AI tools like large language models (LLMs) are making the evaluation of interventions for ageing easier, a new international study has found.
Research into ageing is producing an overwhelming amount of data, researchers say, as interventions like new medicines, dietary changes, or exercise routines are evaluated.
A study has explored how AI can analyse this 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 analyse complex biological data.
The researchers “told” LLMs about eight critical requirements for effective AI-based evaluations, including data accuracy, interpretability and causal mechanisms. Others requirements included the factoring in of diverse longitudinal large-scale data and emphasis on the known mechanisms of ageing.
The study spanned the National University of Singapore (NUS Medicine), and Rostock University Medical Center, Germany.
Professor Brian Kennedy fromNUS Medicine, who co-led the study, said: “We tested AI methods using real-world examples such as medicines and dietary supplements. We found that by following specific guidelines, AI can provide more accurate and detailed insights.
“For instance, when analysing rapamycin, a drug often studied for its potential to promote healthy ageing, the AI not only evaluated its efficacy but also provided context-specific explanations and caveats, such as possible side effects.”
Rostock’s Professor Georg Fuellen, co-leader of the study, said: “The study’s findings could have far-reaching effects.
“For healthcare, telling the AI about the critical requirements of a good response can enable it to find more effective treatments and make them safer to use.
“Generally, AI tools could design better clinical trials and help tailor health recommendations to each person. This research is a major step toward using AI to improve health outcomes for everyone, especially as they age.”
The researches are now focusing on a large-scale study of how to best prompt AI models for longevity-related intervention advice, to evaluate their accuracy and reliability for a range of benchmarks, that is, curated, high-quality data.
The validation of such AI systems is specifically important because the longevity interventions may then be implemented by a large number of healthy people.
Prospective studies will need to demonstrate that AI-based evaluations can accurately predict successful outcomes in human trials, paving the way for safer and more effective health interventions.
The team hopes to use their findings to make health and longevity interventions more precise and accessible, and ultimately improve the quality and duration of life.