Manuscript Number : SHISRRJ258228
A Conceptual Framework for AI-Powered Digital Health Tools in Early Autism Diagnosis
Authors(4) :-Augustine Onyeka Okoli, Opeoluwa Oluwanifemi Akomolafe, Damilola Oluyemi Merotiwon, Erica Afrihyia This paper presents a conceptual framework for the development and implementation of AI-powered digital health tools aimed at early autism diagnosis. The framework integrates advanced machine learning techniques, such as deep learning and natural language processing, with digital health technologies to enhance the accuracy, efficiency, and accessibility of autism spectrum disorder (ASD) diagnosis in children. By utilizing a combination of behavioral data, physiological signals, and environmental factors, the framework seeks to provide a comprehensive approach for early detection and intervention. The proposed model emphasizes the importance of continuous learning and adaptation of AI systems to improve diagnostic outcomes. Additionally, the framework considers ethical implications, data privacy concerns, and the role of healthcare professionals in the diagnostic process. This conceptual framework aims to inspire future research and development in the field of AI-driven healthcare solutions for autism diagnosis.
Augustine Onyeka Okoli Digital Health, Early Detection, Machine Learning, Deep Learning, Behavioral Data, Healthcare Technology.
Publication Details Published in : Volume 8 | Issue 2 | March-April 2025 Article Preview
Longmed Medical Centre, Pietermaritzburg, South Africa
Opeoluwa Oluwanifemi Akomolafe
Independent Researcher, UK
Damilola Oluyemi Merotiwon
Department of Healthcare Administration, University of the Potomac, Washington.D.C. USA.
Erica Afrihyia
Independent Researcher, Ohio, USA.
Date of Publication : 2025-04-12
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 150-166
Manuscript Number : SHISRRJ258228
Publisher : Shauryam Research Institute
URL : https://shisrrj.com/SHISRRJ258228