Monday, May 6th 6-8:30 pm
2024 Frank Le Fever Spring Conference via Zoom
AI and Emerging Technologies in Neuropsychology
Robert M. Bilder, PhD, ABPP-CN
The Future of Neuropsychology:
What is the End Game?
Liz Angoff, PhD
Practical Applications of AI in Neuropsychology
REGISTRATION at www.NYNG.org
Members: $50, which includes attendance & 3 CE credits*
Student Members: FREE
Non-members: $100, which includes attendance & 3 CE credits*
*New York Neuropsychology Group is recognized by the New York State Education Department's State Board for Psychology as an approved provider of continuing education for licensed psychologists #PSY-0107. New York Neuropsychology Group is approved by the American Psychological Association to sponsor continuing education for psychologists. NYNG maintains responsibility for this program and its content. CE credits will be awarded to paid registrants who attend the majority of the conference.
Refund Policy: Full refund will be provided if cancellation occurs up to 24 hours prior to the event. Cancellation requests made within 24 hours are non-refundable.
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The Future of Neuropsychology: What is the End Game?
Abstract: Clinical neuropsychology is poised for revolutionary changes in how information is acquired from individuals about their brains and how we use that information. The traditional methods of testing people using clinical “laboratory” procedures is inefficient, suboptimal in both ecological and biological validity, and relies on outdated methods for data collection and analysis. New methods to acquire data using ubiquitous sensing and data aggregation will displace the older methods, and new clinical methods will be developed to maximize the benefits of the new data. Old normative tables from cross-sectional standardization studies will be replaced by dynamically updated studies of diverse individuals, both healthy and with clinical conditions, and expected scores will be based on mathematical models that consider social and structural determinants of neuropsychological function in appropriate clinical context. Clinical decision support systems fueled by evidence-based algorithms operating with all the information available in electronic health records will replace the currently conventional clinician-centered approach to differential diagnosis and treatment recommendations. Clinicians will need to identify what uniquely human aspects of clinical neuropsychology continue to be done better by humans than by machines (e.g., patient centered, clinician-partnered care). Given the pace of change the time to consider the future is now.
Robert Bilder is the Tennenbaum Family Distinguished Professor of Psychiatry and Psychology, Chief of Psychology at UCLA Health, and Director of the Center for the Biology of Creativity at UCLA’s Semel Institute for Neuroscience and Human Behavior. He is a board-certified clinical neuropsychologist and directs training programs in Clinical Neuropsychology at UCLA. His research focuses on brain and behavior, with aims to eliminate artificial boundaries between mental health and illness, and between every day and exceptional creativity. His current NIH grants examine reward mechanisms and have established a National Neuropsychology Network to aggregate data on a large scale to help develop the next generation of neuropsychological assessment methods, data sharing and data harmonization. He has a long-standing interest in promoting innovation and technology, served as a member of the expert panel at the Minnesota Update Conference for neuropsychology and heads the Disruptive Technology Initiative for the American Academy of Clinical Neuropsychology. His team recently completed the “Big C” project to examine brain function in exceptional creativity and now directs a National Endowment for the Arts Research Lab to measure impact of the arts on well-being, and he co-leads the Mind Well pod of the UCLA Semel Healthy Campus Initiative Center to enhance the psychological well-being of students, staff and faculty across the campus.
Learning Objectives:
DEI: Current models of neuropsychological (NP) assessment suffer from both the methods lacking sensitivity to diversity and the interpretation based on stratified group norms that are outdated and often poorly tailored to understand individual patients. This presentation will highlight how future models can include more comprehensive and appropriate regressors to help estimate expected scores based on social and other contextual determinants of NP function. The presentation will also address the risks of algorithmic discrimination, and continued problems with the digital divide that may impact equitable care.
Financial disclosures: Dr. Bilder is supported in part by a grant from the NIMH (R01MH118514), the National Neuropsychology Network, that focused on data aggregation.
References:
Bilder, R. M., & Reise, S. P. (2019). Neuropsychological tests of the future: How do we get there from here? The Clinical Neuropsychologist, 33(2), 220-245.
Bilder, R. M., Widaman, K. F., Bauer, R. M., Drane, D., Loring, D. W., Umfleet, L. G., ... & Shih, S. (2023). Construct identification in the neuropsychological battery: What are we measuring? Neuropsychology, 37(4), 351.
Reise, S. P., Wong, E., Block, J., Widaman, K. F., Gullett, J. M., Bauer, R. M., ... & Bilder, R. M. (2023). Computerized adaptive test strategies for the matrix reasoning subtest of the Wechsler Adult Intelligence Scale, (WAIS-IV). Journal of the International Neuropsychological Society, 1-10.
Loring, D. W., Bauer, R. M., Cavanagh, L., Drane, D. L., Enriquez, K. D., Reise, S. P., ... & NNN Study Group. (2022). Rationale and design of the National Neuropsychology Network. Journal of the International Neuropsychological Society, 28(1), 1-11.
Abstract: AI offers the potential to transform our practice as psychologists. In this talk, we move beyond increasing efficiency, and address how we can use AI to increase accessibility and truly empower the patients and families we work with. Assessment is a complex process, full of technical terms, psychological jargon, and deficit language. This creates immense barriers for those we serve to fully understand their neuropsychological profile. AI offers a promising solution by helping us transform the language we use, to create positive and simple narratives, provide summaries that make it easier to understand the main points of our work, real-world examples, and compelling metaphors tailored to individual profiles. This session will help participants become familiar and comfortable with AI tools and strategies, to improve our ability to communicate testing results.
Liz Angoff, Ph.D. is a Licensed Educational Psychologist with a Diplomate in School Neuropsychology, providing assessment and consultation services to children and their families in the Bay Area, CA. She is the author of the Brain Building Books, tools for engaging children in understanding their learning and developmental differences as part of the assessment process. More information about Dr. Liz and her work is available at www.ExplainingBrains.com
DEI: The aim of this talk is to help practitioners use AI to make our work more accessible to neurodiverse families.
Financial disclosure: I am the publisher and author of The Brain Building Books. I receive a small compensation from BastionGPT for new subscribers, which supports the Brain Building Book donation program.
Irshad, S., Azmi, S., & Begum, N. (2022). The role of artificial intelligence in psychology: Current developments and future implications. Journal of Mental Health Practice, 15(2), 112-129.
Luxton, D.D. (2014). Artificial intelligence in psychological practice: Current and future applications and implications. Professional Psychology: Research and Practice, 45, 332-339.
Zhou, S., Zhao, J., & Zhang, L. (2022). Application of Artificial Intelligence on Psychological Interventions and Diagnosis: An Overview. Frontiers in psychiatry, 13, 811665. https://doi.org/10.3389/fpsyt.2022.811665
Jiang, L., Tian, X., Ren, P., & Luo, F. (2022). A new type of mental health assessment using artificial intelligence technique. Advances in Psychological Science.
Timmons, A. C., Duong, J. B., Simo Fiallo, N., Lee, T., Vo, H. P. Q., Ahle, M. W., Comer, J. S., Brewer, L. C., Frazier, S. L., & Chaspari, T. (2023). A Call to Action on Assessing and Mitigating Bias in Artificial Intelligence Applications for Mental Health. Perspectives on psychological science : a journal of the Association for Psychological Science, 18(5), 1062–1096. https://doi.org/10.1177/17456916221134490
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