Humanoid robots for psychological assessment in mild cognitive impairment: from evaluation to the future of AI-driven data prediction systems.

Introduction: Neurodegenerative diseases, such as Alzheimer's Disease (AD), are increasingly prevalent, emphasizing the need for early diagnosis and effective intervention. This study explores the feasibility of using the humanoid robot to administer cognitive assessments for Mild Cognitive Impairment (MCI). Specifically, it evaluates the usability, accuracy, and patient experience of robot-administered cognitive testing compared to traditional assessments conducted by neuropsychologists.

Methods: A total of 100 MCI patients were randomly assigned to two groups: one undergoing the Mini-Mental State Examination (MMSE) with and the other receiving the same test administered by a neuropsychologist. After that participants were submitted to a Satisfaction Questionnaire (SQ) designed to assess their emotional and experiential response to the testing procedure, whether administered by a human or a robot.

Results: The intergroup analysis (EG vs. CG) reveals significant differences in age ( = 0.003) and Total SQ ( = 0.01), and in SQ2 (  = 9.76; df = 1;  = 0.002), SQ4 (  = 5.02; df = 1;  = 0.02), SQ5 (  = 25.35; df = 1;  < 0.001), SQ6 (  = 7.68; df = 1;  = 0.006) and SQ7 (  = 7.56; df = 1;  = 0.006). Results indicate no significant differences in MMSE scores between the two groups, suggesting comparable cognitive evaluation accuracy. However, participants assessed by reported lower frustration levels and higher satisfaction (90% vs. 40%) compared to those tested by a neuropsychologist. Additionally, 92% of patients in the robot-assisted group expressed willingness to retake the test in the same manner, indicating high acceptability and engagement.

Discussion: These findings suggest that robot-assisted cognitive assessments may enhance patient comfort and accessibility to neuropsychological testing. The integration of Artificial Intelligence (AI) further supports diagnostic accuracy and predictive potential, offering promising avenues for early intervention in neurodegenerative diseases.