Resources & Education
The Healthy Longevity Medicine Society is determined to promote education in longevity medicine
The development of longevity-focused medical practices greatly depends on bridging the gap between health-care providers and interdisciplinary experts, such as academic biogerontologists, artificial intelligence experts, computer scientists, and informaticians. Health-care providers require strategic and credible educative approaches to upskill clinicians on longevity science and its translative implementation in the practice.

Patients have insufficient access to the health-care providers who have been adequately trained in longevity medicine and can manage a patient from a longevity medicine standpoint. Viable longevity education with practical translation will thus ultimately improve health-care systems worldwide and decrease disease occurrence by training health-care providers to tackle the most common and strongest contributor of disease—unhealthy aging. Longevity medicine combines best practices from various fields and uses leading-edge innovations, such as deep learning and artificial intelligence to evaluate the patient's biological age throughout the course of life. Longevity physicians are looking for ways to reduce the gap between the current parameters (current biological age) and the parameters of optimal maximum physical performance (the ideal biological age, predicted by deep learning).
An important aspect of advancing and maintaining quality in the development and application of longevity medicine is to set and promote professional standards. We encourage our members to suggest topics of interest for future professional standards and to take an active role in spearheading their establishment.
White Papers & Guidelines
The first HLMS white paper is planned for September 2022 and will define longevity medicine.
Longevity Medicine Course Series
Longevity Medicine Course Series at Longevity Education
Longevity medicine is a fast-evolving field and it is currently outpacing the ability of the medical practice to internalize and implement the progress of related research.

Modern advances in AI and machine learning, biomarker research and drug development have produced numerous tools for early diagnostics and prevention, which remain unknown to the global medical community. The paradigm of longevity and healthy aging as the top priority will undoubtedly greatly impact the primary, secondary and tertiary prevention rates. The Longevity Medicine 101 and 201 Courses are a pivotal curriculum that provides medical professionals with most recent advances in biogerontology, geroscience and precision medicine, while teaching the ways to implement this knowledge in the clinical practice.

The curriculum has been developed by interdisciplinary experts (physicians, academic biogerontologists, AI-experts, computer scientists, KOL in longevity), covering the necessary foundation and most recent scientific evidence.

This translational medicine course allowed over 5000 physicians so far to get certified and to gain the fundamentals in the field, including theories of aging, biomarkers of aging, molecular mechanisms, and geroprotector regimens. Courses such as this course serve to close the detrimental knowledge gap and provide an overview of clinical applications of recent advances in aging research, skills to evaluate the validity of biomarkers of aging and other biological age testing systems, and knowledge of the available longevity therapies to tackle diseases related to senescence-related processes in the organisms. It implies bridging biogerontology, machine learning, differential diagnosis, omics, geroprotective interventions, drug design, and health-care organisations informing advances in these research areas while giving examples of their implementation into clinical practice.
Healthy Longevity Medicine Society — International medical Society of Longevity Medicine promoting the highest standards of clinical practice and of interdisciplinary clinical and scientific collaboration
Year 2022, all rights reserved
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