Historically, the term “artificial intelligence” dates to 1956 when it was first used in a conference at Dartmouth College in the US. Since then, the development of artificial intelligence has in part been shaped by the field of neuroscience. By understanding the human brain, scientists have attempted to build new intelligent machines capable of performing complex tasks akin to humans. Indeed, future research into artificial intelligence will continue to benefit from the study of the human brain. While the development of artificial intelligence algorithms has been fast paced, the actual use of most artificial intelligence (AI) algorithms in biomedical engineering and clinical practice is still markedly below its conceivably broader potentials. This is partly because for any algorithm to be incorporated into existing workflows it has to stand the test of scientific validation, clinical and personal utility, application context, and is equitable as well. In this context, there is much to be gained by combining AI and human intelligence (HI). Harnessing Big Data, computing power and storage capacities, and addressing societal issues emergent from algorithm applications, demand deploying HI in tandem with AI. Very few countries, even economically developed states, lack adequate and critical governance frames to best understand and steer the AI innovation trajectories in health care. Drug discovery and translational pharmaceutical research stand to gain from AI technology provided they are also informed by HI. In this expert review, we analyze the ways in which AI applications are likely to traverse the continuum of life from birth to death, and encompassing not only humans but also all animal, plant, and other living organisms that are increasingly touched by AI. Examples of AI applications include digital health, diagnosis of diseases in newborns, remote monitoring of health by smart devices, real-time Big Data analytics for prompt diagnosis of heart attacks, and facial analysis software with consequences on civil liberties. While we underscore the need for integration of AI and HI, we note that AI technology does not have to replace medical specialists or scientists and rather, is in need of such expert HI. Altogether, AI and HI offer synergy for responsible innovation and veritable prospects for improving health care from prevention to diagnosis to therapeutics while unintended consequences of automation emergent from AI and algorithms should be borne in mind on scientific cultures, work force, and society at large.

中文翻译：

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集成人工智能与人类智能：生物医学工程和医学领域负责任创新的合作伙伴关系。

从历史上看，“人工智能”一词可追溯到1956年，最初在美国达特茅斯学院的一次会议上使用。从那时起，人工智能的发展部分受到了神经科学领域的影响。通过了解人的大脑，科学家们试图构建能够执行类似于人类的复杂任务的新型智能机器。实际上，未来对人工智能的研究将继续受益于人脑的研究。尽管人工智能算法的发展已迅速发展，但大多数人工智能（AI）算法在生物医学工程和临床实践中的实际使用仍明显低于其可想象的更广泛的潜力。这部分是因为要将任何算法集成到现有工作流程中，都必须经受科学验证，临床和个人实用性，应用环境的考验，并且也必须公平。在这种情况下，将AI和人类智能（HI）结合起来将有很多收获。利用大数据，计算能力和存储容量以及解决算法应用程序中出现的社会问题，需要与AI一起部署HI。很少有国家，甚至是经济发达的国家，也缺乏足够的，关键的治理框架来最好地了解和指导医疗保健领域的AI创新轨迹。只要HI告知他们，药物发现和转化药物研究将从AI技术中受益。在这篇专家评论中，我们分析了AI应用程序可能从出生到死亡遍历生命连续体的方式，不仅涵盖了人类，还涵盖了越来越多地被AI触及的所有动植物。AI应用的示例包括数字健康，新生儿疾病诊断，智能设备对健康的远程监控，实时诊断心脏病发作的实时大数据分析以及对公民自由有影响的面部分析软件。尽管我们强调了AI和HI集成的必要性，但我们注意到AI技术并不一定要替代医学专家或科学家，而是需要此类专家HI。共，