Validation requirements for AI-based intervention-evaluation in aging and longevity research and practice.
Abstract
The field of aging and longevity research is overwhelmed by vast amounts of data, calling for the use of Artificial Intelligence (AI), including Large Language Models (LLMs), for the evaluation of geroprotective interventions. Such evaluations should be correct, useful, comprehensive, explainable, and they should consider causality, interdisciplinarity, adherence to standards, longitudinal data and known aging biology. In particular, comprehensive analyses should go beyond comparing data based on canonical biomedical databases, suggesting the use of AI to interpret changes in biomarkers and outcomes. Our requirements motivate the use of LLMs with Knowledge Graphs and dedicated workflows employing, e.g., Retrieval-Augmented Generation. While naive trust in the responses of AI tools can cause harm, adding our requirements to LLM queries can improve response quality, calling for benchmarking efforts and justifying the informed use of LLMs for advice on longevity interventions.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper discusses the application of AI, particularly LLMs, in evaluating interventions related to aging and longevity research. It emphasizes the need for rigorous validation and comprehensive analysis in this context, which aligns with the goals of addressing the root causes of aging. However, while the topic is pertinent, the contributions appear to be more about methodological advancements rather than groundbreaking findings that significantly advance the field. Thus, it is rated as a solid but limited impact.
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