Artificial intelligence–enhanced mapping of the international classification of functioning, disability and health via a mobile app
A randomized controlled trial
Bibliographic Data
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: = 0.842), with 80.6% of qualifiers matching exactly. The AI demonstrated high sensitivity and specificity for common functional domains (e.g., codes 1-2), though performance decreased for rare qualifiers. The micro-averaged sensitivity and specificity were 0.806 and 0.952, respectively. Mean SUS score among physicians was 86.8, indicating excellent usability and acceptability. 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
Human–computer interaction · International Classification of Functioning, Disability and Health · Machine learning · Pathology · Physical therapy · Randomized controlled trial · Usability · Artificial Intelligence in Healthcare and Education · Cerebral Palsy and Movement Disorders · Computer Science · Electronic Health Records Systems · Medicine · Artificial Intelligence · Rehabilitation
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| Citations per year | 1 |
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| Citation span | 2026 - 2026 (1) |
| Highly cited | No |