1 Tavis Smiley School of Communication, Texas Southern University, USA.
2 Center for Data Science, College of Arts and Sciences, American University, Washington DC, USA.
3 Department of Electrical and Computer Engineering, University of Delaware, USA.
4 Pompea College of Business, University of New Haven, Connecticut, USA.
5 Business Analytics Program (MSBA), University of Louisville, Kentucky, USA.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(01), 1227-1237
Received on 27 August 2024; revised on 10 October 2024; accepted on 13 October 2024
This study presents the development and evaluation of an AI-driven communication tool designed to stimulate memory recall in patients with Alzheimer’s disease and other neurodegenerative disorders. The tool integrates machine learning algorithms, including Random Forest and Long Short-Term Memory (LSTM) networks, with mass communication theories to deliver personalized and adaptive communication strategies. The AI models were trained and tested on patient interaction data, enabling the tool to dynamically adjust its prompts based on real-time cognitive states. Performance metrics such as accuracy, precision, and AUC-ROC were used to assess model efficacy, with the Random Forest model achieving the highest performance across all metrics. Clinical trials demonstrated significant improvements in memory recall and emotional engagement in patients using the AI tool compared to standard cognitive stimulation therapy. Caregivers also reported higher satisfaction with the tool's usability and impact on patient interaction quality. These findings highlight the potential of AI-driven, personalized communication tools to revolutionize Alzheimer's care, offering scalable, adaptive interventions that improve cognitive and emotional health. Academically, this work advances the integration of AI and health communication, emphasizing culturally sensitive and patient-centered approaches. In healthcare, it presents a promising non-pharmacological intervention that can be easily scaled for widespread use, potentially reducing caregiver burden and improving patient outcomes.
Alzheimer's disease; AI-driven communication; Memory recall; Cognitive stimulation; Health communication
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Hafsat Bello Danmaisoro, Babajide J. Odejide, Eric Jhessim, Adeola Raji and Ayinoluwa Feranmi Kolawole. Design and development of an AI-driven communication model for stimulating memory recall in patients with Alzheimer’s and other neurodegenerative diseases. World Journal of Advanced Research and Reviews, 2024, 24(1), 1227-1237. Article DOI: https://doi.org/10.30574/wjarr.2024.24.1.3052