The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD
- URL: http://arxiv.org/abs/2505.02747v1
- Date: Mon, 05 May 2025 15:58:32 GMT
- Title: The use of Artificial Intelligence for Intervention and Assessment in Individuals with ASD
- Authors: Aggeliki Sideraki, Christos-Nikolaos Anagnostopoulos,
- Abstract summary: It focuses particularly on AI's role in early diagnosis, utilizing advanced machine learning techniques and data analysis.<n>The paper examines AI-powered intervention technologies, emphasizing educational robots and adaptive communication tools.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper explores the use of Artificial Intelligence (AI) as a tool for diagnosis, assessment, and intervention for individuals with Autism Spectrum Disorder (ASD). It focuses particularly on AI's role in early diagnosis, utilizing advanced machine learning techniques and data analysis. Recent studies demonstrate that deep learning algorithms can identify behavioral patterns through biometric data analysis, video-based interaction assessments, and linguistic feature extraction, providing a more accurate and timely diagnosis compared to traditional methods. Additionally, AI automates diagnostic tools, reducing subjective biases and enabling the development of personalized assessment protocols for ASD monitoring. At the same time, the paper examines AI-powered intervention technologies, emphasizing educational robots and adaptive communication tools. Social robotic assistants, such as NAO and Kaspar, have been shown to enhance social skills in children by offering structured, repetitive interactions that reinforce learning. Furthermore, AI-driven Augmentative and Alternative Communication (AAC) systems allow children with ASD to express themselves more effectively, while machine-learning chatbots provide language development support through personalized responses. The study presents research findings supporting the effectiveness of these AI applications while addressing challenges such as long-term evaluation and customization to individual needs. In conclusion, the paper highlights the significance of AI as an innovative tool in ASD diagnosis and intervention, advocating for further research to assess its long-term impact.
Related papers
- The Impact of Artificial Intelligence on Emergency Medicine: A Review of Recent Advances [0.2544903230401084]
Artificial Intelligence (AI) is revolutionizing emergency medicine by enhancing diagnostic processes and improving patient outcomes.<n>Machine learning and deep learning are pivotal in interpreting complex imaging data, offering rapid, accurate diagnoses and potentially surpassing traditional diagnostic methods.<n>Despite these advancements, the integration of AI into clinical practice presents challenges such as data privacy, algorithmic bias, and the need for extensive validation across diverse settings.
arXiv Detail & Related papers (2025-03-17T17:45:00Z) - Analyzing the Impact of AI Tools on Student Study Habits and Academic Performance [0.0]
The research focuses on how AI tools can support personalized learning, adaptive test adjustments, and provide real-time classroom analysis.<n>Student feedback revealed strong support for these features, and the study found a significant reduction in study hours alongside an increase in GPA.<n>Despite these benefits, challenges such as over-reliance on AI and difficulties in integrating AI with traditional teaching methods were also identified.
arXiv Detail & Related papers (2024-12-03T04:51:57Z) - Towards Next-Generation Medical Agent: How o1 is Reshaping Decision-Making in Medical Scenarios [46.729092855387165]
We study the choice of the backbone LLM for medical AI agents, which is the foundation for the agent's overall reasoning and action generation.<n>Our findings demonstrate o1's ability to enhance diagnostic accuracy and consistency, paving the way for smarter, more responsive AI tools.
arXiv Detail & Related papers (2024-11-16T18:19:53Z) - 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 Artificial Intelligence in Gait-Based Neurodegenerative Disease Diagnosis [51.07114445705692]
neurodegenerative diseases (NDs) traditionally require extensive healthcare resources and human effort for medical diagnosis and monitoring.<n>As a crucial disease-related motor symptom, human gait can be exploited to characterize different NDs.<n>The current advances in artificial intelligence (AI) models enable automatic gait analysis for NDs identification and classification.
arXiv Detail & Related papers (2024-05-21T06:44:40Z) - 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) - Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness [53.91018508439669]
The study explores the complexities of integrating Artificial Intelligence into Autonomous Vehicles (AVs)
It examines the challenges introduced by AI components and the impact on testing procedures.
The paper identifies significant challenges and suggests future directions for research and development of AI in AV technology.
arXiv Detail & Related papers (2024-02-21T08:29:42Z) - Edge Computing based Human-Robot Cognitive Fusion: A Medical Case Study
in the Autism Spectrum Disorder Therapy [1.8220718426493654]
This paper proposes the architecture of edge cognitive computing by combining human experts and assisted robots.
By integrating the real-time computing and analysis of a new cognitive robotic model for ASD therapy, the proposed architecture can achieve a seamless remote diagnosis, round-the-clock symptom monitoring, emergency warning, therapy alteration, and advanced assistance.
arXiv Detail & Related papers (2024-01-01T14:45:19Z) - Open Brain AI. Automatic Language Assessment [0.0]
Language assessment plays a crucial role in diagnosing and treating individuals with speech, language, and communication disorders.
This paper discusses the development of Open Brain AI, the AI language processing modules, and the linguistic measurements of discourse macro-structure and micro-structure.
The fast and automatic analysis of language alleviates the burden on clinicians, enabling them to streamline their workflow and allocate more time and resources to direct patient care.
arXiv Detail & Related papers (2023-06-11T14:37:45Z) - Leveraging Pretrained Representations with Task-related Keywords for
Alzheimer's Disease Detection [69.53626024091076]
Alzheimer's disease (AD) is particularly prominent in older adults.
Recent advances in pre-trained models motivate AD detection modeling to shift from low-level features to high-level representations.
This paper presents several efficient methods to extract better AD-related cues from high-level acoustic and linguistic features.
arXiv Detail & Related papers (2023-03-14T16:03:28Z) - 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) - Human-Robot Collaboration and Machine Learning: A Systematic Review of
Recent Research [69.48907856390834]
Human-robot collaboration (HRC) is the approach that explores the interaction between a human and a robot.
This paper proposes a thorough literature review of the use of machine learning techniques in the context of HRC.
arXiv Detail & Related papers (2021-10-14T15:14:33Z) - 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)
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.