Topic: Artificial Intelligence for Pancreato-biliary Surgery: Current Status and Future Challenges

Pancreato-biliary surgery is a complex and challenging field that involves diagnosing and treating disorders affecting the pancreas and bile ducts. These disorders range from benign tumors to life-threatening cancers, and the surgical procedures used to treat them are technically demanding and come with a substantial risk of morbidity and even mortality. In recent years, there has been a growing interest in using artificial intelligence (AI) to improve diagnosis and treatment planning, reduce surgery-related risks, and enhance patient outcomes in pancreato-biliary surgery. One area where AI may be useful in this field is in the analysis of radiological images. Machine learning algorithms can analyze imaging data to identify tumors and abnormalities with greater accuracy, leading to better treatment planning and surgical outcomes. Additionally, AI can analyze vast amounts of clinical data, including medical histories, imaging, and laboratory results, to develop personalized treatment plans for patients, identifying those at high risk for complications during surgery. Apart from improving diagnosis and treatment planning, AI is also driving innovation in surgical techniques and tools, such as computer vision and robotic surgery systems that use AI algorithms to guide surgical instruments, thus potentially increasing the overall precision and accuracy. Furthermore, AI applications can offer surgeons new directions in surgical simulation and education.

Therefore, this Special Issue, "Artificial Intelligence in Pancreato-Biliary Surgery: Development Status and Future Challenges," aims to explore how AI can transform the field of pancreato-biliary surgery by improving patient outcomes, reducing the complexity and risk associated with these surgeries, and identifying future challenges for the integration of AI in clinical practice. Topics include but are not limited to the following:

1. Artificial intelligence & early detection of pancreatic cancer；

2. Artificial intelligence & imaging: risk and survival prediction in patients with pancreatic adenocarcinoma；

3. Deep learning & preoperative computed tomography imaging & pancreatic cystic neoplasms；

4. Artificial intelligence & diagnosis of acute pancreatitis； 

5. Machine learning automatic pancreas segmentation in multimodal imaging；

6. CT and MRI for pancreatic tumors；

7. Artificial intelligence & pancreatic cancer: diagnosis, limitations, and the future prospects；

8. Artificial intelligence & Radiomics for diagnosis of pancreatic cancer； 

9. Machine learning & postoperative pancreatic fistula prediction；

10. Machine learning, computer vision & artificial intelligence in cholecystectomy； 

11. Artificial intelligence & surgical safety in pancreato-biliary surgery；

12. Intraoperative and postoperative outcomes of robot-assisted cholecystectomy；

13. Computer-aided diagnosis of gallbladder polyps based on high-resolution ultrasonography； 

14. Survival prediction models of gallbladder carcinoma.