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].

试想一种尖端的医疗技术，可以治好你的疾病，但是医生因为不掌握信息，推荐你用了传统的治疗手 段，恢复效果远不如采用新技术的病友。知道真相后，你会不会感到恼火？ 同样的情况，如果发生在赛博医生身上，原因不再是信息滞后，而是AI根据你的性别或者收入水平作 出了这样的选择呢？ 近期国际上一系列研究表明，越来越聪明的大模型，把医疗领域"看人下菜碟"的问题也放大了。 美国西奈山伊坎医学院和西奈山卫生系统的研究者在其发表在Nature子刊上的研究成果显示，被标记 为"高收入"的人群更可能获得CT和核磁检查的机会，中低收入病例则通常被安排做基本检查或不进行 检查。 而被标注为"无住房"等信息的患者则会更频繁被指向紧急护理、侵入性干预或心理健康评估。 指望"赛博医生"整顿医疗的人们又失望了。 究其原因，数据确实是非常关键的因素。 根据中国中医科学院中医药信息研究所的仝媛媛等人研究中，除了常受诟病的因为信息化水平偏低等原 因造成的医疗数据质量欠佳，还有很多数据问题。 这项研究评估了9个自然语言大模型，涉及1000个急诊病例（500个真实病例和500个合成病例）的170万 个看诊结果。 更早的研究显示，AI仅凭X射线就能预测出患者 ...