AI tools show the potential to improve aging interventions and recommendations

AI tools show the potential to improve aging interventions and recommendations

A collaborative study between researchers from the Yong Loo Lin School of Medicine, the National University of Singapore (NUS Medicine) and the Institute of Biostatistics and Informatics in Medicine and Aging Research, Rostock University Medical Center, Germany, examined how advanced AI tools such as large-language models (LLMs) can make it easier to evaluate interventions for aging and provide personalized recommendations. The results were published in the Leading Review JournalAlter research reviews.

Research into aging generates an overwhelming amount of data, making it difficult to determine which interventions, such as new medications, dietary changes, or exercise routines, are safe and effective. This study examined how AI can analyze data more efficiently and accurately by proposing a comprehensive set of standards for AI systems to ensure that they provide accurate, reliable and understandable assessments through their ability to analyze complex biological data.

The researchers identified eight critical requirements for effective AI-based assessments:

1. Richtigkeit der Bewertungsergebnisse. Die Datenqualität wird auf Genauigkeit bewertet.
2. Nützlichkeit Und Vollständigkeit.
3. Interpretierbarkeit Und Erklärung der Bewertungsergebnisse. Klarheit und Übersicht über die Ergebnisse und die gegebenen Erklärungen.
4. Spezifische Überlegung von Kausalmechanismen von der Intervention betroffen.
5. Berücksichtigung von Daten in a ganzheitlich Kontext:
   1. Wirksamkeit und Toxizität sowie Beweise für die Existenz eines großen therapeutischen Fensters;
   2. Analysen in einer „interdisziplinären“ Umgebung.
6. Aktivieren ReproduzierbarkeitAnwesend StandardisierungUnd Harmonisierung der Analysen (und der Berichterstattung).
7. Spezifische Betonung auf verschiedene Längsschnittdaten in Längsrichtung.
8. Spezifische Betonung der Ergebnisse, die sich auf Bekannte Mechanismen des Alterns.

Having LLMs tell stories with these requirements as part of the prompt improved the quality of the recommendations they generated.

We tested AI methods on real-world examples such as pharmaceuticals and dietary supplements. We found that by following specific guidelines, AI can provide more accurate and detailed insights. When analyzing rapamycin, a drug that has been widely studied for its potential to promote healthy aging, the AI ​​assessed not only its effectiveness but also context-specific explanations and caveats as possible. “

Professor Brian Kennedy, Co-Leader of Studies, Department of Biochemistry and Physiology and Healthy Longevity Transmission Research Programme, NUS Medicine

“The results of the study could have far-reaching implications” The critical requirements for a good response may make it possible to find more effective treatments and make them safer. Improving health outcomes for everyone, especially as they age. “

Moving forward, the team is now focused on a large study of how best to use AI models for longevity intervention advice to assess their accuracy and reliability on a wide range of carefully designed benchmarks, curated, high-quality data. The validation of such AI systems is explicitly important because the longevity interventions can then be implemented by large numbers of healthy people. Prospective studies must demonstrate that AI-based assessments can accurately predict successful outcomes in human studies, 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, ultimately improving the quality and length of life. Collaboration between researchers, clinicians and policymakers will be important to establish robust regulatory frameworks and ensure the safe and effective use of AI-driven assessments.

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