A collaborative study between researchers from the Yong Loo Lin School of Medicine, National University of Singapore (NUS Medicine), and the Institute for Biostatistics and Informatics in Medicine and aging Research, Rostock University Medical Center, Germany, investigated how advanced AI tools, like Large Language Models (LLMs), can make it easier to evaluate interventions for aging and provide personalised recommendations. The findings were published in the leading review journal aging Research Reviews.
Research into aging is producing an overwhelming amount of data, making it difficult to determine which interventions-such as new medicines, dietary changes, or exercise routines-are safe and effective. This study investigated how AI can analyze data more efficiently and accurately, by proposing a comprehensive set of standards for AI systems to ensure they deliver accurate, reliable, and understandable evaluations through their ability to analyze complex biological data.
The researchers identified eight critical requirements for effective AI-based evaluations:
Correctness of the evaluation results. Data quality will be assessed for accuracy. Usefulness and comprehensiveness. Interpretability and explainability of the evaluation results. Clarity and conciseness of the results and the given explanations. Specific consideration of causal mechanisms affected by the intervention. Consideration of data in a holistic context: Efficacy and toxicity, and evidence for the existence of a large therapeutic window; Analyses in an “interdisciplinary” setting. Enabling reproducibility, standardisation, and harmonization of the analyses (and of the reporting). Specific emphasis on diverse longitudinal large-scale data. Specific emphasis on results that relate to known mechanisms of aging.