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Stanford Aging and Ethnogeriatrics Research Center (SAGE Center)

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SAGE awardees

Travis Shivley-Scott, PhD

Clinical Neuropsychologist,
VA Northern California Health Care System

Dr. Shivley-Scott is a clinical neuropsychologist. He received a Master’s in Clinical Psychology at California State University, Northridge and a PhD in Clinical Psychology from Fordham University. He completed a Clinical Psychology Internship at the San Francisco VA Medical Center in 2019 and then conducted a dual postdoctoral research fellow appointment at Stanford University in Psychiatry and Behavioral Sciences and at the VA Palo Alto Sierra Pacific Mental Illness Research, Education, and Clinical Center (MIRECC). His research focused on understanding the impact of environmental and biological factors that influence neuroanatomical substrates associated with mood regulation in traumatic brain injury and older adults.

SAGE Project: Cross-National Differences in the Interaction between Vascular Burden, Depressive Symptoms, and Cognition in Older Adults in Mexico and the United States

Vascular conditions and depression are each associated with poor cognitive outcomes in older adults from Mexico and the United States (US), and Mexican older adults may be especially susceptible to worse cognitive functioning related to these factors. This study examined cross-national differences in the interaction between vascular burden and depressive symptoms on cognitive outcomes because using two large publicly available databases evaluating cognitive aging in Mexico and the US .

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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).