Introduction

Lung cancer is one of the leading causes of death due to non-transmissible diseases in the world. Lung cancer screening is a key practice to prevent premature death and improve quality of life. However, efficiency and accuracy of screening are still subject to research.

In this context, development of an integrated AI system for lung cancer screening can be a potentially revolutionary solution. Our goal is to develop and apply a system AI that can help doctors identify high-risk images more quickly and accurately.

Technical details

Our AI system uses a combination of image recognition techniques (MRI) to analyze lung images and identify cancer signs. Our technology is based on a convolutional neural network (CNN) model that can be adapted to the specific needs of lung cancer screening.

Our application involves using a cloud platform for sharing and analyzing images, as well as a diagnostic software for medical assistance. We are also working on integrating augmented reality (AR) technologies to provide patients with detailed information about their condition.

Practical implications

Our AI system could represent an innovative solution for lung cancer screening, allowing doctors to identify high-risk images more quickly and accurately. This can lead to a reduction in non-essential hospital visits and premature discharges.

In addition, our technology can be used to develop personalized screening protocols for different populations, including indigenous communities and patients from rural regions. This can help improve access to lung cancer screening and reduce disparities in cancer care.

Conclusion

In conclusion, development of an integrated AI system for lung cancer screening can represent a potentially revolutionary solution to improve efficiency and accuracy of screening. We are excited to share our results and future prospects in the field of lung cancer screening.