JAMA：人工智能在医疗领域的三个时代
2024/4/1 石榴号

    

    

    

     JAMA》Special Communication

     Three Epochs of Artificial Intelligence in Health CareAi 汉译仅供参考。

    

    

    Three Epochs of Artificial Intelligence in Health Care

     人工智能在医疗领域的三个时代

     Abstract

     Importance Interest in artificial intelligence (AI) has reached an all-time high, and health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.

     人们对人工智能(AI)的兴趣达到了历史最高水平，整个生态系统的医疗领导者面临着以下问题:在何处、何时和如何部署AI，以及如何理解其风险、问题和可能性。

     Observations While AI as a concept has existed since the 1950s, all AI is not the same. Capabilities and risks of various kinds of AI differ markedly, and on examination 3 epochs of AI emerge. AI 1.0 includes symbolic AI, which attempts to encode human knowledge into computational rules, as well as probabilistic models. The era of AI 2.0 began with deep learning, in which models learn from examples labeled with ground truth. This era brought about many advances both in people’s daily lives and in health care. Deep learning models are task-specific, meaning they do one thing at a time, and they primarily focus on classification and prediction. AI 3.0 is the era of foundation models and generative AI. Models in AI 3.0 have fundamentally new (and potentially transformative) capabilities, as well as new kinds of risks, such as hallucinations. These models can do many different kinds of tasks without being retrained on a new dataset. For example, a simple text instruction will change the model’s behavior. Prompts such as “Write this note for a specialist consultant” and “Write this note for the patient’s mother” will produce markedly different content.

     虽然AI作为一个概念自20世纪50年代就存在了，但并非所有的AI都是一样的。各种人工智能的能力和风险存在明显差异，经过考察，人工智能出现了三个时代。AI 1.0包括符号AI，它试图将人类的知识编码到计算规则中，以及概率模型。人工智能2.0时代始于深度学习，在深度学习中，模型从标记有基础真相的例子中学习。这个时代为人们的日常生活和医疗保健带来了许多进步。深度学习模型是特定于任务的，这意味着它们一次只做一件事，它们主要专注于分类和预测。AI 3.0是基础模型和生成AI的时代。AI 3.0中的模型具有根本的新(潜在的变革)能力，以及新类型的风险，如幻觉。这些模型可以完成许多不同类型的任务，而无需对新数据集进行重新训练。例如，一个简单的文本指令将改变模型的行为。提示如“为专科医生写这张纸条”和“为病人的母亲写这张纸条”将产生明显不同的内容。

     Conclusions and Relevance Foundation models and generative AI represent a major revolution in AI’s capabilities, ffering tremendous potential to improve care. Health care leaders are making decisions about AI today. While any heuristic omits details and loses nuance, the framework of AI 1.0, 2.0, and 3.0 may be helpful to decision-makers because each epoch has fundamentally different capabilities and risks.

     结论和相关性基金会模型和生成AI代表了AI能力的一场重大革命，为改善医疗提供了巨大的潜力。今天，医疗领导者正在就人工智能做出决策。尽管任何启发式都省略了细节并失去了细微差别，但AI 1.0、2.0和3.0的框架可能对决策者有帮助，因为每个时代都有根本上不同的能力和风险。

     Introduction

     Interest in artificial intelligence (AI) has reached an all-time high—whether the metric is scholarly publications, press coverage, or consumer interest. Health care leaders across the ecosystem are faced with questions about where, when, and how to deploy AI and how to understand its risks, problems, and possibilities.

     无论是学术出版物、新闻报道还是消费者兴趣，人们对人工智能(AI)的兴趣都达到了空前的高度。整个生态系统的医疗领导者都面临着以下问题:在何处、何时和如何部署AI，以及如何了解其风险、问题和可能性。

     All AI Is Not the Same

     Capabilities and risks of various kinds of AI differ markedly. Just as grouping bacterial and viral infections together when making a treatment plan could lead to the wrong clinical response, grouping different kinds of AI together may lead health care decision-makers down the wrong path. A simple, pragmatic framework of 3 epochs of AI may assist decision-makers in understanding the strengths, weaknesses, and challenges of different kinds of AI in this moment of technological change (Figure).

     所有的人工智能都不一样

     各种人工智能的能力和风险明显不同。就像在制定治疗计划时将细菌和病毒感染放在一起可能会导致错误的临床反应一样，将不同种类的AI放在一起可能会导致卫生保健决策者走上错误的道路。一个简单、实用的三个时代的AI框架可能有助于决策者理解在这一技术变革时刻不同类型AI的优势、劣势和挑战(图)。

    

     AI 1.0: Symbolic AI and Probabilistic Models

     Over its first 50-plus years, most AI focused on encoding human knowledge into rules in machines. One can think of this as many, many if-then rules, or decision trees. This symbolic AI had some remarkable achievements, such as IBM’s Deep Blue, which defeated the chess world champion in 1997. In health care, tools such as INTERNIST-I aimed to represent expert knowledge about diseases to help with challenging cases. Today, many electronically implemented clinical pathways encode expert knowledge in decision trees, a type of symbolic AI.

     AI 1.0:符号AI和概率模型

     在最初的50多年里，大多数人工智能都专注于将人类的知识编码成机器的规则。你可以把它想象成很多很多的“如果-那么”规则或决策树。这一具有象征意义的人工智能取得了一些显著的成就，例如IBM的深蓝在1997年击败了国际象棋世界冠军。在医疗保健中，内科医师- i等工具旨在代表有关疾病的专家知识，以帮助处理有挑战性的病例。今天，许多电子实现的临床路径在决策树中编码专家知识，这是一种符号AI。

     Symbolic AI also had key limitations, notably a constant risk of human logic errors in its construction and bias encoded in its rules, because its knowledge base depended solely on those creating it. But perhaps the most important issue was that, empirically, symbolic AI had fundamental capability limitations and appeared brittle when confronted with real-world situations. In response, research began focusing more on probabilistic modeling, such as traditional regression and then bayesian networks, which allowed both expert knowledge and empirical data to contribute to reasoning systems. These models handled real-world situations more elegantly and found some use in health care, but in practice were difficult to scale and had limited ability to manage images, free text, and other complex clinical data.

     象征性AI也有关键的局限性，特别是在其构建过程中存在人类逻辑错误的风险，以及在其规则中编码的偏见，因为它的知识库完全依赖于创造它的人。但也许最重要的问题是，从经验上看，象征性AI具有基本的能力限制，在面对真实世界的情况时显得脆弱。作为回应，研究开始更多地关注概率模型，如传统回归，然后是贝叶斯网络，这使得专家知识和经验数据都有助于推理系统。这些模型对真实世界情况的处理更优雅，在医疗保健中也有一定用途，但在实践中难以缩放，并且管理图像、自由文本和其他复杂临床数据的能力有限。

     AI 2.0: The Era of Deep Learning

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