本报讯 （记者袁传玺）近日，第三届中国抗衰老促进会功能医学专业委员会年会暨学术会议在上海隆 重召开。作为国内功能医学与抗衰老领域的高水平学术盛会，本次会议汇聚了近千名国内外权威专家、 学者及产业代表，共同探讨功能医学在慢病管理、抗衰老与健康促进中的前沿发展和本土化路径。会 上，京东健康全职功能医学专家赵向府荣获个人奖项"2025功能医学卓越医生奖"。 赵向府系京东健康全职功能医学专家，其所从事的功能医学业务隶属于京东健康高端健康管理频道。京 东健康高端健康管理频道，是一个基于国际认证功能医学与生活方式医学理念构建的系统化健康服务平 台。它以"从根源寻找健康答案"为核心，旨在突破传统"头痛医头、脚痛医脚"的局限，通过科学评估与 个性化干预，致力于帮助用户找到并解决健康问题的根本原因。 该频道汇聚了国内获得认证的功能医学与生活方式医学专家，他们兼具国际视野与丰富临床经验，能够 为用户提供精准专业的健康咨询与方案制定。服务覆盖从健康信息收集、深度一对一专家咨询、个性化 检测与干预方案制定，到定期随访与动态调整的全流程，形成完整的健康管理闭环。依托功能医学的整 体观与寻根探源方法论，京东健康高端健康管理频道尤其擅长应 ...

Core Insights - The 2025 Regenerative Medicine Conference will be held in Dubai on October 24-25, 2025, organized by the American Spine Center in collaboration with the Abu Dhabi Biobank and the American Society for Regenerative Medicine [1] Group 1 - The conference will feature experts from the region and globally discussing the latest advancements in cell research, mitochondrial medicine, functional medicine, and applications of stem cell lasers [1] - Topics of discussion will include innovative methods in beauty treatments, hormone regulation, degenerative joint diseases, and brain disease therapies [1]

Core Viewpoint - The article discusses the challenges and biases associated with AI in the medical field, highlighting how socioeconomic factors can influence the quality of care patients receive, leading to disparities in medical treatment and outcomes [2][3][4]. Group 1: AI and Bias in Healthcare - Recent studies indicate that AI models in healthcare may exacerbate existing biases, with high-income patients more likely to receive advanced diagnostic tests like CT scans, while lower-income patients are often directed to basic checks or no checks at all [2][3]. - The research evaluated nine natural language models across 1,000 emergency cases, revealing that patients labeled with socioeconomic indicators, such as "no housing," were more frequently directed to emergency care or invasive interventions [3]. - AI's ability to predict patient demographics based solely on X-rays raises concerns about the potential for biased treatment recommendations, which could widen health disparities among different populations [3][4]. Group 2: Data Quality and Its Implications - The quality of medical data is critical, with issues such as poor representation of low-income groups and biases in data labeling contributing to the challenges faced by AI in healthcare [8][9]. - Studies have shown that biases in AI can lead to significant drops in diagnostic accuracy, with one study indicating an 11.3% decrease when biased AI models were used by clinicians [6][8]. - The presence of unconscious biases in medical practice, such as the perception of women's pain as exaggerated, further complicates the issue of equitable healthcare delivery [9][10]. Group 3: Overdiagnosis and Its Trends - Research from Fudan University indicates that the overdiagnosis rate for female lung cancer patients in China has more than doubled from 22% (2011-2015) to 50% (2016-2020), with nearly 90% of lung adenocarcinoma patients being overdiagnosed [11]. - The article suggests that simply providing unbiased data may not eliminate biases in AI, as the complexity of medical biases requires a more nuanced approach [11][12]. Group 4: The Need for Medical Advancement - The article emphasizes that addressing overdiagnosis and bias in healthcare is linked to the advancement of medical knowledge and practices, advocating for a shift towards precision medicine [19][20]. - It highlights the importance of continuous medical innovation and the need for sufficient data to clarify the boundaries between overdiagnosis and precision medicine [19][20]. - The integration of AI in healthcare should focus on a holistic approach, considering the interconnectedness of various medical fields to improve patient outcomes [21][22].