Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review
Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overl...
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MDPI AG
2025-04-01
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| Online Access: | https://www.mdpi.com/2673-7426/5/2/22 |
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| author | Christos Kokkotis Ioannis Kansizoglou Theodoros Stampoulis Erasmia Giannakou Panagiotis Siaperas Stavros Kallidis Maria Koutra Christina Koutra Anastasia Beneka Evangelos Bebetsos |
| author_facet | Christos Kokkotis Ioannis Kansizoglou Theodoros Stampoulis Erasmia Giannakou Panagiotis Siaperas Stavros Kallidis Maria Koutra Christina Koutra Anastasia Beneka Evangelos Bebetsos |
| author_sort | Christos Kokkotis |
| collection | DOAJ |
| description | Occupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT. |
| format | Article |
| id | doaj-art-bcc167f74db343fe90d5ddbf8dcd69a0 |
| institution | Kabale University |
| issn | 2673-7426 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | BioMedInformatics |
| spelling | doaj-art-bcc167f74db343fe90d5ddbf8dcd69a02025-08-20T03:26:20ZengMDPI AGBioMedInformatics2673-74262025-04-01522210.3390/biomedinformatics5020022Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping ReviewChristos Kokkotis0Ioannis Kansizoglou1Theodoros Stampoulis2Erasmia Giannakou3Panagiotis Siaperas4Stavros Kallidis5Maria Koutra6Christina Koutra7Anastasia Beneka8Evangelos Bebetsos9Department of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceLaboratory of Robotics and Automation, Department of Production and Management Engineering, School of Engineering, Democritus University of Thrace, 67100 Xanthi, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceOccupational Therapy Department, Metropolitan College of Athens, 10672 Athens, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceDepartment of Physical Education and Sport Science, School of Physical Education, Sport Science and Occupational Therapy, Democritus University of Thrace, 69100 Komotini, GreeceOccupational therapy (OT) is vital in improving functional outcomes and aiding recovery for individuals with long-term disabilities, particularly those resulting from neurological diseases. Traditional assessment methods often rely on clinical judgment and individualized evaluations, which may overlook broader, data-driven insights. The integration of artificial intelligence (AI) presents a transformative opportunity to enhance assessment precision and personalize therapeutic interventions. Additionally, advancements in human–computer interaction (HCI) enable more intuitive and adaptive AI-driven assessment tools, improving user engagement and accessibility in OT. This scoping review investigates current applications of AI in OT, particularly regarding the evaluation of functional outcomes and support for clinical decision-making. The literature search was conducted using the PubMed and Scopus databases. Studies were included if they focused on AI applications in evaluating functional outcomes within OT assessment tools. Out of an initial pool of 85 articles, 13 met the inclusion criteria, highlighting diverse AI methodologies such as support vector machines, deep neural networks, and natural language processing. These were primarily applied in domains including motor recovery, pediatric developmental assessments, and cognitive engagement evaluations. Findings suggest that AI can significantly improve evaluation processes by systematically integrating diverse data sources (e.g., sensor measurements, clinical histories, and behavioral analytics), generating precise predictive insights that facilitate tailored therapeutic interventions and comprehensive assessments of both pre- and post-treatment strategies. This scoping review also identifies existing gaps and proposes future research directions to optimize AI-driven assessment tools in OT.https://www.mdpi.com/2673-7426/5/2/22machine learningdeep learningoutcome assessmentergotherapyrehabilitation |
| spellingShingle | Christos Kokkotis Ioannis Kansizoglou Theodoros Stampoulis Erasmia Giannakou Panagiotis Siaperas Stavros Kallidis Maria Koutra Christina Koutra Anastasia Beneka Evangelos Bebetsos Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review BioMedInformatics machine learning deep learning outcome assessment ergotherapy rehabilitation |
| title | Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review |
| title_full | Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review |
| title_fullStr | Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review |
| title_full_unstemmed | Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review |
| title_short | Artificial Intelligence as Assessment Tool in Occupational Therapy: A Scoping Review |
| title_sort | artificial intelligence as assessment tool in occupational therapy a scoping review |
| topic | machine learning deep learning outcome assessment ergotherapy rehabilitation |
| url | https://www.mdpi.com/2673-7426/5/2/22 |
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