Stakeholder perception towards a machine-learning-based digital platform for detection and management of autism spectrum disorder

Autism spectrum disorder (ASD) affects approximately 1% of the population, presenting a significant healthcare challenge due to limited resources, particularly a shortage of clinicians, which impedes timely ASD detection and management in children. This study investigates stakeholder viewpoints regarding the effectiveness of integrating machine learning (ML) into the information and communications technology (ICT) platform for ASD detection and intervention. Primary stakeholders, including parents and clinicians, provide first hand experiences with this technology during and after the COVID-19 pandemic. The research identifies critical technology adoption factors by synthesizing stakeholder input based on user experiences, technology design, technology utility, and its impact. Additionally, the study gathers insights from potential investors interested in assistive technologies. Stakeholders unanimously acknowledge the pivotal role of technology in enhancing current ASD detection and management. However, their attitudes toward technology adoption exhibit divergent trends during and after the COVID-19 pandemic. The study highlights a shift toward a technology-enabled, human-centred framework, which gained prominence post-pandemic. Various factors contributing to this shift in stakeholder perspective were identified, including caregiver stress, technostress, and pandemic-induced environmental factors affecting stakeholders' stress levels and motivating them to shift towards a human-centric model. Stakeholders emphasize the paramount importance of human-centred approaches in ASD detection and intervention, with technology serving as an empowering tool. Stakeholders also highlight the imperative ethical and legal considerations to foster trust and enhance the adoption of ML-based technology. Consequently, future research should delve into stakeholder perspectives within the framework of fairness, accountability, transparency, and ethics (FATE) to ensure these technologies' responsible development and implementation.