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Special Issue "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 2280

Special Issue Editors

1. IRCCS Fondazione Don Carlo Gnocchi, 50143 Florence, Italy
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
IRCCS Fondazione Don Carlo Gnocchi, 50143 Firenze, Italy
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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. International Journal of Environmental Research and Public Health is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • machine learning
  • intelligent systems
  • decision support tool
  • rehabilitation
  • neural network
  • translational medicine
  • clinical validation
  • artificial intelligence

Published Papers (1 paper)

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16 pages, 2022 KiB  
The Use of Machine Learning for Inferencing the Effectiveness of a Rehabilitation Program for Orthopedic and Neurological Patients
Int. J. Environ. Res. Public Health 2023, 20(8), 5575; https://doi.org/10.3390/ijerph20085575 - 19 Apr 2023
Cited by 3 | Viewed by 1062
Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this [...] Read more.
Advance assessment of the potential functional improvement of patients undergoing a rehabilitation program is crucial in developing precision medicine tools and patient-oriented rehabilitation programs, as well as in better allocating resources in hospitals. In this work, we propose a novel approach to this problem using machine learning algorithms focused on assessing the modified Barthel index (mBI) as an indicator of functional ability. We build four tree-based ensemble machine learning models and train them on a private training cohort of orthopedic (OP) and neurological (NP) hospital discharges. Moreover, we evaluate the models using a validation set for each category of patients using root mean squared error (RMSE) as an absolute error indicator between the predicted mBI and the actual values. The best results obtained from the study are an RMSE of 6.58 for OP patients and 8.66 for NP patients, which shows the potential of artificial intelligence in predicting the functional improvement of patients undergoing rehabilitation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Rehabilitation)
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