As an Electrical Engineering graduate student with research focusing on biomedical technology and innovation; the first thing that I tried to decide was how far the technology has evolved in medicine and what prospect does it present in the near future. Physicians and radiologists are still at the core of high demand but with the recent intervention of AI technology, Machine Learning (ML) and Data Science there is a shift to the automation of data analysis. A part of the reason for this shift is because AI technology improves accuracy and time: both of which are crucial elements when it comes to saving lives.

作为电气工程研究生，专注于生物医学技术和创新的研究； 我要决定的第一件事是，该技术在医学上发展了多远，以及在不久的将来它有什么前景。 内科医生和放射科医生仍然是高需求的核心，但是随着AI技术，机器学习(ML)和数据科学的最新介入，向数据分析自动化的转变已经开始。 这种转变的部分原因是因为AI技术提高了准确性和时间：在挽救生命方面，这两项都是至关重要的要素。

Medicine in itself is a complex and challenging domain. In many business areas, including the financial technology industry, artificial intelligence technology has shown encouraging results in automation and effective data analysis. AI is expected to redesign and revolutionize the healthcare industry. This can include devising effective treatment plans through Machine Learning (ML) and assisting professionals in the analysis of medical data.

医学本身是一个复杂而具有挑战性的领域。 在包括金融技术行业在内的许多商业领域，人工智能技术在自动化和有效数据分析方面均显示出令人鼓舞的结果。 人工智能有望重新设计并彻底改变医疗行业。 这可以包括通过机器学习(ML)制定有效的治疗计划，并协助专业人员分析医学数据。

Integration of AI system in healthcare can increase global revenue by 8.4 times. The healthcare industry can expect to save about $150 billion through innovations brought by AI technology.

将AI系统集成到医疗保健中可以使全球收入增长8.4倍。 通过AI技术带来的创新，医疗行业有望节省约1500亿美元。

The different sectors of the medical technology and industries Artificial Intelligence in Medicine (AIM) research is mostly based on biomedicine, patient data-management and information retrieval process. Sufficient investment and research are also allocated to enhance Augmented Intelligence. This field involves combining the power of scientific data with medical professional judgement.

医疗技术和行业的不同领域医学的人工智能(AIM)研究主要基于生物医学，患者数据管理和信息检索过程。 还分配了足够的投资和研究来增强增强智能。 该领域涉及将科学数据的力量与医学专业判断相结合。

Artificial Intelligence in Medicine (AIM) research is mostly based on biomedicine, patient data-management and information retrieval process. Sufficient investment and research are also allocated to enhance Augmented Intelligence. This field involves combining the power of scientific data with medical professional judgement.

医学上的人工智能(AIM)研究主要基于生物医学，患者数据管理和信息检索过程。 还分配了足够的投资和研究来增强增强智能。 该领域涉及将科学数据的力量与医学专业判断相结合。

Certain Ivy League universities are already invested in this technology.

一些常春藤盟校已经在这项技术上进行了投资。

本大学附属于AIM技术的研究 (Current University affiliated researches on AIM technology)

1AIMI Center at Stanford University is utilizing interdisciplinary expertise on statistics, electrical engineering and bioinformatics for developing new AI methods for analyzing medical imaging.

1 斯坦福大学AIMI中心 正在利用统计，电气工程和生物信息学的跨学科专业知识来开发用于分析医学影像的新AI方法。

2HMS (Harvard Medical School) is also actively involved at developing data driven models that can improve decision- making in the health care industry.

2 HMS(哈佛医学院)还积极参与开发数据驱动的模型，以改善医疗保健行业的决策能力。

Research is done in developing a diagnostic algorithm that can find judgement features in digital images e.g. retina image to spot diabetic patients.

在开发诊断算法方面进行了研究，该算法可以在数字图像(例如视网膜图像)中找到判断糖尿病患者的特征。

3Researchers at University of Buffalo (UB) are using ML to analyze predictive patterns in high- resolution medical images., genetic information and medical records to improve diagnosis in patients. The researchers are also working towards a tumor board database that can use AI for ‘precision oncology ’ that can improve on cancer patient care.

3 布法罗大学(UB)的研究人员正在使用ML分析高分辨率医学图像，遗传信息和病历中的预测模式，以改善患者的诊断。 研究人员还致力于建立一个肿瘤板数据库，该数据库可将AI用于“精确肿瘤学”，从而改善癌症患者的护理。

4 University of Maryland is also taking initiative in the field of AI and medicine, project: AIM-HI (AI + Medicine for high impact). As of 2020, they are working on: (i)Tacking chronic pain using ML-enabled biomarker discovery and sensing (ii) a multi-stage ML framework for prioritization in mental health and risk assessment (iii) Precision therapy for neonatal opioid withdrawal syndrome (NOWS).

4 马里兰大学也在AI和医学领域中处于主动地位，项目：AIM-HI(具有高影响力的AI +医学)。 截至2020年，他们正在致力于：(i)使用支持ML的生物标记物发现和感知来解决慢性疼痛(ii)优先考虑精神健康和风险评估的多阶段ML框架(iii)新生儿阿片类戒断综合征的精确治疗(现在)。

There are other universities which are not listed above are also conducting similar research in the field of AI and medicine.

上面没有列出的其他大学也在AI和医学领域进行类似的研究。

行业对AIM技术的研究 (Industry affiliated researches on AIM technology)

1IBM Watson Health brings a vast amount of medical data into a cloud hub. The cognitive capabilities and traditional analytics turn the data into knowledge. It can be used to determine the right medication for any patient. This research has great potential because of the variation in patient conditions and the range of medications available. Using AI technology the platform can decide whether a particular medication is actually suitable for a particular patient.However, some of the limitations and criticism include recommending incorrect treatment advice. Hence this platform has potential when complimented with a registered physician consultation.

1 IBM Watson Health将大量医疗数据带入云中心。 认知能力和传统分析将数据转化为知识。 它可用于为任何患者确定正确的药物。 由于患者状况的变化和可用药物的范围，这项研究具有巨大的潜力。 该平台可以使用AI技术确定特定药物是否真正适合特定患者，但是其中一些局限和批评包括建议使用不正确的治疗建议。 因此，当与注册医生会诊配合使用时，该平台具有潜力。

2Google search engines have already been sincere in its effort in helping us on providing information about medical conditions or locations of nearby hospitals. They are extending their study of AI in assisting diagnosis of cancer, preventing blindness and other ways to improve on patient health care.

2 Google搜索引擎已经竭诚帮助我们提供有关医疗状况或附近医院位置的信息。 他们正在扩展对AI的研究，以帮助诊断癌症，预防失明以及其他改善患者保健的方法。

3Project Baseline by Verily is another approach used to collaborate with researchers, clinicians, engineers and volunteers to build on next generation healthcare tools and services. Their website lists certain research projects including a study on COVID-19, skin, mood, sleep studies and many other relevant fields.

3 Verily的Project Baseline是与研究人员，临床医生，工程师和志愿者合作以利用下一代医疗保健工具和服务的另一种方法。 他们的网站列出了某些研究项目，包括对COVID-19，皮肤，情绪，睡眠研究和许多其他相关领域的研究。

There are other industrial projects which are not listed above are also conducting similar research in the field of AI and medicine.

上面没有列出的其他工业项目也在AI和医学领域进行类似的研究。

人工智能如何彻底改变了医疗行业的某些领域？ (How AI has revolutionized certain sectors of the medical industry?)

AI and Deep Learning (DL) has been the primary focus to make the medical sector more efficient in terms of processing of data. The concept of deep learning for image analysis could be easily applied to read X-ray images and draw necessary conclusions. This eliminates the necessity of the scope for human error and offers a faster processing time.

人工智能和深度学习(DL)一直是提高医疗领域数据处理效率的主要重点。 用于图像分析的深度学习概念可以轻松地应用于读取X射线图像并得出必要的结论。 这消除了人为错误的范围的必要性，并提供了更快的处理时间。

AIM的应用很少 (Few applications of AIM)

(i) Automation: The medical field requires two inseparable aspects, namely data intensive and knowledge-based. Routine consultation and other background analysis can be easily automated through an AI system.

(i) 自动化： 医学领域需要两个不可分割的方面，即数据密集和基于知识。 例行咨询和其他背景分析可通过AI系统轻松实现自动化。

(ii) Virtual diagnosis: Virtual AI-assisted medical screening platform is gaining popularity due to its lower cost and convenience. This also allows scope for an early diagnosis thereby preventing later healthcare complications. Phone screening or virtual interviews can check relevant data for diagnosis and can recommend appropriate physicians for further consultation (if required).

(ii) 虚拟诊断 ：虚拟AI辅助的医学筛查平台因其成本低廉和便捷性而越来越受欢迎。 这也为早期诊断提供了空间，从而防止了以后的医疗并发症。 电话筛查或虚拟访谈可以检查相关数据以进行诊断，并可以推荐合适的医生进行进一步咨询(如果需要)。

The platform can allow online diagnosis by asking a series of relevant questions about your symptoms. AI code can easily diagnose the illness. The most common online diagnosis includes sinusitis, bronchitis, asthma, nail infection and urinary tract infection.

该平台可以通过询问有关症状的一系列相关问题来进行在线诊断。 AI代码可以轻松诊断出疾病。 最常见的在线诊断包括鼻窦炎，支气管炎，哮喘，指甲感染和尿路感染。

(iii) Deep learning (DL)software for image analysis: This field is gaining momentum particularly because it provides scope to integrate the capabilities of a medical professional into an IT platform. Radiology involves the field of diagnosis and treatment of injuries using medical imaging. Deep learning in image analysis can improve the efficiency of diagnosis and actively aid in drawing conclusions on patient treatment from image analysis.

(iii) 用于图像分析的深度学习(DL)软件：此领域正在蓬勃发展，特别是因为它提供了将医疗专业人员的能力集成到IT平台的范围。 放射学涉及使用医学成像诊断和治疗伤害的领域。 图像分析中的深度学习可以提高诊断效率，并积极帮助从图像分析中得出有关患者治疗的结论。

This field has been actively explored in cancer treatment. It can automate the image analysis procedure for tumor diagnosis and give a new error free model for early diagnosis and treatment. Error free analysis can also allow the scope to drive down the healthcare cost. There are several reasons why this field is gaining momentum: (i) lack of professionals in the particular field (ii) complications related to testing and analysis (iii) allows scope for collaboration between physician and pathologist (iv) reduces the cost by improving accuracy in diagnosis.

该领域已在癌症治疗中得到积极探索。 它可以使图像分析程序自动化以进行肿瘤诊断，并为早期诊断和治疗提供新的无错误模型。 无差错分析还可以使示波器降低医疗保健成本。 该领域蓬勃发展的原因有几个：(i)缺少特定领域的专业人员(ii)与测试和分析有关的并发症(iii)允许医师和病理学家之间进行合作的范围(iv)通过提高准确性来降低成本在诊断中。

(iv) Robot-assisted surgery: Robot assisted surgeries involve advances in surgical technologies to improve the efficiency of the surgical procedure. This process usually involves insertion of a 3D camera and miniature surgical instrument into the patient’s body. The surgeon uses external control system to manipulate the instrument to do the surgical task with precision. Though until now the surgeons are only actively involved in the surgical decision- making process but there is scope for the AI technology to improve and automate some of the systems for improved accuracy. This is particularly important because AI technology can run at sites inaccessible by a 3D camera.

(iv) 机器人辅助手术：机器人辅助手术涉及外科技术的进步，以提高手术程序的效率。 该过程通常涉及将3D相机和微型手术器械插入患者体内。 外科医生使用外部控制系统来操纵仪器，以精确地完成手术任务。 尽管到目前为止，外科医生仅积极参与外科手术决策过程，但AI技术仍有改进和自动化某些系统以提高准确性的空间。 这一点特别重要，因为AI技术可以在3D相机无法访问的站点上运行。

(v)Raw data processing: Most professionals struggle at accessing relevant data and have difficulty putting together to make valuable insight. Also, there is a scope for data alteration and integrity of data being compromised. Hence, there is a rising number of healthcare startups that are actively involved at processing a large amount of medical data. They perform data standardization and harmonization and is used in the ML platform.

(v) 原始数据处理 ：大多数专业人员都在努力获取相关数据，并且很难集中精力获得有价值的见解。 此外，还有数据更改和数据完整性受到损害的范围。 因此，越来越多的医疗保健初创公司积极参与处理大量医疗数据。 它们执行数据标准化和统一化，并在ML平台中使用。

AIM的未来 (The future of AIM)

The primary focus of AIM is to integrate the ability of medical professionals with the data intensive system. This can allow scope for comprehensive understanding of medical information and data. AI can help to find accurately the relevant data which is both time-consuming and cost-effective. Healthcare industry is a data intensive system and therefore AI integration can find ways to improve care and cut overload of information. Some of the limitations and criticism include recommending incorrect diagnosis and treatment advice. The AIM platform has potential when complimented with a registered physician consultation. The holistic role of physician , AIM and medical technology can improve the current healthcare system.

AIM的主要重点是将医疗专业人员的能力与数据密集型系统集成在一起。 这可以为全面了解医学信息和数据提供空间。 人工智能可以帮助您准确地找到既耗时又具有成本效益的相关数据。 医疗保健行业是一个数据密集型系统，因此AI集成可以找到改善护理和减少信息过载的方法。 一些限制和批评包括建议不正确的诊断和治疗建议。 当与注册医生会诊配合使用时，AIM平台具有潜力。 医生，AIM和医疗技术的整体作用可以改善当前的医疗保健系统。