数字健康领导者们对 2023 年预期的预测

在大流行期间，随着企业家和消费者越来越多地接受远程医疗、远程监控以及从睡眠追踪器到运动手环等一系列设备，数字健康初创公司和计划的资金激增。

根据 Rock Health的数据，2021 年数字健康领域的风险投资总额创下 292 亿美元的历史新高。资金在 2022 年降温，到第三季度末降至 126 亿美元，但人工智能等技术的进步和大型科技公司日益增长的兴趣必将推动未来的创新。

2022 年出现了CalmWave、Rippl Care、Outbound AI和Birch AI等西雅图地区的初创公司，以帮助解决从医院过度噪音到老年人心理保健等各种医疗问题。大公司也表现出雄心壮志；亚马逊今年宣布以 39 亿美元的价格收购初级保健公司 One Medical，并推出了一项新的在线医疗服务 Amazon Clinic。

专家们认为 2023 年全国和西雅图地区的数字健康趋势是什么？我们请了五位来权衡他们的预测。

Taha Kass-Hout，技术健康 AI 副总裁兼亚马逊网络服务首席医疗官

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医疗保健和生命科学行业前所未有的创新和合作正在推动该行业通过精确、个性化和人性化的患者体验从疾病护理转向预防。该行业已经对云进行了十年的试验，并且了解技术和机器学习如何能够实现更有针对性的诊断和治疗，即精准医学；个性化患者旅程；并改善健康结果。

在 2023 年及以后，我们预计医疗保健和生命科学组织将继续投资于基础设施的现代化，从数据中获得可操作的见解，并将个性化健康的意义内化。这将涉及将基因组学和其他组学数据整合到治疗开发中，利用机器学习和分析来改进临床医生的工作流程，将社会决定因素数据整合到患者或人群层面的疾病管理中，以及使用结构化和非结构化数据更准确地预测疾病— 帮助该行业从被动的患者护理转变为预防性的患者护理。

Kingsley Ndoh，Hurone AI创始人兼首席策略师

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我们应该期待看到更多以人为本的数字健康创新来支持临床决策，例如用于预测某些抗癌药物临床结果的诊断预测技术或工具。这些工具将越来越多地将更多多样性纳入机器学习模型的训练数据集，并将目标用户的特定需求置于开发过程的核心，包括考虑文化观点。

还将更好地整合可穿戴设备、智能手机应用程序和电子病历生成的数据，以通过人工智能的力量支持临床决策、行为改变和大规模个性化。

WRF Capital董事总经理Loretta Little

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大多数早期数字初创公司的资金在 2023 年将继续紧张，但我看到了几个领域的增长机会。我们将继续看到更多公司通过创新产品和方法提供心理健康服务，例如 Joon 和 Rippl Care，以及专注于改善连接性和工具以实现更好远程护理的公司，例如 Valorant Health 和 Wavely Diagnostics。

远程护理对于服务欠缺的农村社区尤为重要，这些社区只能有限或无法获得附近的医疗资源。这种需求只会增加，部分原因 是患者人口结构的变化 。华盛顿州和全国老年人的比例 预计会增长，尤其是在农村地区。农村老年人口占慢性病患者的很大一部分，需要与服务联系起来。华东CIO大会、华东CIO联盟、CDLC中国数字化灯塔大会、CXO数字化研学之旅、数字化江湖-讲武堂，数字化江湖-大侠传、数字化江湖-论剑、CXO系列管理论坛（陆家嘴CXO管理论坛、宁波东钱湖CXO管理论坛等）、数字化转型网，走进灯塔工厂系列、ECIO大会等

Shyam Gollakota，Wavely Diagnostics and Sound Life Sciences（被谷歌收购）联合创始人，华盛顿大学艾伦学院教授

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在 COVID 期间加速的远程医疗的采用很可能会持续下去。我们可能还会看到越来越多的远程家庭测试，如 COVID-19 或血液测试，这将使远程医疗更接近亲自就诊。虽然人们非常关注使用智能手机和智能手表进行移动健康，但耳塞将成为下一个令人兴奋的监测健康和保健的平台，并且在未来几年内，脑电图 (EEG) 信号可能会开辟新的途径大脑接口的机会。

我们也希望看到许多初创公司应用大型语言模型来解决医疗保健系统中的各种痛点，以提高效率和降低成本。深度学习技术将继续改进，我们将开始看到更有希望的结果来解决重要问题，例如使用 AI 发现药物和疫苗。

Su-In Lee，华盛顿大学计算机科学与工程教授

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明年，我们将看到具有可解释人工智能 (XAI) 功能的人工智能设备，使人类能够理解复杂的黑盒机器学习模型的推理过程。我还看到 FDA 批准流程结合了 XAI 分析，以提高机器学习模型的信任度、透明度、公平性和可操作性。

保险提供商和美国医疗保险和医疗补助服务中心增加的报销将推动 FDA 批准的人工智能设备数量的增加。从长远来看，医疗 AI 设备的成功和公平将取决于 FDA 审批流程的更新程度，以反映机器学习的特定问题。例如，如果没有要求在广泛的肤色上评估 AI 皮肤病学设备，那么在深色皮肤上表现不佳的 AI 设备似乎很可能会公开使用，并且不成比例地误诊深色皮肤的人。

原文：

​Funding for digital health startups and initiatives soared during the pandemic as entrepreneurs and consumers increasingly embraced telehealth, remote monitoring, and a suite of devices from sleep trackers to exercise bands.

Total venture capital investment in digital health hit an all time high of $29.2 billion in 2021, according to Rock Health. Funding cooled in 2022, to $12.6 billion by the end of the third quarter, but advances in technology such as artificial intelligence and the increasing interest of big tech companies are sure to propel innovation in the future.

Seattle-area startups such as CalmWave, Rippl Care, Outbound AI and Birch AI emerged in 2022 to help solve medical problems ranging from excess noise in hospitals, to mental healthcare for seniors. Larger companies also signaled major ambitions; Amazon this year announced its bid to acquire primary care company One Medical for $3.9 billion and rolled out a new online health service, Amazon Clinic.

What trends do experts see for digital health nationally and in the Seattle area for 2023? We asked five to weigh in with their predictions.

Taha Kass-Hout, vice president of technology-health AI, and chief medical officer at Amazon Web Services

Unprecedented innovation and collaboration across the healthcare and life sciences industries is pushing the industry to move from sick care to prevention through a patient experience that is precise, personalized, and human. The industry has been experimenting with cloud for a decade and understands how technology and machine learning can enable more targeted diagnostics and treatments, known as precision medicine; personalize patient journeys; and improve health outcomes.

In 2023 and beyond, we expect healthcare and life sciences organizations to continue to make investments in modernizing their infrastructure, derive actionable insights from data, and internalize what it means to personalize health. This will involve integrating genomics and other omics data into therapeutic development, leveraging machine learning and analytics to improve clinician workflows, incorporating social determinants data into disease management at the patient or population levels, and using structured and unstructured data to predict disease with much better accuracy — helping move the industry from reactive to preventive patient care.

Kingsley Ndoh, founder and chief strategist, Hurone AI

We should expect to see more people-centered innovations in digital health to support clinical decision making, such as diagnostic predictive technologies or tools to predict clinical outcomes for certain cancer drugs. These tools will increasingly incorporate more diversity in training datasets for machine learning models and put the specific needs of the target user at the heart of the development process, including taking into account cultural perspectives.

There will also be better integration of data generated from wearables, smartphone apps and electronic medical records to support clinical decisions, behavioral change, and personalization at scale through the power of artificial intelligence.

Loretta Little, managing director of WRF Capital

Funding for most early-stage digital startups will continue to be tight in 2023, but I see opportunities for growth in several areas. We will continue to see more companies that offer access to mental health services through innovative products and approaches, such as Joon and Rippl Care, and companies focused on improving connectivity and tools for better remote care such as Valorant Health and Wavely Diagnostics.

Remote care is especially important for underserved rural communities that have limited or no access to nearby health resources. This need is only increasing, driven in part by demographic shifts in the patient population. The proportion of seniors in Washington state and across the nation is projected to grow, particularly in rural areas. This rural senior population represents a large percentage of chronic disease sufferers and will need to be linked up with services.

Shyam Gollakota, co-founder of Wavely Diagnostics and Sound Life Sciences (acquired by Google), professor at the University of Washington’s Allen School

The adoption of telehealth that accelerated during COVID is likely here to stay. We may also see an increased number of remote in-home tests like COVID-19 or blood tests that will bring telehealth closer to an in-person visit. While there has been a lot of focus on using smartphones and smartwatches for mobile health, earbuds will be the next exciting platform for monitoring health and wellness as well as potentially, in the next few years, electroencephalography (EEG) signals that can open up new opportunities for brain interfaces.

We will hopefully also see a number of startups apply large language models to address various pain points in the healthcare system with the goal of improving efficiency and reducing cost. Deep learning techniques will continue improving and we will start seeing more promising results for addressing important problems like using AI to discover drugs and vaccines.

Su-In Lee, UW professor of computer science and engineering

Next year we will see AI devices with explainable AI (XAI) functionality, enabling humans to understand the reasoning process of complex, black-box machine learning models. I also see FDA approval processes incorporating XAI analysis to engender trust, transparency, fairness, and actionability of machine learning models.

Increased reimbursement by insurance providers and the U.S. Centers for Medicare & Medicaid Services will drive an increase in the number of FDA-approved AI devices. In the long term, the success and fairness of medical AI devices will rely on the extent to which FDA approval processes are updated to reflect machine learning-specific issues. For example, if there aren’t requirements to evaluate an AI dermatology device on a wide range of skin tones, it seems likely that AI devices that perform poorly on darker skin will become publicly available and disproportionately misdiagnosis people with darker skin.

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