Introduction

介绍

Artificial intelligence refers to simulating the behavior of humans, so that machines can be programmed to perform intelligent behavior and mimic human actions. It is a branch of computer science dealing with building smart machines which can perform actions, typically needing human intelligence. With the availability of huge data, faster computation power, and technology advancement in machine learning and deep learning is providing a paradigm shift in across all the sectors. Artificial Intelligence (AI) in healthcare leverages complex algorithms to emulate human behavior in the data exploration, analysis and training the models, and comprehension of complicated medical and healthcare data. In this article, we will review the key applications of artificial intelligence in the healthcare sector.

人工智能是指模拟人类的行为，以便可以对机器进行编程以执行智能行为并模仿人类的行为。 它是计算机科学的一个分支，涉及构建智能机器，这些机器可以执行通常需要人类智能的动作。 随着海量数据的可用性，更快的计算能力以及机器学习和深度学习中的技术进步，所有领域都发生了范式转变。 医疗保健中的人工智能(AI)利用复杂的算法在数据探索，分析和训练模型以及理解复杂的医疗和保健数据中模拟人类行为。 在本文中，我们将回顾人工智能在医疗保健领域的关键应用。

Abstract

抽象

Artificial intelligence (AI) has made significant progress in the recent years and is poised to transform the healthcare sector. Deep learning algorithms can deal with increasing amounts of data provided by wearables, smartphones, and other mobile monitoring sensors in different areas of medicine (Briganti & Le Moine, 2020). This article provides a perspective on how medical field can leverage AI in the future. It includes predictive modeling, and concepts such as feature selection, common algorithms used in the supervised learning and the selected application in the medical field. Also, it includes how deep learning, unsupervised learning techniques can be used to improvise patient outcomes.

近年来，人工智能(AI)取得了重大进展，并有望改变医疗保健行业。 深度学习算法可以处理可穿戴设备，智能手机以及其他医学领域的其他移动监控传感器提供的越来越多的数据(Briganti和Le Moine，2020年)。 本文提供了有关医疗领域未来如何利用AI的观点。 它包括预测建模和概念，例如特征选择，监督学习中使用的常见算法以及医学领域中的选定应用程序。 此外，它还包括如何使用深度学习，无监督学习技术来改善患者的预后。

Significance of AI in healthcare

人工智能在医疗保健中的意义

Correct diagnosis of the diseases by a human needs years of medical study, and still the manual diagnosis is an arduous and very time consuming process. Hence, the demand for experts is ever rising, which puts huge strain on the healthcare professionals and can also lead to delay in the diagnosis of life saving patients. Deep Learning, Machine Learning algorithms have made huge advancement which can make the diagnosis much faster, cheaper and more accessible. Machine learning algorithms can learn from vast available data and accurately classify the patterns in fraction of seconds. Some of the common applications –

由人类正确诊断疾病需要多年的医学研究，而手动诊断仍然是一个艰巨且非常耗时的过程。 因此，对专家的需求不断增长，这给医护人员带来了巨大压力，并且还可能导致挽救生命的患者的诊断。 深度学习，机器学习算法取得了长足的进步，可以使诊断更快，更便宜且更容易获得。 机器学习算法可以从大量可用数据中学习，并能在几秒钟内准确地对模式进行分类。 一些常见的应用–

· Lung cancer detection from the CT scans

·通过CT扫描检测肺癌

· Diabetic retinopathy indicators from the eye images

·眼睛图像中的糖尿病性视网膜病变指标

· Skin Lesions classification from the skin images

·皮肤图像中的皮肤病变分类

· Analyzing the risk of cardiac arrest from cardiac MRI images

·从心脏MRI图像分析心脏骤停的风险

AI is relevant to many healthcare areas including visually-orientated specialties such as radiology, pathology, ophthalmology, and dermatology due to the availability of large digital datasets. Deep learning algorithms leverage these datasets to train themselves and perform a specific tasks e.g. identifying a lesion in an image (Kulkarni et al., 2020). Precision medicine has the potential to improve the traditional symptom-driven practice of medicine by intelligently integrating multi-omics profiles with clinical, imaging, epidemiological and demographic details to allow a wide range of earlier interventions for advanced diagnostics and tailoring better and economical personalized treatment. Below figure depicts the role of artificial intelligence in traditional healthcare data analytics, and in precision medicine(Ahmed et al., 2020).

由于大量数字数据集的可用性，人工智能与许多医疗保健领域相关，包括以视觉为导向的专业，例如放射学，病理学，眼科和皮肤病学。 深度学习算法利用这些数据集进行自我训练并执行特定任务，例如识别图像中的病变(Kulkarni等人，2020年)。 精密医学可以通过将多组学概况与临床，影像学，流行病学和人口统计学信息进行智能集成，从而改善传统的症状驱动医学实践，从而为早期诊断和更广泛，更经济的个性化治疗提供广泛的早期干预措施。 下图描绘了人工智能在传统医疗数据分析和精密医学中的作用(Ahmed等人，2020)。

Machine learning algorithms can be broadly categorized under supervised, unsupervised and reinforcement learning. While supervised learning focus on classification / regression based on intelligence from historical data, however unsupervised learning focus on identifying hidden patterns and relationships from unlabeled data. Reinforcement learning is based on learning the behavior through trial and error from input data, while trying to optimize the outcome.

机器学习算法可以大致分为监督学习，无监督学习和强化学习。 监督学习的重点是基于历史数据的智能进行分类/回归，而无监督的学习重点在于从未标记的数据中识别隐藏的模式和关系。 强化学习的基础是通过尝试从输入数据中反复尝试来学习行为，同时尝试优化结果。

Above figure depicts the typical components of machine learning cycle. It starts with data preparation and cleaning and applying transformations, normalizations or encoding, which is extremely critical for the performance of machine learning models. The next step involves selecting the right set of features to avoid overfitting or underfitting of the machine learning models. It can also include feature engineering, which leverage domain knowledge to create new features for improvising the machine learning models. The subsequent stages involves building machine learning models, training, optimizing, validating and selecting machine learning models to solve a problem (Waring et al., 2020).

上图描绘了机器学习周期的典型组成部分。 它从数据准备和清理以及应用转换，规范化或编码开始，这对于机器学习模型的性能至关重要。 下一步涉及选择正确的功能集，以避免过度拟合或不足拟合机器学习模型。 它还可以包括特征工程，该特征工程利用领域知识来创建用于改进机器学习模型的新特征。 随后的阶段包括建立机器学习模型，训练，优化，验证和选择机器学习模型以解决问题(Waring等人，2020年)。

The central promise of machine learning is to incorporate data from a variety of sources (clinical measurements and observations, biological –omics, experimental results, environmental information, wearable devices) into sensible models for describing and predicting human disease. The typical machine learning workflow begins with data acquisition, proceeds to feature engineering and then to algorithm selection and model development, and finally results in model evaluation and application. Below figure provides the overview of a typical machine learning workflow in the healthcare industry (Johnson et al., 2018) -

机器学习的中心承诺是将来自各种来源(临床测量和观察，生物组学，实验结果，环境信息，可穿戴设备)的数据整合到用于描述和预测人类疾病的明智模型中。 典型的机器学习工作流程从数据采集开始，进行特征工程，然后进行算法选择和模型开发，最后导致模型评估和应用。 下图概述了医疗保健行业中典型的机器学习工作流程(Johnson等，2018)-

Recent Applications of Artificial Intelligence in Healthcare

人工智能在医疗领域的最新应用

With the emergence of massive compute power and data generated in the healthcare systems, it has provided good emergence of new AI applications, which also include faster development and trails of Covid-19 vaccine. Below are two recent applications, which are accurate and clinically relevant to benefit both the patients and the doctors by making diagnosis more straightforward.

随着医疗系统中大量计算能力和生成数据的出现，它提供了新的AI应用程序的良好出现，其中还包括更快的开发和Covid-19疫苗的研发。 以下是两个最近的应用，它们通过使诊断更加简单而准确且在临床上对患者和医生都有益。

The first of these algorithms is one of the multiple existing examples of an algorithm called DLAD (Deep Learning based Automatic Detection) to analyze chest radiographs and detect abnormal cell growth, such as potential cancers. The algorithm’s performance was compared to multiple physician’s detection abilities on the same images and outperformed 17 of 18 doctors. The second of these algorithms, LYNA (Lymph Node Assistant), to identify metastatic breast cancer tumors from lymph node biopsies. This isn’t the first application of AI to attempt histology analysis, but interestingly this algorithm could identify suspicious regions undistinguishable to the human eye in the biopsy samples given. LYNA was tested on two datasets and was shown to accurately classify a sample as cancerous or noncancerous correctly 99% of the time.

这些算法中的第一个是称为DLAD (基于深度学习的自动检测)算法的多个现有示例之一，该算法可分析胸部X射线照片并检测异常细胞生长，例如潜在的癌症。 将算法的性能与同一图像上多个医师的检测能力进行了比较，其性能优于18位医生中的17位。 这些算法中的第二种算法是LYNA (淋巴结辅助)，可从淋巴结活检中识别出转移性乳腺癌肿瘤。 这不是AI尝试进行组织学分析的第一个应用程序，但有趣的是，该算法可以在给定的活检样本中识别人眼无法区分的可疑区域。 LYNA已在两个数据集上进行了测试，结果显示99％的时间正确地将样品正确分类为癌性或非癌性。

The left panel shows the image fed into an algorithm. The right panel shows a region of potentially dangerous cells, as identified by an algorithm, that a physician should look at more closely. Both LYNA and DLAD serve as prime examples of algorithms that complement physicians’ classifications of healthy and diseased samples by showing doctors salient features of images(Greenfield, n.d.).

左面板显示了输入算法的图像。 右面板显示了由算法识别的潜在危险细胞区域，医生应仔细观察。 LYNA和DLAD都是算法的主要示例，通过显示医生的图像显着特征来补充医生对健康和患病样品的分类(Greenfield，nd)。

Conclusion

结论

The advancements of new techniques in artificial intelligence in clinical practice are significantly helping the patients and the healthcare professionals in accurately and faster diagnosis of the diseases, developing drugs and providing personalized treatments. It is a promising area for development which is rapidly evolving along with other modern areas genomics, precision medicines and teleconsultation. While the scientific research can help in faster development of new solutions to help the healthcare, more rigorous policies should be in place to ensure ethical usage from the evolution of the medicines. It is also significant for the physicians to be aware of the recent advancements in AI, which is going to transform the healthcare in the future.

人工智能在临床实践中的新技术的进步极大地帮助了患者和医护人员准确，快速地诊断疾病，开发药物并提供个性化治疗。 这是一个充满希望的发展领域，它与其他现代领域的基因组学，精密医学和远程咨询一起Swift发展。 虽然科学研究可以帮助更快地开发新的解决方案来帮助医疗保健，但应该制定更严格的政策以确保从药物开发中的伦理使用。 对于医生来说，了解AI的最新进展也很重要，这将改变未来的医疗保健。

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