TY - JOUR
T1 - AI-assisted neurocognitive assessment protocol for older adults with psychiatric disorders
AU - Díaz-Guerra, Diego D.
AU - Hernández-Lugo, Marena de la C.
AU - Broche-Pérez, Yunier
AU - Ramos-Galarza, Carlos
AU - Iglesias-Serrano, Ernesto
AU - Fernández-Fleites, Zoylen
N1 - Publisher Copyright:
Copyright © 2025 Díaz-Guerra, Hernández-Lugo, Broche-Pérez, Ramos-Galarza, Iglesias-Serrano and Fernández-Fleites.
PY - 2024
Y1 - 2024
N2 - Introduction: Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed. Objectives: This paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms. Methodology: The proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers. Anticipated results: The AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies. Conclusions: By integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.
AB - Introduction: Evaluating neurocognitive functions and diagnosing psychiatric disorders in older adults is challenging due to the complexity of symptoms and individual differences. An innovative approach that combines the accuracy of artificial intelligence (AI) with the depth of neuropsychological assessments is needed. Objectives: This paper presents a novel protocol for AI-assisted neurocognitive assessment aimed at addressing the cognitive, emotional, and functional dimensions of older adults with psychiatric disorders. It also explores potential compensatory mechanisms. Methodology: The proposed protocol incorporates a comprehensive, personalized approach to neurocognitive evaluation. It integrates a series of standardized and validated psychometric tests with individualized interpretation tailored to the patient’s specific conditions. The protocol utilizes AI to enhance diagnostic accuracy by analyzing data from these tests and supplementing observations made by researchers. Anticipated results: The AI-assisted protocol offers several advantages, including a thorough and customized evaluation of neurocognitive functions. It employs machine learning algorithms to analyze test results, generating an individualized neurocognitive profile that highlights patterns and trends useful for clinical decision-making. The integration of AI allows for a deeper understanding of the patient’s cognitive and emotional state, as well as potential compensatory strategies. Conclusions: By integrating AI with neuro-psychological evaluation, this protocol aims to significantly improve the quality of neurocognitive assessments. It provides a more precise and individualized analysis, which has the potential to enhance clinical decision-making and overall patient care for older adults with psychiatric disorders.
KW - artificial intelligence
KW - cognitive disorders
KW - neurocognitive assessments
KW - neuropsychology
KW - older adults
UR - http://www.scopus.com/inward/record.url?scp=85216182436&partnerID=8YFLogxK
U2 - 10.3389/fpsyt.2024.1516065
DO - 10.3389/fpsyt.2024.1516065
M3 - Article
AN - SCOPUS:85216182436
SN - 1664-0640
VL - 15
JO - Frontiers in Psychiatry
JF - Frontiers in Psychiatry
M1 - 1516065
ER -