Critical Role of Artificially Intelligent Conversational Chatbot

Artificially intelligent chatbot, such as ChatGPT, represents a recent and powerful advancement in the AI domain. Users prefer them for obtaining quick and precise answers, avoiding the usual hassle of clicking through multiple links in traditional searches. ChatGPT's conversational approach makes it comfortable and accessible for finding answers quickly and in an organized manner. However, it is important to note that these chatbots have limitations, especially in terms of providing accurate answers as well as ethical concerns. In this study, we explore various scenarios involving ChatGPT's ethical implications within academic contexts, its limitations, and the potential misuse by specific user groups. To address these challenges, we propose architectural solutions aimed at preventing inappropriate use and promoting responsible AI interactions.Comment: Extended version of Conversation 2023 position pape

Machine learning in the prediction of cancer therapy

Resistance to therapy remains a major cause of cancer treatment failures, resulting in many cancer-related deaths. Resistance can occur at any time during the treatment, even at the beginning. The current treatment plan is dependent mainly on cancer subtypes and the presence of genetic mutations. Evidently, the presence of a genetic mutation does not always predict the therapeutic response and can vary for different cancer subtypes. Therefore, there is an unmet need for predictive models to match a cancer patient with a specific drug or drug combination. Recent advancements in predictive models using artificial intelligence have shown great promise in preclinical settings. However, despite massive improvements in computational power, building clinically useable models remains challenging due to a lack of clinically meaningful pharmacogenomic data. In this review, we provide an overview of recent advancements in therapeutic response prediction using machine learning, which is the most widely used branch of artificial intelligence. We describe the basics of machine learning algorithms, illustrate their use, and highlight the current challenges in therapy response prediction for clinical practice