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OBJECTIVE: Artificial intelligence (AI)-based text generators such as Chat Generative Pre-Trained Transformer (ChatGPT) have come into the forefront of modern medicine. Given the similarity between AI-generated and human-composed text, tools need to be developed to quickly differentiate the two. Previous work has shown that simple grammatical analysis can reliably differentiate AI-generated text from human-written text. STUDY DESIGN: In this study, ChatGPT was used to generate 25 articles related to obstetric topics similar to those made by the American College of Obstetrics and Gynecology (ACOG). All articles were geared towards patient education. These AI-generated articles were then analyzed for their readability and grammar using validated scoring systems and compared to real articles from ACOG. RESULTS: Characteristics of the 25 AI-generated articles included fewer overall characters than original articles (mean 3,066 vs. 7,426; p < 0.0001), a greater average word length (mean 5.3 vs. 4.8; p < 0.0001), and a lower Flesch-Kincaid score (mean 46 vs. 59; p < 0.0001). With this knowledge, a new scoring system was develop to score articles based on their Flesch-Kincaid readability score, number of total characters, and average word length. This novel scoring system was tested on 17 new AI-generated articles related to obstetrics and 7 articles from ACOG, and was able to differentiate between AI-generated articles and human-written articles with a sensitivity of 94.1% and specificity of 100% (Area Under the Curve [AUC] 0.99). CONCLUSION: As ChatGPT is more widely integrated into medicine, it will be important for health care stakeholders to have tools to separate originally written documents from those generated by AI. While more robust analyses may be required to determine the authenticity of articles written by complex AI technology in the future, simple grammatical analysis can accurately characterize current AI-generated texts with a high degree of sensitivity and specificity. KEY POINTS: · More tools are needed to identify AI-generated text in obstetrics, for both doctors and patients.. · Grammatical analysis is quick and easily done.. · Grammatical analysis is a feasible and accurate way to identify AI-generated text..
RESUMEN
BACKGROUND: Opioid misuse is a public health crisis, and unused postoperative opioids are an important source. Although 70% of pills prescribed go unused, only 9% are discarded. This study evaluated whether an inexpensive pill-dispensing device with mail return capacity could enhance disposal of unused opioids after cancer surgery. METHODS: A prospective pilot study was conducted among adult patients who underwent major cancer-related surgery. Patients received opioid prescriptions in a mechanical device (Addinex) linked to a smartphone application (app). The app provided passwords on a prescriber-defined schedule. Patients could enter a password into the device and receive a pill if the prescribed time had elapsed. Patients were instructed to return the device and any unused pills in a disposal mailer. The primary end point was feasibility of device return, defined as ≥50% of patients returning the device within 6 weeks of surgery. Also explored was total pill use and return as well as patient satisfaction. RESULTS: Among 30 patients enrolled, the majority (n = 24, 80%) returned the device, and 17 (57%) returned it within 6 weeks of surgery. In total, 567 opioid pills were prescribed and 170 (30%) were used. Of 397 excess pills, 332 (84% of unused pills, 59% of all pills prescribed) were disposed of by mail. Among 19 patients who obtained opioids from the device, most (n = 14, 74%) felt the benefits of the device justified the added steps involved. CONCLUSIONS: Use of an inexpensive pill-dispensing device with mail return capacity is a feasible strategy to enhance disposal of unused postoperative opioids.
