Baker Adam, Perov Yura, Middleton Katherine, Baxter Janie, Mullarkey Daniel, Sangar Davinder, Butt Mobasher, DoRosario Arnold, Johri Saurabh
Babylon Health, London, United Kingdom.
Northeast Medical Group, Yale New Haven Health, New Haven, CT, United States.
Front Artif Intell. 2020 Nov 30;3:543405. doi: 10.3389/frai.2020.543405. eCollection 2020.
AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients to self-assess their symptoms and to seek further care when appropriate. For these systems to make a meaningful contribution to healthcare globally, they must be trusted by patients and healthcare professionals alike, and service the needs of patients in diverse regions and segments of the population. We developed an AI virtual assistant which provides patients with triage and diagnostic information. Crucially, the system is based on a generative model, which allows for relatively straightforward re-parameterization to reflect local disease and risk factor burden in diverse regions and population segments. This is an appealing property, particularly when considering the potential of AI systems to improve the provision of healthcare on a global scale in many regions and for both developing and developed countries. We performed a prospective validation study of the accuracy and safety of the AI system and human doctors. Importantly, we assessed the accuracy and safety of both the AI and human doctors independently against identical clinical cases and, unlike previous studies, also accounted for the information gathering process of both agents. Overall, we found that the AI system is able to provide patients with triage and diagnostic information with a level of clinical accuracy and safety comparable to that of human doctors. Through this approach and study, we hope to start building trust in AI-powered systems by directly comparing their performance to human doctors, who do not always agree with each other on the cause of patients' symptoms or the most appropriate triage recommendation.
人工智能虚拟助手有巨大潜力减轻负担过重的医疗系统的压力，通过让患者能够自我评估症状并在适当的时候寻求进一步治疗。为使这些系统能对全球医疗保健做出有意义的贡献，它们必须得到患者和医疗专业人员的信任，并满足不同地区和人群的患者需求。我们开发了一种人工智能虚拟助手，它能为患者提供分诊和诊断信息。关键的是，该系统基于生成模型，这使得相对直接的重新参数化成为可能，以反映不同地区和人群中的当地疾病和风险因素负担。这是一个有吸引力的特性，尤其是考虑到人工智能系统在全球许多地区，无论是发展中国家还是发达国家，改善医疗保健服务的潜力时。我们对该人工智能系统和人类医生的准确性和安全性进行了前瞻性验证研究。重要的是，我们针对相同的临床病例独立评估了人工智能和人类医生的准确性和安全性，并且与以往研究不同的是，我们还考虑了两者的信息收集过程。总体而言，我们发现该人工智能系统能够为患者提供分诊和诊断信息，其临床准确性和安全性与人类医生相当。通过这种方法和研究，我们希望通过将人工智能驱动系统的性能与人类医生直接比较来开始建立对它们的信任，因为人类医生在患者症状的病因或最合适的分诊建议上并不总是意见一致。
