有“想象力”的AI可以帮助医生进行诊断

医生在全球范围内过度劳累，供不应求，但他们很快就会得到帮助机器学习在初级保健减少错误。AI症状检查ers are tremendously valuable in providing medical information and safe triaging advice to users. However, none of them performs diagnoses like a doctor. Unlike doctors, existing symptom checkers provide advice based on correlations alone—and correlation is not causation. Researchers at Babylon have, for the first time that we know of, used the principles of causal reasoning to enable AI to diagnose written test cases.

The researchers used a new approach, known as causal machine learning—which is gaining increased traction in theAI.community- 作为一个“想象力”，所以AI可以考虑它可能看出哪些症状，如果患者患有不同于考虑的患者。同行评审的研究表明，解散来自因果关系的相关性使AI更加准确。

乔纳森博士，巴比伦科学家和领导作者说：“我们采取了强大的算法AI，并让它能够想象替代现实，如果它是一种不同的疾病，那么如果存在症状。这允许AI在这些书面测试用例上挑逗患者疾病的潜在原因，比超过70％的医生得分。“

Ali Parsa博士，巴比伦的首席执行官和创始人博士说，“世界几乎没有进入医疗保健。我们需要做得更好。因此，在测试案件中看到这些有希望的结果是令人兴奋的。这不应该被称为更换机器医生，因为这里真正鼓励的是我们最终获得允许我们提高现有医疗系统的覆盖率和生产力的工具。AI将是帮助我们所有结束在医疗保健不均衡的不公正的重要工具，并使其对地球上每个人更容易获得和负担得起。“

超过20个巴比伦GPS的游泳池创造了1,671个现实的书面医疗案例 - 这些包括典型和非典型症状的例子超过350个疾病。每种案件由一名医生撰写，然后由多个其他医生验证，以确保它代表了一个现实的诊断案例。然后，每个单独的44babylonGPS组在至少50个书面情况下（平均值为159）以评估。医生列出了他们认为最有可能的疾病（平均返回每种诊断的2.58个潜在疾病）。它们被诊断中包括真实疾病的案例比例来测量它们的准确性。巴比伦的AI采用相同的测试，并使用基于专门为本研究创建的相关性的旧算法，以及更新的因果关系。对于每个测试，AI只能在医生拥有时报告尽可能多的答案。

The doctors had a mean score of 71.40% (± 3.01%) and ranged from 50-90%. The older correlative algorithm performed on par with the average doctor, achieving 72.52% (± 2.97%). The new causal algorithm scored 77.26% (± 2.79%) which was higher than 32 of the doctors, equal to 1, and lower than 11.

Dr. Tejal Patel, associate medical director and GP, Babylon, said, "I'm excited that one day soon this AI could help support me and other doctors reduce misdiagnosis, free up our time and help us focus on the patients who need care the most. I look forward to when this type of tool is standard, helping us enhance what we do."

Dr. Saurabh Johri, chief scientist and author, Babylon, added, "Interestingly, we found that the AI and doctors complemented each other—the AI scored more highly than the doctors on the harder cases, and vice versa. Also, the algorithm performed particularly well for rare diseases which are more commonly misdiagnosed, and more often serious. Switching from using correlations improved accuracy for around 30% of both rare and very-rare conditions."

没有必要改变AI使用的疾病的潜在模型，以便提高准确性。它是一个适用于现有相关算法的好处，包括医疗环境之外的相关算法。

Ciaran Lee博士，学习作者以前是巴比伦和UCL的名誉讲师，说：“因果机学习使我们能够要求更丰富，对医学的更自然的问题。这种方法具有巨大的潜力来改善每种其他目前的症状检查器也可以应用于医疗保健和超越的许多其他问题 - 这就是原因ai是如此令人印象深刻的原因，这是普遍的。“

这项技术为未来的临床医生和AI之间的伙伴关系铺平了道路，这将加速医生的诊断，提高准确性，为临床医生提供释放时间，并改善患者结果和患者的经历。它有可能增强临床医生的工作，并继续为患者提供更好的医疗保健系统。

This new causal algorithm is not yet present in Babylon's publicly available app. It will only be released after further development and testing, and once it has met all necessary regulatory approvals in the UK and other markets where it will be released.

The study was published inNature Communications.