Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: Systematic Literature Review
- URL: http://arxiv.org/abs/2405.10883v1
- Date: Fri, 17 May 2024 16:20:34 GMT
- Title: Application of Artificial Intelligence in Schizophrenia Rehabilitation Management: Systematic Literature Review
- Authors: Hongyi Yang, Fangyuan Chang, Dian Zhu, Muroi Fumie, Zhao Liu,
- Abstract summary: This review aims to assess the current status and prospects of artificial intelligence (AI) in the rehabilitation management of patients with schizophrenia.
We selected 70 studies from 2012 to the present, focusing on application, technology categories, products, and data types in mental health interventions and management.
The results indicate that AI can be widely used in symptom monitoring, relapse risk prediction, and rehabilitation treatment by analyzing ecological momentary assessment, behavioral, and speech data.
- Score: 4.619934969700147
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This review aims to systematically assess the current status and prospects of artificial intelligence (AI) in the rehabilitation management of patients with schizophrenia and their impact on the rehabilitation process. We selected 70 studies from 2012 to the present, focusing on application, technology categories, products, and data types of machine learning, deep learning, reinforcement learning, and other technologies in mental health interventions and management. The results indicate that AI can be widely used in symptom monitoring, relapse risk prediction, and rehabilitation treatment by analyzing ecological momentary assessment, behavioral, and speech data. This review further explores the potential challenges and future directions of emerging products, technologies, and analytical methods based on AI, such as social media analysis, serious games, and large language models in rehabilitation. In summary, this study systematically reviews the application status of AI in schizophrenia rehabilitation management and provides valuable insights and recommendations for future research paths.
Related papers
- Artificial intelligence techniques in inherited retinal diseases: A review [19.107474958408847]
Inherited retinal diseases (IRDs) are a diverse group of genetic disorders that lead to progressive vision loss and are a major cause of blindness in working-age adults.
Recent advancements in artificial intelligence (AI) offer promising solutions to these challenges.
This review consolidates existing studies, identifies gaps, and provides an overview of AI's potential in diagnosing and managing IRDs.
arXiv Detail & Related papers (2024-10-10T03:14:51Z) - A Survey of Models for Cognitive Diagnosis: New Developments and Future Directions [66.40362209055023]
This paper aims to provide a survey of current models for cognitive diagnosis, with more attention on new developments using machine learning-based methods.
By comparing the model structures, parameter estimation algorithms, model evaluation methods and applications, we provide a relatively comprehensive review of the recent trends in cognitive diagnosis models.
arXiv Detail & Related papers (2024-07-07T18:02:00Z) - Reporting Risks in AI-based Assistive Technology Research: A Systematic Review [2.928964540437144]
We conducted a systematic literature review of research into AI-based assistive technology for persons with visual impairments.
Our study shows that most proposed technologies with a testable prototype have not been evaluated in a human study with members of the sight-loss community.
arXiv Detail & Related papers (2024-07-01T05:22:44Z) - Emotional Intelligence Through Artificial Intelligence : NLP and Deep Learning in the Analysis of Healthcare Texts [1.9374282535132377]
This manuscript presents a methodical examination of the utilization of Artificial Intelligence in the assessment of emotions in texts related to healthcare.
We scrutinize numerous research studies that employ AI to augment sentiment analysis, categorize emotions, and forecast patient outcomes.
There persist challenges, which encompass ensuring the ethical application of AI, safeguarding patient confidentiality, and addressing potential biases in algorithmic procedures.
arXiv Detail & Related papers (2024-03-14T15:58:13Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Automated Fidelity Assessment for Strategy Training in Inpatient
Rehabilitation using Natural Language Processing [53.096237570992294]
Strategy training is a rehabilitation approach that teaches skills to reduce disability among those with cognitive impairments following a stroke.
Standardized fidelity assessment is used to measure adherence to treatment principles.
We developed a rule-based NLP algorithm, a long-short term memory (LSTM) model, and a bidirectional encoder representation from transformers (BERT) model for this task.
arXiv Detail & Related papers (2022-09-14T15:33:30Z) - Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy:
Iterative Design and Evaluation with Therapists and Post-Stroke Survivors [66.07833535962762]
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Previous work explored the potential of automatically monitoring exercises for AI and robotic coaches, but deployment remains a challenge.
We present our efforts on eliciting the detailed design specifications on how AI and robotic coaches could interact with and guide patient's exercises.
arXiv Detail & Related papers (2021-06-15T22:06:39Z) - AI-Augmented Behavior Analysis for Children with Developmental
Disabilities: Building Towards Precision Treatment [2.0324247356209835]
We present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans.
By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior.
arXiv Detail & Related papers (2021-02-21T16:15:40Z) - Achievements and Challenges in Explaining Deep Learning based
Computer-Aided Diagnosis Systems [4.9449660544238085]
We discuss early achievements in development of explainable AI for validation of known disease criteria.
We highlight some of the remaining challenges that stand in the way of practical applications of AI as a clinical decision support tool.
arXiv Detail & Related papers (2020-11-26T08:08:19Z) - Review of Machine Learning Algorithms for Brain Stroke Diagnosis and
Prognosis by EEG Analysis [50.591267188664666]
Strokes are the leading cause of adult disability in the United States.
Brain-Computer Interfaces (BCIs) help the patient either restore neurologic pathways or effectively communicate with an electronic prosthetic.
The various machine learning techniques and algorithms that are addressed and combined with BCIs technology show that the use of BCIs for stroke treatment is a promising and rapidly expanding field.
arXiv Detail & Related papers (2020-08-06T19:50:29Z) - A Review of Computational Approaches for Evaluation of Rehabilitation
Exercises [58.720142291102135]
This paper reviews computational approaches for evaluating patient performance in rehabilitation programs using motion capture systems.
The reviewed computational methods for exercise evaluation are grouped into three main categories: discrete movement score, rule-based, and template-based approaches.
arXiv Detail & Related papers (2020-02-29T22:18:56Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.