Exploring the Use of Synthetic Data from AI Chatbots for Predicting Alzheimer's Disease: Methods for Validation and Barriers to Real-World Implementation

Introduction 1.1 Advancements of AI in HealthcareArtificial intelligence has rapidly advanced in the last decade for applications in healthcare from diagnostic tools to predictive analytics.Machine learning, a subset of the wide range of AI tools undergoing implementation, uses its sophisticated abilities to recognize patterns in subsequent data sets to help enable the early detection of chronic illnesses like Alzheimer's disease. Alzheimer's Disease and the Need for Early DiagnosisAlzeihemer's, "a type of dementia that affects memory, thinking and behavior" (Alzheimer's Association, 2025), affects millions worldwide and remains a significant challenge in public health.Early diagnosis is vital to allowing opportunities for intervention in the early growth activity of the disease, which could slow disease progression and improve patient outcomes.However, a tenacious challenge persists in the reliance of real world data, when developing machine learning models, raising ethical concerns over privacy, security, and bias. The Role of Synthetic Data and ChatbotsSynthetic data offers a potential solution by mimicking real-world medical datasets while avoiding privacy issues.Traditional approaches rely on statistical analytics and simulations to generate viable data sets, unlike this research, which will be proposing a new source of data generation: ChatGPT.Chatbots such as ChatGPT utilize conversational AI techniques like natural language processing systems to understand user questions and simulate responses to them (IBM, 2025).This can provide the edge of the implementations of conversational, contextual, and behavioral aspects onto the data generated, making the model increasingly authentic.This study investigates the viable use of AI generated synthetic data in effectively training machine learning models to predict the future onset of Alzheimer's disease.It specifically seeks to answer if synthetic data from AI chatbots can produce accurate and reliable predictive models with further investigation into methods that can validate these models' effectiveness and barriers that might prevent their adoption in real-world healthcare settings. Research Significance and Future ImplicationsThe desire of advancing this research topic originates from a fascination with AI's potential in predictive analytics within healthcare.Since the public debut of ChatGPT, AI has been implemented into various domains with a wide breadth of exploration of its applications, including health care.However, chatbots are generally utilized in website management, its true potential remaining unexamined.This interest is personally further driven by an aspiration to merge computer science and healthcare, through observing the challenges and opportunities within the healthcare field through familial connections.This research dives into uncharted territory, with the exploration of the intersection of natural language processing and predictive machine learning models, leveraging synthetic data generated by AI chatbots to train machine learning models for predicting the onset of Alzheimer's disease.This new approach seeks to plug the existing gaps in healthcare technology with emphasis on current ethical dilemmas on the usage of patient data.Exploring AI-generated synthetic data shows promise for a future in enhanced early diagnostic capabilities, and if successful, goes far beyond Alzheimer's.My research shows the possibilities of AI in healthcare developments, where it is further magnified for a maximized psychosocial