In the realm of education, the challenge lies in effectively utilizing Artificial Intelligence to transform medical learning. Artificial Intelligence Applications Using ChatGPT in Education: Case Studies and Practices, authored by Muhammad Shahzad Aslam and Saima Nisar, offers insights into this issue. With expertise in Medical and Health Education, and Health Informatics, the authors explore AI's potential in reshaping medical education. Traditional medical education struggles to keep up with expanding knowledge and evolving medical science, leaving educators and students overwhelmed by vast information. Ethical concerns, such as plagiarism, further complicate matters. A solution is needed that blends technology with effective teaching. Artificial Intelligence Applications Using ChatGPT in Education: Case Studies and Practices proposes such a solution. By harnessing ChatGPT's capabilities as an AI chatbot, the book suggests a self-guided learning tool. Backed by case studies, the authors demonstrate how ChatGPT can become a personalized tutor, helping students grasp complex medical concepts at their own pace. The book also delves into the ethical aspects of AI integration, ensuring responsible use in academia.
The average age of people has increased due to advances in health sciences, which has led to an increase in the elderly population. This is positive news, but it also raises questions about the quality of independent living for older people. Clinicians use Activities of Daily Living (ADLs) to assess older people's ability to live independently. In recent years, portable computing devices have become more present in our daily lives. Therefore, a software system that can detect ADLs based on sensor data collected from wearable devices is beneficial for detecting health problems and supporting health care. In this context, this book presents several machine learning-based approaches for human activity recognition (HAR) using time-series data collected by wearable sensors in the home environment. In the first part of the book, machine learning-based approaches for atomic activity recognition are presented, which are relatively simple and short-term activities. In the second part, the algorithms for detecting long-term and complex ADLs are presented. In this part, a two-stage recognition framework is also presented, as well as an online recognition system for continuous monitoring of HAR. In the third and final part, a novel approach is proposed that not only solves the problem of data scarcity but also improves the performance of HAR by implementing multitask learning-based methods. The proposed approach simultaneously trains the models of short- and long-term activities, regardless of their temporal scale. The results show that the proposed approach improves classification performance compared to single-task learning.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.