The role of artificial intelligence in improving patient communication and shared decision-making in urology: A systematic review

BackgroundEffective communication and shared decision-making are essential for optimising urological care, making informed decisions, and improving patient outcomes. The integration of artificial intelligence (AI) in urology has the potential to act as a supportive tool in this process. This review aims to evaluate how AI-based tools support and enhance patient-provider communication and shared decision-making within urological care.MethodsFollowing PRISMA 2020 guidelines, a systematic search was performed using Cochrane, EMBASE, MEDLINE, and Scopus for literature published between 2019 and 2024. Search terms included 'Artificial Intelligence', 'Urology', 'Shared Decision-Making', and 'Communication'. Studies were screened using our predefined inclusion and exclusion criteria. Three primary themes were identified, through which the studies were analysed.ResultsOf 807 identified studies, 14 were appropriate for inclusion. Only 14 studies met criteria because most excluded articles did not evaluate AI tools designed for communication, health literacy, or shared decision-making. AI-driven tools, particularly large language models (LLMs), show the potential to reduce knowledge gaps for diverse literacy levels and improve patient comprehension. These aids may improve the readability of complex medical content and translate information with cultural sensitivity. AI may also enhance communication between patients and healthcare providers by automating repetitive tasks, such as responding to frequently asked questions. However, AI has limitations, with different LLMs displaying variable levels of effectiveness and accuracy across urological conditions.ConclusionsThe integration of AI has the potential to enhance communication and promote shared decision-making in urology. However, patients should use AI as a complement to physicians rather than a replacement. To confidently determine their role and ensure AI output accuracy, further studies, including validation against clinical standards and real-world accuracy are required.