Microsoft Bing vs. Google Bard in Neurology: A Comparative Study of AI-Generated Patient Education Material

Background Patient education is an essential component of healthcare, and artificial intelligence (AI) language models such as Google Bard and Microsoft Bing have the potential to improve information transmission and enhance patient care. However, it is crucial to evaluate the quality, accuracy, and understandability of the materials generated by these models before applying them in medical practice. This study aimed to assess and compare the quality of patient education materials produced by Google Bard and Microsoft Bing in response to questions related to neurological conditions. Methods A cross-sectional study design was used to evaluate and compare the ability of Google Bard and Microsoft Bing to generate patient education materials. The study included the top ten prevalent neurological diseases based on WHO prevalence data. Ten board-certified neurologists and four neurology residents evaluated the responses generated by the models on six quality metrics. The scores for each model were compiled and averaged across all measures, and the significance of any observed variations was assessed using an independent t-test. Results Google Bard performed better than Microsoft Bing in all six-quality metrics, with an overall mean score of 79% and 69%, respectively. Google Bard outperformed Microsoft Bing in all measures for eight questions, while Microsoft Bing performed marginally better in terms of objectivity and clarity for the epilepsy query. Conclusion This study showed that Google Bard performs better than Microsoft Bing in generating patient education materials for neurological diseases. However, healthcare professionals should take into account both AI models’ advantages and disadvantages when providing support for health information requirements. Future studies can help determine the underlying causes of these variations and guide cooperative initiatives to create more user-focused AI-generated patient education materials. Finally, researchers should consider the perception of patients regarding AI-generated patient education material and its impact on implementing these solutions in healthcare settings. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This study did not receive any funding ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors.