Industry players expect generative AI applications to play a major role in improving healthcare.

Applications of AI


AI Photo: VCG

AI Photo: VCG

As generative AI proves its power, industry players are excited to see more generative artificial intelligence (AI) applications to address problems in healthcare and beyond.

They said AI has the potential to further drive business efficiency and hoped to see greater international cooperation in AI development.

As the global healthcare sector faces major challenges such as an aging population and an imbalance between medical supply and demand, AI technology plays a key role in accelerating clinical research, new drug development and improving the efficiency of health insurance, as well as addressing evolving issues, Gong Rujing, chairman of Yidu Technology, a leading Chinese company in the AI ​​medical industry, told the Global Times.

The remarks were made at the 15th Annual Meeting of New Champions, also known as “Summer Davos,” currently being held in Dalian, northeast China's Liaoning Province, where AI has become one of the hottest topics along with other emerging technologies.

AI technology continues to advance in the healthcare industry, but there are still few examples of large-scale application in clinical settings.

When asked how the new generation of large-scale language models differ from previous models, Gong said that each iteration of AI technology is driven by the deep integration of expert knowledge and massive data, and is supported by efficient algorithms and powerful computing power. Computing power, data, algorithms and practical scenarios have become essential to train a good large-scale model. This requires strengthening domestic chip development and supply chain construction, improving data quality and diversity, deepening algorithm research and scenario exploration, and promoting verification and deployment in the medical field.

For example, integrating large-scale models in hospitals can change the way administrators and doctors interact with data, helping to achieve efficient research outcomes, improve hospital management, and increase diagnostic efficiency and quality. In addition, large-scale language models can also be applied in the biopharmaceutical field, such as mass health checkups, public health services, patient education, health consultations, rehabilitation management, target discovery, compound screening, and intelligent customer service in health insurance, Gong added.

Gong said that medical large-scale language models have potential application value in the fields of medicine, pharmaceuticals, insurance and patient care. Their role as a tool to increase productivity and efficiency is well established. Through continuous innovation and exploration, medical large-scale models are expected to open up broader perspectives in the medical field.

Large-scale models and AI technologies in the medical field have great potential but also face challenges such as interpretability, data security, privacy protection, etc. The training and application of large-scale models in medicine requires multilateral cooperation and support to make meaningful contributions to human health.

Gong called for strengthening collaboration among medical institutions, researchers and technology companies to jointly establish global standards and rules for AI development and increase cooperation to ensure the sustainable growth of artificial intelligence.

Catherine Daniel, interim director of the School of Cybernetics at the Australian National University, stressed the importance of international collaboration in AI governance and noted that industry needs to understand the complexity of AI systems and clarify which parts need to be governed.

“From sensor technology to privacy to the algorithms that are actually being used and how we look at their biases to how we think about the underlying information technology systems that connect them all, all of these things need to be carefully managed,” Daniel said.



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