Medicine and health related to people's livelihood has always been the focus of the two sessions. At this year's two sessions, what proposals will representatives and members from the medical community bring that are closely related to people's livelihood? How can these suggestions accurately target industry needs?
The special column of "Focus on the Two Sessions" of the Obstetrics and Gynecology Network will take you to listen to the insights of the deputies and members.
During the National Two Sessions, in response to the chaos of popular science content on the platform, Professor Xu Congjian, member of the National Committee of the Chinese People's Political Consultative Conference and director of the Obstetrics and Gynecology Center of Westlake University School of Medicine, suggested that it should be solved by establishing a direct connection mechanism of "expert database + platform" and implementing a hierarchical and classified review model of medical science popularization.
Although documents such as the "Guiding Opinions on Establishing and Improving the Mechanism for the Release and Dissemination of All-Media Health Science Knowledge" have been issued, in actual implementation, due to different platform review standards, algorithm recommendation traffic-only theory, poor docking between the expert database and the platform, and low costs of violations, false science popularization is still repeatedly banned.
Pseudo-science popularization is rampant and seriously harmful. More than 90% of Internet users across the country have been exposed to health science popularization through short videos, and the huge audience provides a "hotbed" for the rapid spread of pseudo-science popularization.
There are blind spots in the current policy. In 2025, the four departments will jointly issue the "Notice on Regulating the Popularization of Medical Science Behavior of "Self-media", but the traditional "machine initial screening-manual review" model lacks "relevance review" on whether the popular science content matches the field of practice, and it is easy to form a blind spot in the review.
AI face swapping and counterfeiting methods have been iteratively upgraded. Some agencies use deepfake technology to create fake doctor videos with natural expressions and smooth language, making it difficult for ordinary users to distinguish between authenticity and fake. Some even use "real mask + AI voice change" to pass live verification, and the system has a very high misjudgment rate.
The algorithm mechanism intensifies "bad money drives out good money". Curiosity pseudo-science popularization is prioritized for recommendation due to its high click-through rate, while rigorous scientific content is overwhelmed. Existing algorithms are difficult to identify the scientific depth of content, making it difficult for high-quality science popularization to reach users.
In this regard, Professor Xu Congjian suggested:
Establish a direct connection mechanism of "expert database + platform". It is recommended that the National Health Commission, the Cyberspace Administration of China and other departments open up and upgrade the existing health science expert database, and develop standardized API interfaces for major platforms to access. The platform should force the account applying for medical science popularization permission to verify in real time through the expert database interface, not only to verify the physician qualification certificate, but also to verify whether the popular science content is consistent with the professional secondary discipline it has filed, and clearly identify its professional field on the homepage.
Implement the hierarchical and classified review mode of medical science popularization. It is recommended that relevant departments jointly formulate the "Guidelines for the Classification and Classification of Internet Medical Science Content", which divides the content into three levels: the first level is "common sense", which is automatically reviewed by the platform according to authoritative guidelines; the second level "professional level" needs to be issued by the certified corresponding specialist; The third level of "frontier/controversial level" needs to be marked with "this article is only a popular science and is not used as a basis for diagnosis and treatment", and peer review is introduced. All AI-generated popular science content must be prominently marked as "AI synthesis".
Optimize the algorithm recommendation logic and set up an exclusive traffic pool for "health science popularization". It is recommended that the Cyberspace Administration of China guide platform enterprises to introduce "scientific weight" in the field of health knowledge, and give an initial traffic tilt to the original science popularization published by certification experts. Establish a "library of high-quality popular science works" and provide long-term recommendations for selected works (such as winning national popular science awards and being reprinted by authoritative journals).
Establish an industry-wide "blacklist" sharing system. Implement cross-platform network-wide bans on accounts and MCN institutions behind them that have caused serious social impact or have been punished multiple times for publishing false medical science popularization to increase the cost of violations. Encourage national medical organizations and mainstream media to settle on the platform and establish a "Medical Science Popularization and Refuting Alliance". Open a convenient public reporting channel, and the platform must initiate an expert review process within 24 hours for suspected false information in the report concentration, and publicize the review results.
Professor Xu Congjian, member of the National Committee of the Chinese People's Political Consultative Conference and director of the Obstetrics and Gynecology Center of Westlake University School of Medicine, paid attention to the application of AI in medical care.
"At my age, I may be able to get by if I don't have a deep understanding of AI, but if doctors in my graduate generation don't know how to use AI tools, just like my generation didn't know how to use computers, it would be very troublesome." In an interview with Science and Technology Daily, Professor Xu Congjian said that AI will definitely cause a profound revolution in the medical industry, which is not a multiple-choice question, but a compulsory question.
Professor Xu Congjian revealed that an important direction he is currently promoting is the construction of "smart delivery rooms". In Hangzhou, he has raised this clinical need to his hospital and has received great attention from the health department. He envisions digitizing and intelligentizing various indicators in the delivery room through wearable devices and automatic monitoring to achieve automatic early warning and real-time monitoring like ICU. "It takes some difficult things to overcome, but the time has come."
Professor Xu Congjian predicts that 2027 will be the climax of the construction of intelligent delivery rooms. "The development of technology is a matter of course, and AI is enough to be applied in clinical practice; At the same time, 2027 is the Year of the Goat, and the number of births is relatively small, leaving a window period for the delivery room to configure new technologies. ”
Regarding the impact of AI on the medical industry, Professor Xu Congjian believes that "it will profoundly affect the development of the medical industry and is closely related to our daily activities." He mentioned that the National Health Commission has released several major scenario plans for the application of AI in the medical field, which is a long-term guidance. Let's not hide from AI, but actively embrace it. From clinical diagnosis and treatment, medical technology assistance, observation and follow-up to drug development and clinical research, it is necessary to pay close attention to and actively integrate into the progress of AI.
In the face of concerns in the industry about whether AI will make young doctors lose their basic skills, Professor Xu Congjian has a clear attitude: young doctors should be encouraged to use AI, but they need to be cautious, have a transition period, and have a guarantee. "With AI, we still have to emphasize the training of basic skills, basic knowledge and basic clinical thinking." Healthcare itself is a risky industry, and AI can reduce risks in some channels, but it cannot be fully entrusted. "For a long time, AI will not be able to completely replace clinical thinking, and we need to use it to ensure that we do a good job at the same time."
Content source: Shangguan News, Science and Technology Daily
Editor-in-charge:lucy