, fostering trust and accountability.

Ethical Guidelines and Governance: Implementing ethical guidelines and governance structures specific to AI in healthcare can guide the responsible development and use of AI systems. Ethical review boards, data ethics committees, and AI oversight bodies can help ensure that AI applications align with ethical principles and patient rights.

Collaboration and Stakeholder Engagement: Engaging diverse stakeholders, including patients, healthcare providers, policymakers, and AI developers, in the development process can promote transparency and accountability. By involving stakeholders in decision-making and governance, AI systems can be designed to meet the needs and expectations of all parties involved.

Continuous Monitoring and Evaluation: Regular monitoring and evaluation of AI systems for clinical evidence synthesis are essential to assess performance, detect biases, and ensure compliance with ethical and legal standards. Ongoing audits, reviews, and feedback mechanisms can help identify and address issues proactively.

By integrating these strategies into the development of generative AI for clinical evidence synthesis, healthcare organizations can build trustworthy AI systems that prioritize transparency, accountability, and ethical use. This holistic approach can contribute to the responsible adoption of AI in healthcare and foster public trust in AI technologies.