The development of Artificial Intelligence (AI) hospitals and the biomedical industry chain is a crucial part of modern health care systems. It primarily involves the following aspects:

AI Hospitals: The application of AI technology is vast in hospitals. For instance, AI can be used to enhance the diagnostic accuracy of diseases by identifying signs of illness through the analysis of medical imaging (such as X-rays, CT and MRI scans). Moreover, AI can predict the progression and treatment response of a patient's condition, assisting doctors in devising more effective treatment plans.

Biomedical Industry Chain: In the biomedical industry, AI can be used for early detection and prevention of diseases and the development of new drugs. For example, AI can analyze large amounts of genomic data to find genetic markers for diseases, helping us detect diseases earlier and take preventative measures. Furthermore, AI can screen numerous compounds to identify potential new drugs.

Innovation and Collaboration: The development of AI hospitals and the biomedical industry chain requires constant innovation and collaboration. Innovation includes not only technological innovation but also business and management model innovation. Collaboration calls for cross-industry, cross-disciplinary, and cross-border cooperation to drive the application of AI in healthcare.

Ethics and Regulations: Although the application of AI in the medical and biomedical fields holds great potential, we must also consider its ethical and legal issues. For instance, we need to ensure the protection of patient data privacy and that AI decisions are fair and transparent.

Overall, the development of AI hospitals and the biomedical industry chain is a process filled with both opportunities and challenges. We need to combine multidisciplinary knowledge and skills and embrace the spirit of innovation and collaboration to overcome these challenges and fully harness the potential of AI.

 Urstroke –Solution for the Acute Ischemia Stroke

YH., Lin, SC., Chung, HW. et al. The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net. Eur Radiol (2023). https://doi.org/10.1007/s00330-023-09622-z

The software automatically detects, segments, and calculates the volume of the stroke lesion and classifies the edema type. 
It can enable physicians to obtain rapid, accurate and individual information of acute ischemic stroke, allowing precise and individualized treatment of choice for stroke.

AI in Medical Image

Deep learning has been applied to a variety of medical imaging tasks, including but not limited to segmentation of acute ischemic stroke (left)1, classification of parotid gland tumors (middle)2, segmentation of teeth (right)3, etc.

1. Juan, CJ., Lin, SC., Li, YH. et al. Improving interobserver agreement and performance of deep learning models for segmenting acute ischemic stroke by combining DWI with optimized ADC thresholds. Eur Radiol 32, 5371–5381 (2022). https://doi.org/10.1007/s00330-022-08633-6
2. Juan CJ, Huang TY, Liu YJ, Shen WC, Wang CW, Hsu K, Shin N, Chang RF. Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging. NMR Biomed. 2022 Mar;35(3):e4642. doi: 10.1002/nbm.4642. 
3. Hsu, K., Yuh, DY., Lin, SC. et al. Improving performance of deep learning models using 3.5D U-Net via majority voting for tooth segmentation on cone beam computed tomography. Sci Rep 12, 19809 (2022). https://doi.org/10.1038/s41598-022-23901-