Evidence map›Paper›PMID 41052508›Full record

ArticleJMIR serious games2025

Vision AI-Based Gamified Cognitive Prosthesis for Executive Function: Feasibility and Usability Study.

Co Yih Siow, Yao-Hua Yang, Cheng-Jui Tsai, Wan-Wan Yang, Chaur-Jong Hu, Jia-Ying Sung, Jowy Tani

Abstract read
In one paragraph

Article in JMIR serious games, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Co Yih SiowDepartment of Physical Medicine and Rehabilitation, Taipei Medical University Hospital, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0000-0003-4033-5481
Yao-Hua YangBiomed Innovation Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0009-0009-5800-4737
Cheng-Jui TsaiSchool of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0000-0003-1728-1833
Wan-Wan YangBiomed Innovation Center, Wan Fang Hospital, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0009-0003-6591-3548
Chaur-Jong HuDepartment of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0000-0002-4900-5967
Jia-Ying SungDepartment of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0000-0003-0534-7882
Jowy TaniDepartment of Neurology, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.ORCID http://orcid.org/0000-0002-9979-6779

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Dementia is a progressive neurodegenerative condition marked by cognitive decline and loss of functional independence. Among cognitive domains, executive dysfunction is a critical early contributor to reduced self-care capacity and increased caregiver burden. While cognitive assistive technologies have focused primarily on memory, few tools address executive function in real-time, daily tasks. To fill this gap, we developed a novel gamified cognitive prosthesis that integrates artificial intelligence (AI) and computer vision to guide users step-by-step through a simulated egg-cooking task. This system provides real-time audiovisual feedback to support planning, sequencing, and error correction. Objective: This study aimed to evaluate whether the AI-based cognitive prosthesis improves task completion time and executive function performance in individuals with mild dementia. Methods: We conducted a pilot study involving 12 patients with mild dementia and 7 age-matched healthy controls. Participants were asked to complete a 6-step gamified egg boiling task under 2 conditions: with and without guidance. The task was evaluated using a custom "Daily Task Completion Test" and a modified executive function performance test (EFPT) adapted to the cooking activity. Demographic and clinical data (age, sex, education, Mini-Mental State Examination, Clinical Dementia Rating, activities of daily living, instrumental activities of daily living, and Dementia Severity Rating Scale) were recorded. The System Usability Scale (SUS) was also collected postintervention. Results: In the mild dementia group, AI assistance significantly reduced median task completion time from 134.75 (IQR 92.50-134.75) to 92.00 (IQR 65.00-92.00; P=.03) seconds, and significantly improved the Executive Function Performance Test (EFPT) scores from 4.25 (IQR 1.75-4.25) to 1.00 (IQR 0.00-1.00; P=.005), reflecting a 31.7% improvement in efficiency and a 76.5% reduction in required assistance. No significant changes were observed in the control group. The mean SUS score was 80.53 (SD 24.97), indicating high usability. The AI system achieved a cumulative recognition precision of 0.93 (SD 0.07) and cumulative recall of 0.94 (SD 0.11). Conclusions: This pilot study provides preliminary evidence that an AI-based cognitive prosthesis can enhance executive function and task performance in individuals with mild dementia. The results support the feasibility of using real-time AI guidance in everyday tasks to promote independence. Given its modular design and promising usability profile, this system may serve as the foundation for future digital therapeutics targeting executive dysfunction. Larger, longitudinal studies are warranted to evaluate sustained cognitive and functional benefits.

Indexed as

artificial intelligenceaudiovisual stimulationcognitive impairmentcognitive prosthesisdigital therapeuticsexecutive functionmild dementia

Identifiers

PMID41052508
PMCPMC12500313

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.