Artificial Intelligence in Rehabilitation
A special issue of International Journal of Environmental Research and Public Health (ISSN 1660-4601). This special issue belongs to the section "Disabilities".
Deadline for manuscript submissions: closed (18 March 2023) | Viewed by 3104
Special Issue Editors
2. Department of Clinical and Experimental Medicine, University of Florence, 50134 Florence, Italy
Interests: stroke rehabilitation; neurological rehabilitation; exercise therapy; technologies for rehabilitation; musculoskeletal disorders; geriatric rehabilitation; healthy ageing; rehabilitation outcome measures; rehabilitation outcome prediction
* Associate Professor
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; clinical outcome prediction; rehabilitation; wearable sensors; activity recognition; inertial sensors; movement analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The increasing availability of healthcare data and computational power is fostering the development of novel data analytical methods, which are opening new avenues for the use of artificial intelligence- and machine learning (ML)-based technology in medical practice. The term “artificial intelligence” (AI), coined in the 1956 Dartmouth Artificial Intelligence conference, is a broad concept, which now includes, in its more recent definitions, both virtual (informatics) and physical (e.g., robotics) branches. Machine learning (ML) was defined by Arthus Samuel in 1959, as “the field of study that gives computers the ability to learn without being explicitly programmed”, which could be explained as studying algorithms that improve automatically by building on the “experience” obtained from the data.
Rehabilitation is a complex process, including a variety of interventions that are aimed at improving the dimensions of functioning, including body structure and functions, activity and participation. The use of AI to improve the delivery and outcomes of rehabilitation is a priority of the National Institutes of Health Rehabilitation Research Plan, and, in the last decade, research on the use of AI to improve key rehabilitation processes and outcomes is steadily growing.
The use of AI in rehabilitation has the potential to improve the quality of care given to patients, while also providing economic benefits, building its knowledge on the amount of health-related data that can be collected to assess patient outcomes, in real time, and even out of hospital settings, within the environments that people live in.
One example of a relevant application of ML methods for this purpose is the development of predictive models and their inclusion in decision support tools. Medical decision making, for both diagnostic and prognostic aims, requires clinical experts to make decisions, integrating patient information from many sources that can interact in complex ways. To aid in managing this uncertainty, computer tools are being developed, with the aim of becoming integrated in daily clinical practice. The clinical validation to assess the reliability and safety of these systems is another emerging issue; however, altogether, ML-based intelligent decision support tools may improve the clinical workflow, increase patients’ safety, support diagnosis, and promote personalized treatment.
This Special Issue welcomes original research articles, narratives and systematic reviews that are focused on, but not limited to, the following themes:
- Learning/AI applications in rehabilitation;
- Clinical validation of algorithms;
- Decision support in rehabilitation;
- Data-driven rehabilitation.
Dr. Francesca Cecchi
Dr. Andrea Mannini
Guest Editors
Manuscript Submission Information
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Keywords
- machine learning
- intelligent systems
- decision support tool
- rehabilitation
- neural network
- translational medicine
- clinical validation
- artificial intelligence
