Validation requirements for AI-based intervention-evaluation in aging and longevity research and practice.

Area of Science:

• Gerontology and Longevity Research
• Biomedical Data Science
• Artificial Intelligence in Healthcare

Background:

• Aging research generates vast datasets, necessitating advanced analytical tools.
• Evaluating geroprotective interventions requires comprehensive, explainable, and causality-aware approaches.
• Current data analysis often relies on canonical biomedical databases, limiting comprehensive interpretation.

Purpose of the Study:

• To propose the use of Artificial Intelligence (AI), specifically Large Language Models (LLMs), for evaluating geroprotective interventions.
• To outline requirements for AI-driven evaluations, including correctness, utility, comprehensiveness, explainability, causality, interdisciplinarity, and adherence to standards.
• To advocate for AI's role in interpreting biomarker and outcome changes beyond traditional database comparisons.

Main Methods:

• Leveraging Large Language Models (LLMs) integrated with Knowledge Graphs.
• Employing dedicated workflows such as Retrieval-Augmented Generation (RAG).
• Developing and applying specific query requirements to enhance LLM response quality.

Main Results:

• AI, particularly LLMs with Knowledge Graphs and RAG, can facilitate comprehensive analysis of aging data.
• Incorporating specific requirements into LLM queries improves the quality and reliability of AI-generated insights.
• Naive reliance on AI without these enhancements can be detrimental.

Conclusions:

• Informed application of LLMs, guided by specific analytical requirements, is crucial for advancing longevity research.
• Benchmarking efforts are needed to validate and optimize AI tools for evaluating interventions.
• AI holds significant potential to aid in personalized longevity strategies and understanding aging biology.