MDDI 演讲稿 · 2021-12-03
杨颖仪常任秘书在第四届新加坡医疗 AI 数据马拉松暨展览会上的演讲
Speech by PS Yong Ying-I at 4th Singapore Healthcare AI Datathon & Expo
要点
- • AI 是「时机已到」的平台技术——已被部署到图像识别、逻辑推理、临床诊断等领域；新加坡医疗已有局部应用——含 NCID 用 TTSH/A*STAR 的 AI 读胸片预测重症肺炎、NUS IDentif.AI 加速 COVID 药物组合筛选、NUHS 用机器人量体温。
- • 标杆 SELENA+：SNEC + SERI + NUS Computing 联合开发的糖尿病视网膜病变 AI——准确率 90%+，已在大多数综合诊所部署、2021 年底覆盖所有综合诊所、年筛查 12 万人；授权给本地初创 EyRis（业务覆盖 20+ 国家与地区）。
- • AI 落地需要「全村合力」——临床、护理、行政、技术、IT 协同；之前已经手工跑通的远程视网膜诊断——为 SELENA+ 铺平了道路。
- • 治理工具：2020 年 1 月《Model AI Governance Framework》第二版；今年 10 月卫生部发布《医疗 AI 指南》（AIHGle），与 HSA 的 AI 医疗设备监管互补。
- • 本地 AI 初创案例：UCARE.AI 的 AI 账单预测器——做到了「使用 AI 透明、加密敏感数据、最小化使用」。
完整译文（中文）
MDDI 英文原文译文 · 翻译日期：2026-05-03
本文已从早期版本的网站迁移过来——格式可能有不一致之处。
早安——尊敬的与会者与朋友们：
感谢邀请我今天发言——这是一场把顶级临床医生、数据科学家、创新者聚到一起、应对医疗紧迫问题的活动。我祝贺新加坡国立大学医学组织（NUHS）、新加坡国立大学（NUS）与麻省理工学院（MIT）「Critical Data」组织这一重要活动。
我有幸在公共服务生涯的多个岗位上与你们的社群合作——在卫生部——与许多人一起开发新加坡国家电子临床医疗记录与医疗 IT；推动学术医学；推进生物医学研究与创新；现在则共同主持政府对「智慧国与数字经济」的资助与发展工作。
AI 在医疗中具有大规模部署的巨大潜能
基于这一视角——今天我想和大家分享几点反思。第一——AI 是一项「时机已到」的平台技术。人工智能与机器学习——将被广泛部署。用于图像识别的 AI——已部署到许多应用领域——包括人脸识别、监控、客户与情感分析。用于逻辑推理的 AI 也在发展——最有名的展示——是一台计算机在「AlphaGo」比赛中击败了世界顶级棋手。它因此可以被应用到临床诊断。在新加坡——AI 已在医疗的若干领域被局部应用——包括目前抗击 COVID-19 的工作中——
a. NCID（国家传染病中心）使用了由 TTSH（陈笃生医院）与 A*STAR 开发的 AI——读胸片、预测患者发展为重症肺炎的可能性——让医生能更及时地分诊与干预。
b. AI 也被用来快速评估「对抗 COVID-19 病毒的最有效药物组合与剂量」。识别一种可用于治疗 COVID-19 的药物——若手工去做——是一项艰苦的任务——既要时间、也要精度——去筛过海量可能的药物组合。NUS 的「IDentif.AI¹」显著加速了这一过程——几天内就给出答案。
c. 智能机器人也被部署到多种用途。NUHS 用 AI 加持的机器人——帮助测温与扫描——以确保入口安全；社区照护设施——则用于密切监测患者——以便及时进行临床与社会心理干预。这对减轻在社区照护设施中照顾大量 COVID-19 患者的医疗专业人士的压力——非常宝贵。
因此——我很高兴看到这么多人参与本次 Datathon——尤其是当我们的医疗社群已经被 COVID-19 的需求拉得很紧时。这显示了你们对「更好的患者照护」事业的投入。我相信——这场 Datathon 会激发跨医疗领域 AI 应用的新想法——惠及你们服务的患者。
应用需要多方利益相关方的「系统级支持」
这就引到我的第二点——从我刚谈到的「个体创新火花」——拉升到「系统级视角」。我的第二个反思是——把 AI 创新应用到改善医疗——需要一支多学科团队来落地——也需要多利益相关方的努力。「这要全村合力」（it takes a village）。让我用 SELENA+ 作为说明。SELENA+ 是医疗行业的国家级 AI 项目——由「新加坡全国眼科中心」（SNEC）、「新加坡眼科研究院」（SERI）与 NUS 计算学院共同开发——是一个分析眼底扫描、检测糖尿病视网膜病变（DR）的 AI 算法。SELENA+ 的准确率达 90% 以上——与专业评级师的表现相当——但用时短得多。
SELENA+ 不是「凭空出现」——它是「远程诊断影像创新」的最新一章。新加坡的「远程放射影像」（teleradiology）努力——至少 15 年前就开始了——包括我们的 X 光片在印度被读、美国的 X 光片由我们读——以利用时区差。我们逐渐学会了「确信于临床标准与患者安全」——这是医疗创新成功的关键因素。我记得——当时 SNEC 引入了「在综合诊所对患者进行糖尿病视网膜病变远程诊断」。我们国家级专科中心的眼科专家——能触达基层医疗系统中更广泛的患者——这是一大跨越——能比以往更早检测与治疗患者。「早期检测」对应对糖尿病非常宝贵。让这件事发生——需要专科中心与基层医疗网络在很多事情上的协作——包括工作流、患者管理、数据传输。它要求临床医生、护士、行政人员、技术专家与 IT 专业人士走到一起。要解决障碍、构建协作——需要辛勤工作。因为我们已经把这件事「手工跑通」过——所以现在引入 SELENA+ 时变得顺滑。「全村合力」加上「持久的决心与承诺」——才能让创新落地。
SELENA+ 已在大多数综合诊所部署——并将于 2021 年底覆盖所有综合诊所。预计每年筛查 12 万名患者——覆盖全国综合诊所所有的糖尿病眼科筛查。SELENA+ 也已授权给本地初创 EyRis——其业务覆盖欧盟、中国、东南亚等 20 多个国家与地区。
旅程继续。我们正在举办「新加坡医疗 AI 大挑战」（AI in Healthcare Grand Challenge）——目标是应对影响新加坡人的主要慢性病——糖尿病、高血压、高血脂。我期待——研究者、临床医生、计算机科学家、数据分析师组成的多学科团队——为此开发出更多创新方案。
「伦理 AI 与信任」的重要性——以及新加坡的思想领导力
我留给各位的第三点——是「我们对 AI 的采用——必须以『安全、伦理使用』为前提」。我们必须管理一种微妙的平衡——既鼓励创新——又维持公众对所部署技术的高水平信任。在医疗领域——这一点尤其重要——因为「误用」可能给患者带来巨大伤害——并摧毁信任。
回应「AI 使用伤害风险」的担忧——需要我们深入思考一些跨政策、法律、技术、伦理的问题——比如——
a. AI 容易受错误与偏见影响。我们如何评估「什么准确度算可接受」——以及它如何在不同用例间变化？
b. 我们如何确保 AI 方案足够稳健——以防范 AI 算法的不负责任部署、训练数据的不当管理？以及如何防范网络安全风险（包括数据操纵）？
c. 一旦 AI 系统出错——我们如何确定问责？在真实生活的并发情境里——谁来负责？
这些挑战在哪里都适用——但在医疗中尤具回响——因为它影响患者的健康与生命。所以在你们的讨论与创新开发中——请把治理、伦理与韧性的考量——融入到 AI 创新的设计中。
新加坡已采取具体措施——帮助建立原则与实操指南——确保 AI 以负责任、伦理的方式被部署。这对公众与专业人士对 AI 的信任至关重要。2020 年 1 月——新加坡发布了《Model AI Governance Framework》第二版——把伦理原则——翻译为机构能采用的实务措施。今年 10 月——卫生部（MOH）向医疗提供者发布了《AI 在医疗的指南》（AI in Healthcare Guidelines, AIHGle）——以建议支持患者安全、提升对 AI 的信任——并把最佳实践分享给 AI 系统开发者与落地团队。对 AI 医疗设备制造商——这与新加坡卫生科学局（HSA）对 AI 医疗设备的监管互补。
医疗行业中的多家公司——已经实施了《Model AI Governance Framework》的条款。举一个例子——本地医疗初创 UCARE.AI——开发了 AI 账单预测器——为患者推算精准的住院账单估值。它在 AI 使用上保持透明——对账单预测有疑虑的客户被鼓励提出；UCARE.AI 也对敏感数据加密、最小化使用——以维护患者保密。据我了解——该 AI 方案的部署进展良好——医院能给出更准确的账单估值——患者也对账单大致规模有了更安心的预期。
新加坡也积极参与「伦理 AI」的国际讨论。比如——IMDA 与 PDPC 正在开发一个「AI 治理测试框架」——与欧盟、联合国教科文组织、经合组织（OECD）等国际公认原则一致。AI 的最佳实践——也出现在我们的《数字经济协定》中。这些努力支撑新加坡「成为 AI 方案开发、试点、部署与规模化的全球枢纽」的愿景。
结尾之前——祝大家有一场富有成效的 Datathon。这几天专题与工作坊中的对话——把当下医疗议题的一些最新解决方案带到台前。我希望——它们能激励你们——开发出惠及我们医疗系统与你们服务的患者的创新。
再次祝贺 NUHS、NUS 与 MIT Critical Data——让这一活动成真。祝你们与所有参赛者——一切顺利。谢谢。
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英文原文
MDDI 官网原始记录 · 抓取日期：2026-05-02
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Good morning, distinguished participants and friends.
Thank you for inviting me to speak today in an event bringing together top clinicians, data scientists and innovators to address pressing problems in healthcare. I congratulate National University Health System (NUHS), National University of Singapore (NUS) and the Massachusetts Institute of Technology (MIT) Critical Data for organising this important event.
I have had the pleasure of working with your community in my various jobs in my public service career – at the Ministry of Health, working with many of you to develop our national electronic clinical medical records and healthcare IT, on developing academic medicine and advancing our work in biomedical sciences research and innovation, and now co-chairing the Govt’s funding and development effort on Smart Nation and the Digital Economy.
AI has huge potential for widespread deployment in healthcare
With this perspective, I’d like to share a few reflections with you today. First, AI is a platform technology whose time has come. Artificial intelligence and machine learning will be widely deployed. AI for image recognition is being deployed in many application areas, including facial recognition, surveillance, customer and sentiment analysis. AI for logic deduction is also being developed, most famously demonstrated by a computer winning an AlphaGo contest against one of the best players in the world. It can therefore be applied to clinical diagnostics. In Singapore, AI is being applied in pockets in healthcare, including in the current fight against Covid-19:
a. NCID used AI developed by TTSH & A*STAR to read chest-x-rays and to calculate the likelihood that a patient may develop severe pneumonia. This has enabled more timely intervention from clinicians to better triage patients.
b. AI has been used to quickly evaluate the most effective combinations and dosage of medicines to combat the Covid-19 virus. Identifying a drug that can be used to treat Covid-19 is a grueling task, if done manually. It would require both time and precision to sieve through the massive range of possible drug combinations for effective solutions. NUS’ IDentif.AI1 has accelerated this process significantly, yielding answers within just days.
c. Smart bots are also being deployed for various uses. NUHS is using AI-enabled robots to help temperature taking and scanning for safe entry. Community care facilities for close monitoring of patients to enable timely clinical and psychosocial interventions. This has been valuable to reduce the strain on our healthcare professionals caring for many Covid-19 patients in our community care facilities.
I am therefore happy to see so many people participating in this Datathon, and all the more so when our healthcare community is already so heavily stretched by the demands on you from Covid-19. It shows your dedication to the cause of better patient care. I am confident that the Datathon will spark new ideas for the potential uses for AI across the spectrum of healthcare, which will benefit the patients you serve.
Application requires system-level support from multiple stakeholders
This brings me to my second point, which zooms up from the individual innovative spark I just spoke of to a system-level viewpoint. My second reflection is that the application of AI innovations to improve healthcare requires a multidisciplinary team to bring it to fruition, and it requires multi-stakeholder effort. It takes a village. Let me explain, using SELENA+ as an illustration. SELENA+ is the National AI project from the healthcare sector. Jointly developed by the Singapore National Eye Centre, the Singapore Eye Research Institute and the NUS School of Computing, it is an AI algorithm that analyses eye scans to detect diabetic retinopathy. SELENA+ has achieved accuracy levels of over 90%, on par with the performance of professional graders but doing it in a much shorter time.
SELENA+ is not a bolt out of the blue, but is the latest chapter of innovations in remote diagnostic imaging. Singapore’s efforts in teleradiology started at least a decade and half ago, including our x-rays being read in India, and US x-rays being read by us to take advantage of time-zone differences. We gradually learnt to be assured of clinical standards and patient safety, critical factors for successful healthcare innovations. I recall SNEC then introduced the remote diagnosis of diabetic retinopathy, for our patients in our polyclinics. The ability of our eye specialists in our national specialty centre to reach a much broader swathe of patients in the primary care system was a huge step forward, enabling us to detect and treat patients much earlier than before. Early detection was immensely valuable to tackling diabetes. Making this happen required collaboration between our specialty centres and our primary care network in many things, including workflow processes, patient management and data transfers. It required clinicians, nurses, administrators, technical specialists, and IT professionals to come together. It took hard work to resolve obstacles and build collaborations. Because we made this work manually so to speak, introducing SELENA+ now was made smoother. It takes a village, with sustained determination and commitment over time, to make innovations happen.
SELENA+ has been deployed in most polyclinics already and will reach all polyclinics by the end of 2021. It is projected to screen 120,000 patients annually, covering all diabetic eye screenings in our polyclinics across the nation. SELENA+ has also been licensed to local start-up EyRis, which has business in more than 20 countries and regions including EU, China and SEAsia.
The journey continues. We have an on-going AI in Healthcare Grand Challenge for Singapore, which aims to tackle major chronic diseases afflicting Singaporeans such as diabetes mellitus, hypertension and hyperlipidemia. I look forward to multidisciplinary teams of researchers, clinicians, computer scientists and data analysts developing more innovative solutions for this.
The importance of ethical AI and trust, and Singapore’s thought leadership
My third point to leave you with, is that our adoption of AI must be premised upon its safe and ethical use. We must manage a delicate balance between encouraging innovation while maintaining a high level of public trust in the technologies deployed. This is especially so in healthcare where misapplication can cause great harm to patients and destroy trust.
Addressing concerns about the risk of harm from using AI requires us to think deeply about questions that span policy, legal, technological and ethical domains. For example:
a. AI can be susceptible to errors and bias. How do we assess what level of accuracy is considered acceptable, and how might this vary across use cases?
b. How do we ensure that AI solutions are sufficiently robust to guard against irresponsible deployment of AI algorithms or improper management of training data? And to protect against cybersecurity risks, including data manipulation?
c. How do we determine accountability, if an AI system goes awry? In real life complications, who would be responsible?
These challenges apply to AI everywhere, but they have a particular resonance in healthcare because it affects patients’ health and their lives. So in your discussions and in developing innovations, do build in governance, ethical and resilience considerations into the design of the AI innovation.
Singapore has taken concrete measures to help establish principles and practical guidelines to ensure that AI is deployed in a responsible and ethical manner. This is critical for public and professional trust in AI. In Jan 2020, Singapore released the 2nd edition of the Model AI Governance Framework. This translates ethical principles into practical measures that organisations can adopt. In Oct this year, MOH issued to healthcare providers its AI in Healthcare Guidelines or AIHGle, with recommendations to support patient safety and improve trust in AI and sharing best practices with AI system developers and implementation teams. For AI medical device manufacturers, I understand that this complements HSA’s regulations for AI Medical Devices.
Various companies in the healthcare sector have implemented the Model AI Governance provisions. To cite one example, UCARE.AI which is a local healthcare start-up, has developed an AI-powered cost predictor to derive accurate estimates of hospital bills for patients. It has been transparent in its usage of AI, clients with concerns about bill predictions were encouraged to highlight them; and UCARE.AI also encrypted sensitive data and minimised usage of this data to preserve patient confidentiality. I understand deployment of the AI solution has gone well, with hospitals providing more accurate bill estimates, and patients having greater peace of mind over the likely size of these bills.
Singapore is also actively involved in international discussions on ethical AI. For example, IMDA and PDPC are developing an AI Governance testing framework, consistent with internationally recognized principles from EU, UNESCO, OECD and others. Best practices in AI feature in our Digital Economy Agreements. These various efforts support Singapore’s vision to become a global hub for the development, test-bedding, deployment and scaling of AI solutions.
Let me conclude by wishing you a rich and fruitful Datathon. The conversations in panels and workshops these few days bring to the fore some of the latest solutions for today’s healthcare issues. I hope that they will inspire you to develop innovations that will benefit our healthcare system and the patients that you serve.
My congratulations to NUHS, NUS and MIT Critical Data for making this event happen. All the very best to you and the participants. Thank you.
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