Artificial Intelligence in Neuropsychology: Advancing Diagnosis, Personalized Treatment, and Predictive Modeling
A special issue of Brain Sciences (ISSN 2076-3425). This special issue belongs to the section "Neuropsychology".
Deadline for manuscript submissions: 1 November 2025 | Viewed by 22
Special Issue Editor
2. Neuroradiology Unit, Department of Diagnostic and Technology, Fondazione IRCCS Isttuto Neurologico Carlo Besta, Milan, Italy
3. Neuroscience Institute of Turin, University of Turin, Turin, Italy
Interests: brain ageing; cognitive reserve; normal cognitive aging; frailty; minor and major neurocognitive disorders; metacognitive–executive functions; placebo; pain; neuroimaging
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of artificial intelligence (AI) into neuropsychology is transforming the field, offering innovative solutions for diagnosis, treatment, and predictive modeling. AI-driven diagnostic tools enhance the accuracy and efficiency of neuropsychological assessments by analyzing complex cognitive and behavioral data. Personalized treatment approaches leverage AI algorithms to tailor interventions to the unique characteristics of each patient, optimizing therapeutic outcomes. Additionally, predictive modeling techniques, powered by machine learning, allow for early identification of patients at risk and provide insights into treatment efficacy. This Special Issue aims to explore the latest advancements in AI applications for neuropsychology, highlighting methodological innovations, clinical applications, and ethical considerations. We invite contributions on AI-based diagnostic tools, personalized neuropsychological interventions, predictive analytics, and related topics. By bridging AI and neuropsychology, this issue seeks to foster a deeper understanding of how intelligent systems can enhance patient care and clinical decision-making.
Dr. Sara Palermo
Guest Editor
Manuscript Submission Information
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Keywords
- AI-driven diagnostic tools
- personalized treatment
- machine learning predictive modeling
- data analysis
- mental health and rehabilitation
- decision support systems
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