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Published on: February 2, 2020
AI-Powered Chatbot as a Health Literacy Tool for Enhancing Oral Cancer Awareness: Expert Feedback
NourEldin Abosamak1, Asmaa M Namoos2, Rana Ramadan1
1C. Kenneth and Dianne Wright Center for Clinical and Translational Research, Virginia Commonwealth University, Richmond, Virginia, USA.
Abstract:
Artificial intelligence (AI)-powered chatbots have emerged as potentially effective tools for delivering personalized, interactive, and culturally sensitive health education. Avoidable factors majorly cause oral and oropharyngeal cancers, and usually present with confusable symptoms, often leading to delayed diagnoses and poorer outcomes. Traditional oral health education methods have limitations in addressing the unique accessibility needs of groups that are most impacted by these outcomes. The primary aim of this study was to evaluate the usability and accuracy of an AI-powered chatbot prototype as an approachable, trustworthy, and engaging educational resource to enhance oral cancer awareness. A mixed-methods evaluation was conducted with six experts in behavioral science, oncology, dentistry, and AI. Experts interacted with the chatbot via a web interface and provided feedback via surveys rating usability and accuracy. Qualitative insights were gathered through open-ended survey questions and analyzed using thematic analysis, and quantitative data were collected through REDCap. The experts provided positive and constructive feedback, recognizing the chatbot's potential as an educational tool aiming to improve oral cancer-related information. They particularly praised its ease of access and the reliability of the information provided. However, experts identified areas for enhancement, including user interface changes, simplifying medical terminology, providing clearer initial guidance for users, and improving visual accessibility. Additionally, recommendations included integrating supplementary educational resources and clearer medical definitions to support deeper understanding and user engagement. This expert evaluation will guide the refinement for broader implementation in the project's next phase, evaluating acceptability and efficacy among non-expert users.
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