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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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VJ Periyakoil, MD

Scientific Methods

Pilot Studies

Validity and Reliability

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Welcome to the Stanford SAGE Research Center

Opening remarks by Lloyd Minor, MD, Dean, Stanford School of Medicine during Stanford SAGE Research Center Inauguration Event

SAGE Center Awardees

Monique Cano, PhD

Clinical Assistant Professor Department of Psychological Science, The University of Texas Rio Grande Valley Dr. Monique T. Cano is a Licensed Clinical Psychologist and received her Ph.D. in clinical psychology from Palo Alto University in 2020. She completed her NIH T32 Postdoctoral Fellowship in Substance Use Disorders Treatment and Services Research at the University of […]

Ngan F. Huang, PhD 

Associate Professor Department of Cardiothoracic Surgery Department of Chemical Engineering (by courtesy) https://profiles.stanford.edu/ngan-huang Ngan F. Huang is an Associate Professor in the Department of Cardiothoracic Surgery at Stanford University and Principal Investigator at the Veterans Affairs Palo Alto Health Care System. Dr. Huang completed her BS in Chemical Engineering from the Massachusetts Institute of Technology, […]

Gen Shinozaki, MD

Associate Professor Department of Psychiatry and Behavioral Sciences https://profiles.stanford.edu/gen-shinozaki Dr. Gen Shinozaki earned his MD degree from the University of Yamanashi, Japan. He has a Master’s degree in quantum optics from the University of Tokyo and in bioinformatics from the University of Iowa. He completed his psychiatry residency and consultation-liaison psychiatry fellowship both at Mayo […]

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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).