The Bispectral Index (BIS) has been widely utilized to monitor patients' levels of consciousness during anesthesia. Despite its practicality and prevalence, BIS monitors have been reported to show erroneous readings due to various factors that interfere with the proper reading of the brain's electrical activity. We present a case where the BIS monitor misinterpreted the patient's cardiac activity as her neural activity and resulted in a falsely elevated BIS number despite proper placement and lack of underlying patient medical condition, including neurological injury. It is crucial to remain vigilant about monitoring and understanding BIS readings to assess patients' awareness and effectiveness of anesthesia properly.

Artificial intelligence (AI) language generation models, such as ChatGPT, have the potential to revolutionize the field of medical writing and other natural language processing (NLP) tasks. It is crucial to consider the ethical concerns that come with their use. These include bias, misinformation, privacy, lack of transparency, job displacement, stifling creativity, plagiarism, authorship, and dependence. Therefore, it is essential to develop strategies to understand and address these concerns. Important techniques include common bias and misinformation detection, ensuring privacy, providing transparency, and being mindful of the impact on employment. The AI-generated text must be critically reviewed by medical experts to validate the output generated by these models before being used in any clinical or medical context. By considering these ethical concerns and taking appropriate measures, we can ensure that the benefits of these powerful tools are maximized while minimizing any potential harm. This article focuses on the implications of AI assistants in medical writing and hopes to provide insight into the perceived rapid rate of technological progression from a historical and ethical perspective.