Trial reportFrontiers in public health2025
Artificial intelligence-enhanced mapping of the international classification of functioning, disability and health via a mobile app: a randomized controlled trial.
Trial report in Frontiers in public health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07021781 (Integrating Artificial Intelligence Into International Classification of Functioning, Disability, and Health Coding), which is not on this map. Cited by 3 papers.
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.
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.
Integrating Artificial Intelligence Into International Classification of Functioning, Disability, and Health Coding: Effectiveness of a Mobile Application for Patient Questionnaires
Who cites it
3 citing papers in PubMed.
- A Prediction Model for One-Year Disability Risk Among Community-Dwelling Older Adults in China: Integrating Physical, Metabolic, and Psychosocial Factors.Health care science · 2026Article
- Mapping the landscape of AI in healthcare in Kazakhstan: a scoping review of readiness, development, and adoption.BMC health services research · 2026Article
- A call for action to strengthen stakeholder readiness for ICF data exchange in European health data space: a structured narrative review.Frontiers in public health · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Mobile health applications and artificial intelligence (AI) are increasingly utilized to streamline clinical workflows and support functional assessment. The International Classification of Functioning, Disability and Health (ICF) provides a standardized framework for evaluating patient functioning, yet AI-driven ICF mapping tools remain underexplored in routine clinical settings. Objective: This study aimed to evaluate the efficiency and accuracy of the MedQuest mobile application-featuring integrated AI-based ICF mapping-compared to traditional paper-based assessment in hospitalized patients. Methods: A parallel-group randomized controlled trial was conducted in two medical centers in Astana, Kazakhstan. A total of 185 adult inpatients (≥18 years) were randomized to either a control group using paper questionnaires or an experimental group using the MedQuest app. Both groups completed identical standardized assessments (SF-12, IPAQ, VAS, Barthel Index, MRC scale). The co-primary outcomes were (1) total questionnaire completion time and (2) agreement between AI-generated and clinician-generated ICF mappings, assessed using quadratic weighted kappa. Secondary outcomes included AI sensitivity/specificity, confusion matrix analysis, and physician usability ratings via the System Usability Scale (SUS). Results: The experimental group completed questionnaires significantly faster than the control group (median 18 vs. 28 min, Conclusion: The MedQuest mobile application significantly improved workflow efficiency and demonstrated strong concordance between AI- and clinician-assigned ICF mappings. These findings support the feasibility of integrating AI-assisted tools into routine clinical documentation. A hybrid model, combining AI automation with clinician oversight, may enhance accuracy and reduce documentation burden in time-constrained healthcare environments. Trial registration: ClinicalTrials.gov, identifier NCT07021781.
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Registered trials
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 Socratic graph.