Data-driven future smart healthcare: Where should doctors, governments and businesses go? -AI4kids

Table of Contents

• introduction
• Research and application of chronic kidney disease
• Applications in disease prediction and risk assessment
• Challenges and risks in promoting smart medical technologies

introduction

The development and future of smart healthcare is a hot topic in the current medical field. With the development of medical technology and big data, smart healthcare has become one of the main directions of future healthcare. The core of smart healthcare lies in the application of artificial intelligence technology, machine learning, deep learning and other technologies to conduct research and application in disease prediction, risk assessment and precision treatment.

The development and evolution of smart healthcare is driven by cross-domain applications. With the advancement of technology, the application of smart medical care is becoming more and more extensive. The international development trend of smart healthcare is to achieve precision medicine, which improves the efficiency and accuracy of medical care through the integration of multiple technologies. The opportunity for the development of smart healthcare in Taiwan is due to insufficient medical resources and high medical costs, and the need to improve the utilization and efficiency of medical resources through smart medical technology.

AI technology in the biomedical field is one of the core technologies of smart medicine. AI technology can predict diseases and assess risks based on clinical data. Disease prediction based on clinical data refers to predicting whether a patient will develop a certain disease through data analysis and modeling. Tumor recognition through image recognition is to detect the tumor in the patient's body by analyzing image data.

Research and application of chronic kidney disease

Chronic kidney disease is a common disease, and machine learning can help doctors predict and assess the risk of chronic kidney disease. To obtain training data, you can use Kaggle or public medical data. In terms of data pre-processing, the main operations are data cleaning, conversion, and filling in gaps. In the application of machine learning to predict the stage of chronic kidney disease, models such as Random Forest, SVM, and XGBoost can be used. When using machine learning to assess the risk of chronic kidney disease, models such as Lasso Regression, Ridge Regression, and SVR can be used. The establishment of these models can help doctors make more accurate predictions and risk assessments of patients' conditions, thereby improving patients' treatment outcomes and survival rates.

Professor Wang Zhaoneng is a professor in the Department of Information Engineering at Asia University, focusing on the application of artificial intelligence and machine learning technologies in the medical field. He pointed out that with the development of smart medical technology, machine learning and artificial intelligence technology have become important tools in the medical field, which can help doctors with disease prediction, risk assessment and treatment plan design.

In the study of chronic kidney disease, Professor Wang Zhaoneng and his team used deep learning technology to combine multiple traditional machine learning models and successfully predicted the stage of chronic kidney disease and assessed the risk. In addition, they have also developed a smart medical platform that can realize the sharing and management of medical resources and provide patients with more personalized and precise medical services.

Applications in disease prediction and risk assessment

In terms of disease prediction models and disease risk assessment, a disease can be selected for classification and risk assessment. The establishment and application of these models can help doctors better understand the patient's disease condition and provide patients with more accurate treatment plans and care. Medical institutions can apply smart medical technology in practice to help doctors better detect signs of disease and provide more personalized and accurate treatment plans. At the same time, medical institutions can also strengthen the sharing and management of medical resources, improve medical efficiency and quality, and provide patients with better medical services and care.

In general, smart healthcare is one of the future development directions of medical care. The application of machine learning and artificial intelligence technologies can help doctors predict diseases and assess risks, thereby improving the efficiency and accuracy of medical treatment. Through the application of these technologies, patients can receive more accurate diagnosis and treatment, improving their survival rate and quality of life.

In addition, the application of smart medical technology can also help medical institutions and governments with public health management. For example, when an epidemic breaks out, smart medical technology can be used to track and monitor the spread of the disease, promptly detect and control the spread of the epidemic, and improve public health safety.

Challenges and risks in promoting smart medical technologies

Therefore, the promotion and application of smart medical technology is very important. Governments and medical institutions can increase investment in and research on smart medical technologies, promote the development and application of smart medical technologies, improve the utilization and efficiency of medical resources, and provide patients with better medical services and care. At the same time, patients can also actively participate in the application of smart medical technology to understand their own disease conditions, improve their understanding and awareness of the disease, and thus better protect their health.

Technology companies can further promote smart medical technologies, develop more advanced and efficient medical products and services, and provide more comprehensive and precise support to medical institutions and patients.

In short, the development of smart medical technology is an important trend in the current medical industry. Using smart medical technology to predict and assess disease risks can help doctors improve the accuracy of diagnosis and treatment, and improve patients' treatment outcomes and survival rates. Governments, medical institutions and technology companies need to work together to promote the development and application of smart medical technologies and provide patients with better medical services and care.

However, there are still some challenges and risks in the application of smart medical technology. For example, issues such as the collection and sharing of medical data, privacy and security issues, etc. Therefore, when promoting the development and application of smart medical technology, we need to pay attention to the formulation and implementation of relevant laws and policies to ensure the rational use and development of technology.

In short, the development and application of smart medical technology will bring important impacts and challenges to the medical industry. Governments, medical institutions and technology companies need to work together to promote the development and application of technology and ensure the rational use and development of technology.

Want to learn more?

AI Medical Special Practice Camp: https://ai4kids.ai/ai-camp-ai-for-health/ Professor Wang Wei-zhong's team from National Taiwan University, Professor Wang Zhao-neng and Professor Wu Hua-shan's team from Asia University, and medical experts will teach the analysis and application of medical images and big data, and see how big data and AI will affect Taiwan's future medical and care fields. The hackathon event allowed all students to work in teams and produce their own AI medical results.

Want to learn more? Click the button for more information!