The medical care industry is on the cusp of an extraordinary period, with large language models (LLMs) arising as incredible assets for reclassifying medical care elements. This paper investigates the potential and effect of LLMs in different parts of medication, including diagnostics, patient consideration, drug revelation, and medical services organization. It dives into the open doors and difficulties introduced by LLMs, accentuating the moral contemplations and the requirement for capable reception. By looking at late turns of events and contextual investigations, this paper offers a brief look into the developing scene of medical services, where LLMs are ready to assume a focal part in reshaping the eventual fate of medication.

Globally, the problem of chronic diseases like diabetes, hypertension, and heart disease is getting worse. These diseases have a significant impact on patients and healthcare systems. Innovative technologies like chatbots, which promise to improve the management of chronic diseases, have emerged as potential solutions to these difficulties. Chatbots are man- made brainpower (artificial intelligence) programs that can recreate discussions with people and give customized and practical consideration. The primary purposes of this study are to investigate the most important applications of chatbots in the management of chronic diseases, including personalized education, symptom monitoring, medication management, and lifestyle coaching. In addition, it discusses the potential ad- vantages and disadvantages of chatbots in the management of chronic diseases, including the need for human oversight, data privacy, and patient engagement. The paper concludes that, although chatbots have the potential to transform the management of chronic diseases by providing personalized and cost-effective care, careful consideration of ethical, legal, and regulatory frameworks is required for their integration into healthcare systems.

This is a review of chatbots in the field of mHealth

This is a WIP (Work in progress) article to determine hate speech in social network using ML

Malware for Android is becoming increasingly dangerous to the safety of mobile devices and the data they hold. Although machine learning techniques have been shown to be effective at detecting malware for Android, a comprehensive analysis of the methods used is required. We review the current state of Android malware detection using machine learning in this paper. We begin by providing an overview of Android malware and the security issues it causes. Then, we look at the various supervised, unsupervised, and deep learning machine learning approaches that have been utilized for Android malware detection. Additionally, we present a comparison of the performance of various Android malware detection methods and talk about the performance evaluation metrics that are utilized to evaluate their efficacy. Finally, we draw attention to the drawbacks and difficulties of the methods that are currently in use and suggest possible future directions for research in this area. In addition to providing insights into the current state of Android malware detection using machine learning, our review provides a comprehensive overview of the subject.

This is research about how reinforcement learning benefits autonomous drone navigation and control