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Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Consortium

Longevity & Healthy Aging Research Center

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About Us

Stanford Medicine’s Longevity and hEalthy Aging Research coNsortium (LEARN) is funded by the National Institute of Health and directed by Dr. VJ Periyakoil.

LEARN is a multi-organizational research consortium that seeks to advance longevity, and prosperity for all Americans. Waiting until you become 65 years of age to focus on your longevity, and quality of life while trying to prevent chronic illnesses is a recipe for failure. Prevention of many chronic illnesses is best done by adopting key health behaviors in our youth and middle age. This is a primary focus of the LEARN multi-institutional consortium.

LEARN is standing at the doorstep of discovering implementing and evaluating novel and innovative ways of radically changing and improving the aging experience and extending healthspan for everyone.

The LEARN scientists use the latest methodologies like machine learning, artificial intelligence, and other big data techniques, as well as innovative methods like virtual reality, wearables, digital interventions, and precision medicine approaches. Our consortium has numerous faculty who not only conduct research but also mentor numerous junior scientists.

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Next-Generation Artificial Intelligence for Diagnosis: From Predicting Diagnostic Labels to "Wayfinding"

Julia Adler-Milstein, PhD1; Jonathan H. Chen, MD, PhD; Gurpreet Dhaliwal, MD

Improving the diagnostic process is a quality and safety priority.With the digitization of health records and rapid expansion of health data, the cognitive demand on the diagnostician has increased. The use of artificial intelligence (AI) to assist human cognition has the potential to reduce this demand and associated diagnostic errors. However, current AI tools have not realized this potential, due in part to the long-standing focus of these tools on predicting final diagnostic labels instead of helping clinicians navigate the dynamic refinement process of diagnosis. This Viewpoint highlights the importance of shifting the role of diagnostic AI from predicting labels to “wayfinding” (interpreting context and providing cues that guide the diagnostician).