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Introduction: The emergence of artificial intelligence (AI) chat programs has opened two distinct paths, one enhancing interaction and another potentially replacing personal understanding. Ethical and legal concerns arise due to the rapid development of these programs. This paper investigates academic discussions on AI in medicine, analyzing the context, frequency, and reasons behind these conversations. Methods: The study collected data from the Web of Science database on articles containing the keyword "ChatGPT" published from January to September 2023, resulting in 786 medically related journal articles. The inclusion criteria were peer-reviewed articles in English related to medicine. Results: The United States led in publications (38.1%), followed by India (15.5%) and China (7.0%). Keywords such as "patient" (16.7%), "research" (12%), and "performance" (10.6%) were prevalent. The Cureus Journal of Medical Science (11.8%) had the most publications, followed by the Annals of Biomedical Engineering (8.3%). August 2023 had the highest number of publications (29.3%), with significant growth between February to March and April to May. Medical General Internal (21.0%) was the most common category, followed by Surgery (15.4%) and Radiology (7.9%). Discussion: The prominence of India in ChatGPT research, despite lower research funding, indicates the platform's popularity and highlights the importance of monitoring its use for potential medical misinformation. China's interest in ChatGPT research suggests a focus on Natural Language Processing (NLP) AI applications, despite public bans on the platform. Cureus' success in publishing ChatGPT articles can be attributed to its open-access, rapid publication model. The study identifies research trends in plastic surgery, radiology, and obstetric gynecology, emphasizing the need for ethical considerations and reliability assessments in the application of ChatGPT in medical practice. Conclusion: ChatGPT's presence in medical literature is growing rapidly across various specialties, but concerns related to safety, privacy, and accuracy persist. More research is needed to assess its suitability for patient care and implications for non-medical use. Skepticism and thorough review of research are essential, as current studies may face retraction as more information emerges. [ABSTRACT FROM AUTHOR]

Abstract Wind speed and power forecast is an essential component to ensure grid stability and reliability. The traditional forecasting methods fail to address the non-linearity in the wind speed time-series, thus paving way for machine intelligent algorithms. This paper discusses a hybrid machine intelligent wind forecasting model utilizing different variants of Support Vector Regression (SVR) built on wavelet transform. Various performance indices are evaluated to identify the possible best one among four different machine learning regressors for wind forecasting application. Apart from standard ε-SVR and LS-SVR, two new regression models, namely, ε-Twin Support vector regression (ε-TSVR) and Twin Support vector regression (TSVR) are used to forecast short-term wind speed, and are compared with Persistence model for four wind farm sites. The effect of larger dataset on forecasting performance is evaluated for two wind farm sites from USA and India. Further, wind power ramp events are investigated at different hub heights and the forecasting performance of different variants of SVR is compared for five wind farm sites. Highlights • Machine intelligent SVR variants are discussed for short-term wind forecasting. • Overfitting and underfitting scenarios of variants of SVR are assessed. • Forecasting performance of SVR variants is assessed during wind power ramp event. • Wind power ramp events are analyzed at different hub heights for 5 wind farm sites. [ABSTRACT FROM AUTHOR]