"Pay no attention to the man behind the curtain" was first heard in The Wizard of Oz in 1939. But allow me to take you, for a moment, behind the curtain at AJN. Each month as we put the issue together, often one of the last components to be included is my editorial. Sometimes I wait to see if there is something new and interesting in health care or in the nursing world that I want to opine on. More often, I look to draw inspiration from the "book," and its collection of articles, columns, and news pieces.
This month, I knew our senior editor Jacob Molyneux was writing an AJN Reports on artificial intelligence (AI). What I didn't know was the angle the piece would take. As you know, several AI camps exist. There is the "everything about AI is negative" camp-the group that will never capitulate to anything having to do with AI. In common parlance, these are your stragglers and laggards. There is the "moderate" camp. Folks in this camp are cautious and may have set rules about AI's use (they don't agree with using ChatGPT to author papers, for example) but have not completely rejected the notion that this new technology is a part of our present and will be a part of our future (in plagiarism detection software or conversational chatbot applications, for example). And lastly, there is the "nonpartisan" group. People in this group are not fully committed to any one AI position, they are in the "wait and see" camp. Similar arguments occurred when evidence-based practice (EBP) methodologies were first introduced as an important part of nursing practice in the 1990s. I recall many discussions about EBP being "cookbook" medicine that would, as Gail J. Mitchell says in her 1999 Nursing Science Quarterly article, obstruct the "nursing process, human car[ing] and professional accountability." Similar arguments were made about the proliferation of clinical decision support systems integrated into electronic health records. No doubt, a healthy dose of skepticism is always warranted.
I feel privileged to be immersed in a discussion about a disruptive technology. As an editor, yes, I am concerned about the role AI language models, like ChatGPT, may play in threatening the very assembly and distribution of our science. Recently at AJN, a prospective author disclosed the use of an AI language model tool to help them "generate ideas" for their submission. We also recently received a piece of artwork that aroused a high degree of suspicion for being AI generated.
Despite this, I am a glass-half-full type of person. This summer, along with other members of the AJN editorial team, I attended the International Academy of Nursing Editors (INANE) annual meeting and heard a wonderful presentation from Justin Fontenot, AJN contributor and editor-in-chief of Teaching and Learning in Nursing. Fontenot gave a contemplative presentation on how AI might be used to conduct efficient literature searches for doctoral students. This technology has wide application not only for these students, but for established researchers, librarians, and journal editors. To enhance my own development, I have begun to experiment with some of these platforms (Research Rabbit, Elicit, and Lippincott's AI virtual research assistant, LARA: https://lww.com/Pages/ask-lara.aspx) and I am quite impressed.
AI in a variety of forms will be part of nursing's future and at AJN we want to be ahead of the curve. In this issue, Molyneux's report, "Artificial Intelligence and Nursing: Promise and Precaution," provides foundational information and a nice, broad overview for nurses of how AI might be potentially used. In reading it, I learned several new important ways in which AI might enhance and even contribute to an improved nursing practice.
Taking you further behind our curtain, this month's cover photo was rejected at first. We thought it would send the message to readers that robots will be replacing nurses and performing nursing functions. After some discussion, and a little drink from the glass-half-full perspective, we began to imagine a world where a robotic assistant takes vitals exactly when they should be taken. It might even collect biophysical data (skin temperature, hydration status, or facial expressions) that could be placed into a predictive algorithm and notify the nurse or rapid response team to an important change in status. AI just might be a viable solution to a growing nurse staffing problem.