The idea that a single selfie could predict how long someone with cancer may live sounds like an incredible breakthrough(突破). AI is getting better at spotting patterns(识别模式) in medical data, but can it be used to reliably predict what will happen to an individual patient? I asked a clinical colleague what she thought about the recent headlines. She found it an interesting development given that doctors have long used facial appearance to assess patient health.(她认为这是一个有趣的进展，因为医生长期以来一直通过面部外观来评估患者健康。) For example, she noted that they may write “appears stated age” or “appears older than stated age” as a proxy for(作为……的替代指标) general health.
仅凭一张自拍就能预测癌症患者还能活多久，这听起来像是一项了不起的突破。人工智能在识别医疗数据模式方面越来越擅长，但它能否可靠地预测单个患者未来的情况？我问了一位临床同事对最近新闻的看法。她认为这是一个有趣的进展，因为医生长期以来一直通过面部外观来评估患者健康。例如，她指出，医生可能会写下“看起来与所述年龄相符”或“看起来比所述年龄老”，作为整体健康的替代指标。
Now, researchers from Mass General Brigham, a healthcare institution in Massachusetts, have taken this idea a step further(把这一想法推进了一步). They have developed an AI model trained on more than 40 million facial images – called FAHR-Face – to assess biological age(生物学年龄) and predict survival in cancer patients. For biological age, they developed FAHR-FaceAge, which estimates age from certain facial features such as the forehead, the wrinkles between the nose and mouth and the temples(太阳穴). For cancer survival, they developed FAHR-FaceSurvival, trained using almost 35,000 photos of cancer patients. This model focused more closely on the area below the eyes and the bridge of the nose(鼻梁) – regions of the face “linked with dying”. Dr Raymond Mak, one of the study’s authors, noted that these parts of the face are the same as those referenced by Hippocrates in 400BC as signs of muscle wasting and mortality risk: “the nose sharp, the eyes sunken, the temples fallen in”.(研究作者之一雷蒙德·马克博士指出，这些面部部位与公元前400年希波克拉底提到的肌肉萎缩和死亡风险迹象相同：“鼻子尖削、双眼凹陷、太阳穴塌陷”。)
如今，马萨诸塞州医疗机构麻省总医院布莱根的研究人员把这一想法又推进了一步。他们开发了一个基于超过4000万张面部图像训练的人工智能模型——名为FAHR-Face——用于评估生物学年龄和预测癌症患者的生存期。在生物学年龄方面，他们开发了FAHR-FaceAge，通过额头、鼻唇沟皱纹和太阳穴等面部特征来估算年龄。在癌症生存期方面，他们开发了FAHR-FaceSurvival，使用近3.5万张癌症患者照片进行训练。该模型更关注眼睛下方和鼻梁区域——这些面部区域“与死亡相关”。研究作者之一雷蒙德·马克博士指出，这些面部部位与公元前400年希波克拉底提到的肌肉萎缩和死亡风险迹象相同：“鼻子尖削、双眼凹陷、太阳穴塌陷”。
The results, published in The Lancet Digital Health(《柳叶刀数字健康》，权威医学期刊) this month, are intriguing(引人入胜的；有趣的) – but far from conclusive(远未定论). Researchers found that AI-estimated facial age was significantly associated with(与……显著相关) survival in 18 of 28 cancer categories(类别). So, basically, it works for some cancers, but not for others. In addition, Prof Miroslaw Bober noted, “The tool still needs testing against doctors’ own judgments and across different hospitals and patient groups. For example, the facial-age measure was not significantly associated with survival for Black patients after adjustment for other factors.” That finding adds to the growing concern that AI models can exhibit racial bias based on the data used to train them and underlying assumptions.(这一发现加剧了人们日益增长的担忧：AI模型可能基于训练数据和潜在假设表现出种族偏见。) For example, racial bias(种族偏见) has been shown when AI models analyse cardiac magnetic resonance (CMR) scans(心脏磁共振扫描) – used to diagnose and monitor heart disease.
这些结果本月发表在《柳叶刀数字健康》上，引人入胜——但远未定论。研究人员发现，在28类癌症中，有18类癌症的AI估算面部年龄与生存期显著相关。所以，基本上，它对某些癌症有效，对另一些则无效。此外，米罗斯瓦夫·博伯教授指出：“该工具仍需与医生自己的判断进行对比测试，并在不同医院和患者群体中验证。例如，在调整其他因素后，面部年龄指标与黑人患者的生存期没有显著关联。”这一发现加剧了人们日益增长的担忧：AI模型可能基于训练数据和潜在假设表现出种族偏见。例如，AI模型在分析用于诊断和监测心脏病的心脏磁共振（CMR）扫描时，已被证明存在种族偏见。