Basic principles for the development of an AI-based tool for assistive technology decision making
| Autor: | Moran Ran, David Banes, Marcia J. Scherer |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Technology
030506 rehabilitation Computer science Decision Making Rehabilitation Biomedical Engineering Physical Therapy Sports Therapy and Rehabilitation Recommender system Self-Help Devices 03 medical and health sciences Speech and Hearing Engineering management 0302 clinical medicine Artificial Intelligence Assistive technology Humans Disabled Persons Orthopedics and Sports Medicine 0305 other medical science 030217 neurology & neurosurgery |
| Zdroj: | Disability and Rehabilitation: Assistive Technology. 17:778-781 |
| ISSN: | 1748-3115 1748-3107 |
| DOI: | 10.1080/17483107.2020.1817163 |
| Popis: | The impact of assistive technology use on the lives of people with disabilities has long been demonstrated in the literature. Despite the need for assistive technologies, and a wealth of innovative, afford-able, and accessible products, a low rate of assistive technology uptake is globally maintained. One of the reasons for this gap is related to data and knowledge formation and management. Low access to information and a lack of assessment services is evident. Fragmentation of data, inconsistency in assessment methodology and heterogeneity in the competence of assistive technology professionals, has led to a growing interest in the opportunities that data sciences, including AI, hold for the future of the assistive technology sector, as a supportive and constructive mechanism in any decision-making process.In this short paper, we seek to describe some of the principles that such an AI-based recommendation system should be built upon, using the Atvisor platform as a case study. Atvisor.ai is an AI-based digital platform that supports assistive technology assessments and the decision-making process.Our recommendations represent the aggregated insights from two pilots held in Israel, testing the platform in multiple environments and with different stakeholders. These recommendations include ensuring the continuum of care and providing a full user journey, incorporating shared decision making and self-assessment features, providing data personalisation and a holistic approach, building a market network infrastructure and designing the tool within a wider service delivery model design. Assessment and decision-making processes, crucial to optimal uptake, cab be leveraged by technology to become more accessible and personalised.IMPLICATIONS FOR REHABILITATIONProvides principles for the development of an AI-based recommendation system for assistive technology decision making.Promotes the use of artificial intelligence to support users and professionals in the assistive technology decision making process.Personalization of data regarding assistive technology, according to functional, holistic and client centered profiles of users, ensures optimal match and better use of assistive technology.Self-assessment and professional assessment components are important for enabling multiple access points to the assistive technology decision making process, based on the preferences and needs of users. |
| Databáze: | OpenAIRE |
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