Industry players are looking forward to more generative artificial intelligence (AI) applications to address issues such as medical care as generative AI is displaying its power. 

They said AI could further promote business efficiency, and they hope enhanced international cooperation for AI development can be made a reality.

The global health care sector faces significant challenges, such as an aging population and an imbalance between medical supply and demand, so AI technology is accelerating efficiency improvements in clinical research, new drug development, and health insurance, playing a crucial role in addressing the evolving issues, Gong Rujing, chairman of Yidu Tech, a leading company in China's AI medical industry, told the Global Times.

The remarks were made during the on-going 15th Annual Meeting of the New Champions, also known as Summer Davos, in Dalian, Northeast China's Liaoning Province, AI is one of the hottest topics along with other emerging technologies.

In the healthcare industry, although AI technology continues to advance, there are still few cases of large-scale application in clinical scenarios. 

When asked the question about how do the new generation of large language models differ from their predecessors, Gong said that the iterations of AI technology are driven by the deep integration of specialized knowledge and vast amounts of data, supported by efficient algorithms and powerful computing capabilities. To train an excellent large model, computing power, data, algorithms, and practical scenarios have become indispensable. This necessitates strengthening domestic chip development and supply chain construction, improving data quality and diversity, deepening algorithm research and scenario exploration, and promoting validation and deployment in medical contexts.

For example, the integration of large models in hospitals can transform the way managers and doctors interact with data, aiding efficient research output, refined hospital management, and improved diagnostic efficiency and quality. Furthermore, large language models have applications in mass health screenings, public health services, patient education, health consultations, rehabilitation management, and in biopharmaceutical fields like target discovery, compound screening, and intelligent customer service in health insurance, Gong added. 

Gong said large language models for medicine hold potential application value across medicine, pharmaceuticals, insurance, and patient care. Their role as productivity and efficiency tools has been well established. Through continuous innovation and exploration, medical large models are expected to open up broader prospects in the medical field.

Despite the vast potential of large models and AI technologies in the healthcare sector, they also face challenges such as interpretability, data security, and privacy protection. The training and application of medical large models require multi-faceted cooperation and support to make a meaningful contribution to human health. 

Gong calls for strengthened collaboration among medical institutions, researchers, and technology companies, as well as enhanced cooperation to jointly establish global standards and rules for AI development, ensuring sustainable growth in artificial intelligence.

Katherine Daniell, interim director of the School of Cybernetics from the Australia National University, emphasized the importance of international cooperation for AI governance, while noting that the industry should understand the complexity of AI system and ensure which part need to be governed. 

"From sensing technology to privacy, as well as the actual algorithms that are used, how we can look at their biases, how we think about the underlying Information technology systems that are linking those up, all of these should be carefully governed," Daniell said. 

Source:Gobal Times