Ethical Dimensions of Artificial Intelligence Chatbots Use in Rare Disease Diagnosis: A Scoping Review - Bélisle, Alexia; Lavoie, Marie-Océane; Banville, Frédéric; Haidar, Hazar (Canadian Journal of Bioethics / Revue canadienne de bioéthique, 2026)
Introduction: A rare disease is a health condition affecting a small percentage of the population, characterized by its low prevalence and the potential to result in chronic disabilities. Symptoms of these diseases may overlap with those of more prevalent conditions, complicating the diagnostic process. Additionally, many rare diseases are genetically inherited, and their diagnosis often requires specialized medical expertise and advanced diagnostic tools, highlighting the need for targeted approaches in healthcare. Consequently, the integration of artificial intelligence (AI) tools, including chatbots, presents a promising opportunity to support diagnostic processes in healthcare. However, their use raises ethical concerns, particularly related to data privacy, patient confidentiality, and the transparency of AI-driven recommendations. Objective: This study explores the ethical issues arising from the use of AI chatbots in rare disease diagnosis. Method: We conducted a scoping review related to ethical issues raised using chatbots in rare diseases diagnosis, searching databases and reference lists between January 1, 2010, and February 10, 2024. Results: Following screening process, six studies were included in the review. Data were grouped into four themes: 1) trust in AI chatbot; 2) humanization/dehumanization of AI; 3) data security and commercialization; and 4) psychometric considerations. Discussion: AI chatbots hold promise for diagnosing rare diseases, but challenges like data scarcity, diagnostic accuracy, and trust remain. Addressing these issues, particularly through improved data representativeness and enhanced collaboration with healthcare professionals, is crucial for the effective integration of AI in clinical practice. Future research should focus on overcoming these limitations to ensure AI can serve as a reliable decision-support tool in rare disease diagnosis.