With the development of artificial intelligence, what kind of surprise will it bring to medical treatment?

With the rapid advancement of medical artificial intelligence, its applications are becoming increasingly diverse. Today, AI is actively involved in various medical fields such as abdominal tumor analysis systems, precise liver cancer radiofrequency ablation surgeries, ultrasound robots, and intelligent diagnostic tools for childhood growth disorders. Additionally, providing doctors with smart medical tools and clinical decision support systems has become a major focus of AI in healthcare. Let’s explore this fascinating topic further. Data remains one of the biggest challenges in advancing AI technology. At the 2018 China Hospital Information Network Conference on March 23, Yang Jijiang, deputy director of the Web and Software Technology Research Center at Tsinghua University’s Institute of Information Technology, emphasized that “data is the foundation of artificial intelligence.” The complexity of medical environments and decision-making processes has led many AI-driven medical companies to shift their focus from algorithm development to data collection and management. Artificial intelligence is now being applied across multiple medical domains. Professor Kong Dexing from Zhejiang University highlighted that these include AI-assisted therapy, medical imaging, precision surgery, drug discovery, and health management solutions like virtual nurses. In some European and American countries, AI is already used in self-monitoring, dementia early detection, pancreatic cancer diagnosis, and ophthalmic imaging. One prominent example of AI in healthcare is IBM Watson, an AI platform that assists oncologists in making treatment decisions. Since 2017, Baiyang Intelligent Technology has been the exclusive distributor of Watson for oncology in China, with Watson Cancer Consultation Centers now deployed in 29 top-tier hospitals. AI has also made significant progress in practical applications. For instance, China has developed an abdominal medical image processing system that has saved numerous lives. A case involved a 37-year-old man diagnosed with advanced pancreatic head tumors, initially deemed unresectable. However, the system revealed that the tumor was only partially compressed and could be removed, leading to a successful operation and good recovery. In the field of ultrasonic robotics, AI is playing an increasing role. After acquiring high-quality images, these robots perform automatic screening, intelligent diagnosis, and help design surgical plans. Kong Dexing and his team have created a database with over 24,000 samples, including ultrasound and pathology data. Using deep learning algorithms, they developed the DE ultrasound robot, which achieved impressive results in thyroid nodule recognition. The accuracy of medical data is crucial for AI applications. Companies like Keda Xunfei are developing AI-assisted diagnostics for chest CT scans and mammography. Meanwhile, Ali Health's "Doctor You" aims to promote the overall development of medical AI, supported by big data and AI integration. As AI evolves, the importance of data becomes even more evident. Yang Jijiang noted that AI is not just about algorithms but also about combining diverse data sources. Cleaning and managing large datasets is essential, as even a small error can affect accuracy. Although AI cannot guarantee 100% accuracy due to individual variations, it significantly improves efficiency and provides valuable insights for doctors. Companies like Mindray have enhanced diagnostic accuracy by analyzing multiple parameters together. Their “smart alarm” system reduced false alarms and improved heart rate and pulse monitoring accuracy. While AI may not replace human doctors, it plays a vital role in supporting medical decisions and improving patient outcomes. In conclusion, while AI in healthcare still faces challenges, its potential to enhance medical practices is undeniable. As data quality improves and AI systems evolve, we can expect even greater advancements in the future.

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