这篇文章讨论了人工智能在医疗保健领域的应用，特别是在癌症早期检测方面。研究人员使用大数据集和机器学习算法来预测肺癌复发的风险，并且他们发现这种算法可以比当前的方法更有效地诊断癌症生长。文章中提到了一些技术术语，如大数据集、机器学习、放射组学和预测生物标志物。这些术语都是指在人工智能和医疗保健领域中使用的技术和方法。 Can AI save lives? Cancer detection study suggests yes 人工智能能拯救生命吗？癌症检测研究表明是的 New research shows algorithm efficient at detecting recurrence in high-risk patients 新研究表明，算法在检测高危患者复发方面是有效的 Much of the world may currently be fretting about how to limit the impact (lack of privacy, copyright issues, loss of jobs, world domination, etc.) of artificial intelligence. However, that does not mean that there isn’t enormous potential for AI to improve quality of life on earth. 目前，世界上大部分地区都在为如何限制人工智能的影响（缺乏隐私、版权问题、失业、统治世界等）而烦恼。然而，这并不意味着人工智能没有巨大的潜力来改善地球上的生活质量。 One such application is healthcare. With the ability to process big data sets, the deployment of AI could lead to significant advances in predictive diagnostics, including early detection of cancer. While more research is needed, one of the latest studies in the field shows promising results for AI-assisted diagnosis of lung cancer. 其中一个应用是医疗保健。凭借处理大数据集的能力，人工智能的部署可能会导致预测诊断的重大进步，包括癌症的早期检测。虽然还需要更多的研究，但该领域的最新研究之一显示了人工智能辅助诊断肺癌的有希望的结果。 Doctors and researchers at the Royal Marsden NHS foundation trust, the Institute of Cancer Research, and Imperial College London have built an AI algorithm they say can diagnose cancerous growths more efficiently than current methods. 皇家马斯登NHS基金会信托基金，癌症研究所和伦敦帝国理工学院的医生和研究人员已经建立了一个人工智能算法，他们说可以比目前的方法更有效地诊断癌症生长。 In the study named OCTAPUS-AI, researchers used imaging and clinical data from over 900 patients from the UK and Netherlands following curative radiotherapy to develop and test ML algorithms to see how accurately the models could predict recurrence. 在这项名为OCTAPUS-AI的研究中，研究人员使用来自英国和荷兰的900多名患者的成像和临床数据来开发和测试ML算法，以了解模型预测复发的准确性。 Specifically, the study looked at if AI could help identify the risk of cancer returning in non-small cell lung cancer (NSCLC) patients. Researchers used CT scans to develop an AI algorithm using radiomics. This is a quantitative approach which extracts novel data and predictive biomarkers from medical imaging. 具体来说，该研究着眼于人工智能是否可以帮助识别非小细胞肺癌（NSCLC）患者癌症复发的风险。研究人员使用CT扫描开发使用放射组学的AI算法。这是一种定量方法，可从医学成像中提取新数据和预测生物标志物。 Research algorithm superior to current technology 优于当前技术的研究算法 NSCLC patients make up 85% of lung cancer cases. While the disease is often treatable when caught early, in over a third of patients, the cancer returns. The study found that using the algorithm, clinicians may eventually be able to identify recurrence earlier in high-risk patients. NSCLC患者占肺癌病例的85%。虽然这种疾病在早期发现时通常是可以治疗的，但在超过三分之一的患者中，癌症会复发。研究发现，使用该算法，临床医生最终可能能够更早地识别高风险患者的复发。 The scientists used a measure called area under the curve (AUC) to see how efficient the model was at detecting cancer. A perfect 100% accuracy score would be a 1, whereas a model that was purely guessing 50-50 would get 0.5. In the study, the AI algorithm built by the researchers scored 0.87. This can be compared to the 0.67 score of the technology currently in use. 科学家们使用了一种称为曲线下面积（AUC）的测量方法来观察模型在检测癌症方面的效率。完美的 100% 准确率分数将是 1，而纯粹猜测 50-50 的模型将得到 0.5。在这项研究中，研究人员构建的AI算法得分为0.87。这可以与目前使用的技术的0.67分进行比较。 “Next, we want to explore more advanced machine learning techniques, such as deep learning, to see if we can get even better results,” Dr Sumeet Hindocha, Clinical Oncology Specialist Registrar at The Royal Marsden NHS Foundation Trust, and Clinical Research Fellow at Imperial College London, said. “We then want to test this model on newly diagnosed NSCLC patients and follow them to see if the model can accurately predict their risk of recurrence.” “接下来，我们希望探索更先进的机器学习技术，如深度学习，看看我们是否可以获得更好的结果，”皇家马斯登NHS基金会临床肿瘤学专家注册员Sumeet Hindocha博士和伦敦帝国理工学院临床研究员说。“然后，我们想在新诊断的NSCLC患者身上测试这个模型，并跟踪他们，看看该模型是否可以准确预测他们的复发风险。 Support for practitioners – and patients 为从业者和患者提供支持 Rather than believing it will replace doctors, most now view AI in healthtech as a tool that will assist practitioners in providing the best possible care – including improved bedside manners. Despite investors growing gradually more risk-averse over the past year, the healthcare AI sector is still expected to grow from close to $14 billion in 2023 to $103 billion by 2028. 大多数人现在不相信它会取代医生，而是将健康科技中的人工智能视为一种工具，可以帮助从业者提供最好的护理——包括改善床边礼仪。尽管投资者在过去一年中逐渐变得更加厌恶风险，但医疗保健人工智能行业仍有望从2023年的近140亿美元增长到2028年的1030亿美元。 The UK is teeming with AI healthtech startups. Many are focused on drug development, genomic analysis or more consumer-centric telehealth symptom checking and wearables. However, some are intent on improving disease detection and diagnosis. These include the likes of Mendelian, who just received close to £1.5 million to roll out its AI-based solution for rare disease diagnosis as part of the government’s investment into AI technology within the NHS. 英国充斥着人工智能健康科技初创公司。许多人专注于药物开发、基因组分析或更多以消费者为中心的远程医疗症状检查和可穿戴设备。然而，有些人打算改善疾病的检测和诊断。其中包括Mendelian等人，他刚刚收到近150万英镑，用于推出其基于人工智能的罕见疾病诊断解决方案，这是政府在NHS内对人工智能技术投资的一部分。 The rest of Europe also has its fair share of diagnostic AI startups. Among them are Liége-based Radiomics. The company focuses on the detection and phenotypic quantification of solid tumours based on standard-of-care imaging. In Norway, DoMore diagnostics is using AI and deep learning to increase the prognostic and predictive value of cancer tissue biopsies. The company’s founders also say it could help guide the selection of therapy to avoid over- and undertreatment. 欧洲其他地区也有相当多的诊断人工智能初创公司。其中包括基于列日的放射组学。该公司专注于基于标准护理成像的实体瘤检测和表型定量。在挪威，DoMore Diagnostics正在使用人工智能和深度学习来提高癌症组织活检的预后和预测价值。该公司的创始人还表示，它可以帮助指导治疗的选择，以避免过度治疗和治疗不足。 Meanwhile, a few percentage points of more accurate diagnosis, vital though they may be for the affected individual, may not be the only positive impact AI could have on our care systems. 与此同时，几个百分点的更准确的诊断，尽管它们可能对受影响的个体至关重要，但可能不是人工智能可能对我们的护理系统产生的唯一积极影响。 According to Eric Topol, the author of Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again, “the greatest opportunity offered by AI is not reducing errors or workloads, or even curing cancer: it is the opportunity to restore the precious and time-honoured connection and trust—the human touch—between patients and doctors.” 根据《深度医学：人工智能如何使医疗保健再次成为人类》一书的作者埃里克·托波尔（Eric Topol）的说法，“人工智能提供的最大机会不是减少错误或工作量，甚至不是治愈癌症：而是恢复患者和医生之间宝贵而历史悠久的联系和信任的机会 - 人情味。