Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective

Zdravkova, Katerina; Krasniqi, Venera; Dalipi, Fisnik; Ferati, Mexhid

doi:10.3389/frai.2022.970430

Cited by 18 publications

(11 citation statements)

References 102 publications

(109 reference statements)

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“…By analyzing data from educational programs, ML algorithms can identify effective teaching strategies, personalize learning approaches, and provide recommendations for individualized educational plans [74]. Additionally, ML-based technologies, such as speech and language processing algorithms, can assist in speech therapy and communication interventions for individuals with Down syndrome [68]. This study provides an in-depth analysis of AI-driven solutions that enhance communication and education for disabled children, concluding with considerations for future developments and ethical concerns associated with these technologies [68].…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, ML-based technologies, such as speech and language processing algorithms, can assist in speech therapy and communication interventions for individuals with Down syndrome [68]. This study provides an in-depth analysis of AI-driven solutions that enhance communication and education for disabled children, concluding with considerations for future developments and ethical concerns associated with these technologies [68]. Together, these studies showcase the multifaceted applications of AI and ML in advancing understanding, diagnosis, and care for individuals with DS.…”

Section: Discussionmentioning

confidence: 99%

“…Indeed, the neurodevelopmental and cognitive profiles in individuals with Down syndrome exhibit significant diversity [66,67]. While the presence of an extra copy of chromosome 21 contributes to shared characteristics, such as intellectual disability and certain physical features, the specific cognitive abilities, strengths, and challenges can vary widely among individuals [68]. Factors such as genetic variations and individual differences contribute to this variability.…”

Section: Prenatal Diagnosticsmentioning

confidence: 99%

“…A variety of clinical data can be analyzed using AI and ML algorithms, which can be trained to identify patterns that might be symptomatic of Down syndrome. Incorporating ML techniques into Down syndrome detection holds significant potential for enhancing accuracy, efficiency, and accessibility [64,68,73]. The integration of machine learning (ML) into cell-free prenatal screening and maternal plasma cell-free DNA sequencing for Down syndrome diagnosis will present a transformative paradigm with significant motivations and potential enhancements.…”

Section: Artificial Intelligence (Ai)-based Diagnosismentioning

confidence: 99%

See 3 more Smart Citations

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Koul,

Ahmad,

Bhat

et al. 2023

Biomedicines

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Down syndrome arises from chromosomal non-disjunction during gametogenesis, resulting in an additional chromosome. This anomaly presents with intellectual impairment, growth limitations, and distinct facial features. Positive correlation exists between maternal age, particularly in advanced cases, and the global annual incidence is over 200,000 cases. Early interventions, including first and second-trimester screenings, have improved DS diagnosis and care. The manifestations of Down syndrome result from complex interactions between genetic factors linked to various health concerns. To explore recent advancements in Down syndrome research, we focus on the integration of artificial intelligence (AI) and machine learning (ML) technologies for improved diagnosis and management. Recent developments leverage AI and ML algorithms to detect subtle Down syndrome indicators across various data sources, including biological markers, facial traits, and medical images. These technologies offer potential enhancements in accuracy, particularly in cases complicated by cognitive impairments. Integration of AI and ML in Down syndrome diagnosis signifies a significant advancement in medical science. These tools hold promise for early detection, personalized treatment, and a deeper comprehension of the complex interplay between genetics and environmental factors. This review provides a comprehensive overview of neurodevelopmental and cognitive profiles, comorbidities, diagnosis, and management within the Down syndrome context. The utilization of AI and ML represents a transformative step toward enhancing early identification and tailored interventions for individuals with Down syndrome, ultimately improving their quality of life.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Prenatal Diagnosticsmentioning

confidence: 99%

Section: Artificial Intelligence (Ai)-based Diagnosismentioning

confidence: 99%

See 2 more Smart Citations

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Koul,

Ahmad,

Bhat

et al. 2023

Biomedicines

View full text Add to dashboard Cite

show abstract

“…However, one of the most common problems with clustering patients into discrete groups is the lack of using a rigorous approach for selecting the number of clusters [ 31 ]. Anatomical and pathophysiological differences between patients may not result in coherent groups at all, resulting in inconsistent clinical outcomes [ 38 , 42 ].…”

Section: Topics In Deep Brain Stimulation Research Using Explainable ...mentioning

confidence: 99%

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

Allen

2023

Biomedicines

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Deep brain stimulation is a treatment that controls symptoms by changing brain activity. The complexity of how to best treat brain dysfunction with deep brain stimulation has spawned research into artificial intelligence approaches. Machine learning is a subset of artificial intelligence that uses computers to learn patterns in data and has many healthcare applications, such as an aid in diagnosis, personalized medicine, and clinical decision support. Yet, how machine learning models make decisions is often opaque. The spirit of explainable artificial intelligence is to use machine learning models that produce interpretable solutions. Here, we use topic modeling to synthesize recent literature on explainable artificial intelligence approaches to extracting domain knowledge from machine learning models relevant to deep brain stimulation. The results show that patient classification (i.e., diagnostic models, precision medicine) is the most common problem in deep brain stimulation studies that employ explainable artificial intelligence. Other topics concern attempts to optimize stimulation strategies and the importance of explainable methods. Overall, this review supports the potential for artificial intelligence to revolutionize deep brain stimulation by personalizing stimulation protocols and adapting stimulation in real time.

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A Critical Review of Literature on Social Media and Developmental Communication Disability: Implications for Future Social Media and Generative AI Research

Hemsley,

Given,

Almond

et al. 2024

Curr Dev Disord Rep

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Purpose of Review The widespread use of social media and recent development and use of generative artificial intelligence (GenAI) has implications for the advancement of inclusive communication technologies to benefit people with developmental communication disability. This paper synthesises and critiques recent research on social media use in populations with developmental communication disability, discusses the intersection of social media and GenAI that could impact this population, and provides directions for future research. Recent Findings There is encouraging growth in the use of innovative methods to gather the views of people with developmental communication disability on their use of social media, advancing understanding of the risks they face and how they and their supporters manage these risks. However, there continues an underutilization of social media data published by and with people with developmental communication disability and a lack of co-design. Moving beyond patterns and purposes of use, future inclusive and co-designed research with this population should (a) include social media data, whether independently or co-produced with supporters; (b) explore the under-researched legal, ethical, and safety issues; (c) measure the outcomes and impact of social media publication in relation to the user or user group’s emancipation, education, and enablement; and (d) consider the impact of GenAI on social media use for this group. Summary Continued attention to the safe and enjoyable use of social media for all those who wish to use it, and foundational research on the uptake and use of GenAI, will be important to understand and support the advanced communication technology support needs of people with developmental communication disability. To inform future advances in both social media and GenAI for populations with developmental communication disability, future research directions are proposed.

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Cutting-edge communication and learning assistive technologies for disabled children: An artificial intelligence perspective

Cited by 18 publications

References 102 publications

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Unraveling Down Syndrome: From Genetic Anomaly to Artificial Intelligence-Enhanced Diagnosis

Discovering Themes in Deep Brain Stimulation Research Using Explainable Artificial Intelligence

A Critical Review of Literature on Social Media and Developmental Communication Disability: Implications for Future Social Media and Generative AI Research

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