Disruptive Dozen: 12 Emerging AI Technologies Impacting Healthcare

12项颠覆性AI技术出炉,有望影响医疗行业发展

2019-04-11 17:42:00 HIT Consultant

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Today during the 2019 World Innovation Forum, Partners HealthCare unveiled its selections for the fifth annual “Disruptive Dozen,” an annual list of 12 emerging artificial intelligence technologies with the greatest potential to impact healthcare in the next year. Disruptive Dozen Overview & Criteria The annual 12 most disruptive technologies are selected through a rigorous process by Partners HealthCare thought leaders, clinicians, and researchers. Interview results from nearly 100 experts are assembled into a field of nominated technologies. The nominated innovation must have a strong potential for significant clinical impact at some point in the next decade and offers significant patient benefit in comparison to current practices. The innovation may also have a significant benefit to the delivery/efficiency of health. In addition, the nominated AI-related innovations must have a high probability of successful commercial deployment—e.g., payers will be expected to support it The output of the voting is a consensus on the 12 technologies that will have the greatest impact in the next year. The final list is then announced in rank order at the World Medical Innovation Forum. The output of the voting is a consensus on the 12 technologies that will have the greatest impact in the next year. The final list is then announced in rank order at the World Medical Innovation Forum. Without further ado, here is a look at the fifth annual “Disruptive Dozen” for 2019: 1. Reimagining Medical Imaging AI is transforming radiology and imaging, including mammography and ultrasound, to bring improvements in clinical care and diagnoses to patients worldwide. Researchers envision AI transforming mammography from one-size-fits-all to a more targeted tool for assessing breast cancer risk, and further increasing utility for ultrasound for disease detection and rapid acquisition of clinical-grade images.    2. Better Prediction of Suicide Risk Suicide is the 10th leading cause of death in the U.S. and the second leading cause of death among young people. AI is proving powerful in helping identify patients at risk of suicide (based on EHR data,) and also examining social media content with the goal of detecting early warning signs of suicide. These efforts toward an early warning system could help alert physicians, mental health professionals and family members when someone in their care needs help. These technologies are under development and not cleared for clinical use. 3. Streamlining Diagnosis The application of AI in clinical workflows such as imaging and pathology is ushering in a new era of AI-enabled disease diagnosis. From identifying abnormal and potentially life-threatening findings in medical imaging, to screening pathology cases according to the presence of urgent findings such as cancer cells, AI is poised to aid the diagnostic, prognostic, and treatment decisions that clinicians make while caring for patients. 4. Automated Malaria Detection Nearly half a million people succumbed to malaria in 2017, with the majority being children under five. Deep learning technologies are helping automate malaria diagnosis, with software to detect and quantify malaria parasites with 90 percent accuracy and specificity. Such an automated approach to malaria detection and diagnosis could benefit millions of people worldwide by helping to deliver more accurate and timely diagnoses and could enable better monitoring of treatment efficacy. 5. Real-time Monitoring and Analysis of Brain Health – a Window on the Brain A new world of real-time monitoring of the brain promises to dramatically improve patient care. By automating the manual and painstaking analysis of EEGs and other high-frequency wave-forms, clinicians can rapidly detect electrical abnormalities that signal trouble.  Deep learning algorithms based on terabytes of EEG data are helping to automatically detect seizures in the critically ill, regardless of the underlying cause of illness. 6. “A-Eye”: Artificial Intelligence for Eye Health and Disease Not only is AI is helping advance new approaches in ophthalmology, it’s demonstrating the ability of AI-enabled technologies to enhance primary care with specialty level diagnostics. In 2018, the Food and Drug Administration approved a new AI-based system for the detection of diabetic retinopathy, marking the first fully automated, AI-based diagnostic tool approved for market in the U.S. that does not require additional expert review. The technology could also play a role in low-resource settings, where access to ophthalmologic care may be limited. 7. Lighting a“FHIR” Under Health Information Exchange A new data standard, known as the Fast Healthcare Interoperability Resources (FHIR) has become the de facto standard for sharing medical and other health-related information. With its modern, web-based approach to health information exchange, FHIR promises to enable a new world of possibilities rooted in patient-centered care. While this new world is just emerging, it promises to give patients unfettered access to their own health information — allowing them to decide what they want to share and with whom and demanding careful consideration of data privacy and security. 8. Reducing the Burden of Health Care Administration se of AI to automate routine and highly repetitious administrative functions. In the U.S., more than 25% of health care expenditures are due to administrative costs, far surpassing all other developed nations. One important area where AI could have a sizeable impact is medical coding and billing, where AI can develop automated approaches. The goal is to help reduce the complexity of the coding and billing process thereby reducing the number of mistakes and minimize the need for intense regulatory oversight. 9. A Revolution in Acute Stroke Care Stroke is a major cause of death and disability across the world and a significant source of health care spending. Each year in the U.S., nearly 800,000 people suffer from a stroke, with a cost of roughly $34 billion. AI tools to help automate the diagnostic journey of ischemic stroke can help determine whether there is bleeding within the brain — a crucial early insight that helps doctors select the proper treatment. These algorithms can automatically review a patient’s head CT scan to identify a cerebral hemorrhage as well as help localize its source and determine the volume of brain tissue affected. 10. The Hidden Signs of Intimate Partner Violence Researchers are working to develop AI-enabled tools that can help alert clinicians if a patient’s injuries likely stem from intimate partner violence (IPV). Through an AI-enabled system, they hope to help break the silence that surrounds IPV by empowering clinicians with powerful, data-driven tools. While screening for intimate partner violence (IPV) can help detect and prevent future violence, less than 30% of IPV cases seen in the ER are appropriately flagged as abuse-related. Health care providers are optimistic that AI tools will further complement their role as a trusted source for divulging abuse. 11. Voice-first Technology Comes to Health Care AI-powered, voice-first technology is coming to the clinic. Using speech recognition and natural language processing, several companies are developing tools designed to help clinicians deliver better care and provide more of what matters: quality time with patients. Voice assistants are being explored for reducing physicians’ data entry burdens. Health care technology companies are developing applications that can run on consumer-grade voice technology platforms while complying with the Health Insurance Portability and Accountability Act, or HIPAA. 12. Narrowing the Gaps in Mental Health Care In the U.S., mental illness has reached epidemic proportions; nearly one in five adults grapples with a mental disorder, and opioid addiction and misuse claim the lives of more than 130 adults every day. Promising innovation is being seen in the integration of rigorously validated mental health methods into smartphone apps. One AI app under development is for patients with opioid, alcohol, and other forms of drug addiction with co-occurring mental illness. The app provides patients with a virtual form of integrated group therapy (IGT), a highly effective treatment that teaches behaviors and skills to manage recovery and prevent relapse.
今天,在2019年世界创新论坛上,合作伙伴 HealthCare 公布了其第五届年度“破坏性 Dozen ”评选结果,这是一份由12项新兴人工智能技术组成的年度名单,这些技术最有可能在明年影响医疗保健。 中断 Dozen 概述和标准 每年12项最具颠覆性的技术是由合作伙伴 HealthCare 思想领袖、临床医生和研究人员通过严格的流程选择的。近100名专家的访谈结果被汇集到一个被提名的技术领域。 提名的创新必须在未来十年的某个时刻具有重大临床影响的强大潜力,并提供与当前实践相比较的重大患者利益。创新也可能对健康的提供/效率产生重大的好处。此外,指定的与人工智能相关的创新必须具有成功商业部署的高可能性——例如,预期支付者将支持它 表决结果是就将在明年产生最大影响的12项技术达成共识。最后的名单然后在世界医学创新论坛按次序公布。 表决结果是就将在明年产生最大影响的12项技术达成共识。最后的名单然后在世界医学创新论坛按次序公布。 没有更多的讨论,这里是第五个2019年的年度“破坏性 Dozen : 1。重塑医学影像 人工智能正在改变放射学和成像,包括乳房 X 线摄影和超声,以改善临床护理和诊断世界各地的病人。研究人员设想,人工智能将乳房 X 线摄影从一刀切转变为更有针对性的评估乳腺癌风险的工具,并进一步提高超声波在疾病检测和快速获取临床级别图像方面的效用。 2。更好地预测自杀风险 自杀是美国第十大死因,也是年轻人第二主要原因。人工智能在帮助识别有自杀风险的患者(基于 EHR 数据)和检查社交媒体内容以检测自杀的早期预警信号方面发挥着强大的作用。这些针对早期预警系统的努力可以帮助提醒医生、精神健康专业人员和家庭成员,当他们的护理需要帮助时。这些技术正在开发中,尚未用于临床。 3。简化诊断 人工智能在影像和病理等临床工作流程中的应用,正迎来一个人工智能支持疾病诊断的新时代。从在医学成像中识别异常和潜在的威胁生命的发现,到根据肿瘤细胞等紧急发现来筛选病理病例,人工智能准备帮助临床医生在照顾病人的同时做出诊断、预后和治疗决定。 4。自动检出疟疾 2017年,近50万人死于疟疾,其中大多数是5岁以下的儿童。深度学习技术有助于实现疟疾诊断的自动化,其软件能以90%的准确率和特异性检测和量化疟疾寄生虫。这种自动化的疟疾检测和诊断方法可以帮助提供更准确和及时的诊断,使全世界数百万人受益,并能更好地监测治疗效果。 5。脑健康实时监测与分析——脑的窗户 一个实时监测大脑的新世界有望极大地改善病人的护理。通过对脑电图和其他高频波形进行自动、细致的分析,临床医生可以快速检测出信号故障的电气异常。基于 TB EEG 数据的深度学习算法有助于自动检测危重患者的癫痫发作,而不管其潜在的病因。 6。“ A 眼”:眼健康与疾病的人工智能 人工智能不仅帮助推进了眼科的新方法,还展示了人工智能技术通过专业水平诊断提高初级保健的能力。2018年,美国食品药品监督管理局(Food and Drug Administration)批准了一种新的基于人工智能的糖尿病性视网膜病变病变检测系统,这标志着美国市场上首个完全自动化、基于人工智能的诊断工具,不需要额外的专家审查。该技术还可以在资源有限的情况下发挥作用,在这些情况下,获得眼科护理可能有限。 7。在健康资讯交换下点燃“ FHIR ” 一种新的数据标准,称为快速医疗互操作资源( FHIR ),已经成为共享医疗和其他健康相关信息的事实上的标准。通过其现代的、基于网络的健康信息交换方法, FHIR 保证了一个新的世界的可能性根植于以病人为中心的护理。尽管这个新的世界刚刚出现,但它承诺让患者能够自由地获取自己的健康信息——让他们决定想要共享什么,与谁共享,并要求仔细考虑数据隐私和安全。 8。减少卫生保健管理的负担 自动化日常和高度重复的管理功能。在美国,超过25%的卫生保健支出是由于行政费用,远远超过所有其他发达国家。人工智能可能产生巨大影响的一个重要领域是医疗编码和计费,人工智能可以开发自动化方法。目标是帮助降低编码和计费流程的复杂性,从而减少错误的数量,并最大限度地减少对严格监管的需要。 9。急性中风护理的革命 中风是世界范围内死亡和残疾的主要原因,也是医疗支出的重要来源。在美国,每年有近80万人患中风,花费约340亿美元。人工智能的工具,帮助自动化诊断过程中的缺血性中风可以帮助确定是否有出血的大脑-一个关键的早期洞察,帮助医生选择适当的治疗。这些算法可以自动审查患者的头部 CT 扫描,以确定脑出血,并帮助定位其来源和确定受影响的脑组织体积。 10。亲密合作伙伴违规的隐藏迹象 研究人员正在开发支持人工智能的工具,这些工具可以帮助提醒临床医生,如果病人的伤害可能来自亲密伴侣的暴力( IPV )。通过人工智能系统,他们希望通过赋予临床医生强大的数据驱动工具,帮助打破围绕 IPV 的沉默。虽然筛查亲密伴侣暴力可以帮助发现和预防未来的暴力行为,但在急诊室中发现的 IPV 病例中,只有不到30%被适当地标记为与虐待有关。卫生保健提供者乐观地认为,人工智能工具将进一步补充他们作为一个值得信赖的来源,披露虐待的作用。 11。语音第一技术走向医疗保健 人工智能技术,语音第一技术即将进入临床。使用语音识别和自然语言处理,几家公司正在开发工具,旨在帮助临床医生提供更好的护理,并提供更多重要的东西:患者的质量时间。语音助手正在探索减少医生数据输入负担。医疗保健技术公司正在开发可在消费者级语音技术平台上运行的应用程序,同时遵守《健康保险便携性和责任法》( HIPAA )。 12。缩小心理卫生保健的差距 在美国,精神疾病已经达到了流行病的程度;近五分之一的成年人与精神疾病作斗争,阿片类药物成瘾和滥用每天夺走130多名成年人的生命。在将经过严格验证的精神健康方法集成到智能手机应用程序中,人们看到了有希望的创新。正在开发的一个人工智能应用程序是针对阿片类药物、酒精和其他形式的药物成瘾伴有共同发生的精神疾病的患者。该应用程序为患者提供了一种虚拟的综合团体治疗( IGT ),这是一种非常有效的治疗方法,教授行为和技能,以管理恢复和防止复发。

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