HIPDS医创研究院是学会设立的专项学术平台，于2021年伴随课题研究合作计划和医学研究与数据中心（MRDC）建设逐步形成。研究院不作为独立行政序列，而是依托学术发展部、学术评审委员会、MRDC和临床实践与循证中心（CPEC）等既有机构，统筹课题申报、研究设计、数据支持、成果整理和转化咨询等学术服务。
The HIPDS Research Institute is a specialised academic platform established by the Society and gradually formed in 2021 alongside the Research Collaboration Programme and the development of the Medical Research and Data Centre (MRDC). It is not a separate administrative hierarchy; instead, it coordinates research applications, study design, data support, outcome documentation, and translation consultation through existing bodies including the Academic Development Department, Academic Review Committee, MRDC, and the Centre for Clinical Practice and Evidence-Based Medicine (CPEC).
研究院的工作重点，是把会员和合作机构的研究需求连接到学会既有组织体系中：由学术发展部负责协调执行，MRDC提供研究数据管理和质量控制支持，CPEC提供循证实践与临床路径建议，学术评审委员会负责独立评审与认证。
The Institute's role is to connect the research needs of members and partner institutions with the Society's established organisational system: the Academic Development Department coordinates implementation, MRDC provides research data management and quality control support, CPEC offers evidence-based practice and clinical pathway input, and the Academic Review Committee conducts independent review and certification.
自2019年学会成立以来，研究院相关协作机制共孵化和管理86个课题研究项目，覆盖精准医疗、医疗人工智能、转化医学、公共卫生和临床技术创新五大方向，参与研究人员来自亚太地区60余家医疗机构和科研院所。其优势在于"转化导向"的项目设计理念——在课题立项之初即明确临床转化路径，并结合顾问委员会和相关专家资源协助规划知识产权保护与产业化策略，课题临床转化率达35%，远超行业平均水平（不足5%）。
Since the Society's founding in 2019, the Institute's collaborative mechanism has supported the incubation and management of 86 research projects across five priority domains: precision medicine, medical artificial intelligence, translational medicine, public health, and clinical technology innovation, with participating researchers from over 60 medical institutions and research organisations across the Asia-Pacific region. Its strength lies in a translation-oriented project design approach: clinical translation pathways are defined at the outset, while the Advisory Committee and relevant expert resources help plan intellectual property protection and commercialisation strategies. The clinical translation rate of supported projects has reached 35%, far exceeding the industry average of under 5%.
会员的研究成果和创新项目，可依照学会评审规则提交至学术评审委员会开展独立评审，并获得学会颁发的正式评审认证报告。研究院负责前期材料规范、项目衔接和成果整理支持，确保评审与研究服务既相互衔接，又保持职责边界清晰。
Members' research outcomes and innovation projects may be submitted to the Academic Review Committee for independent review under the Society's review rules and may receive formal review and certification reports issued by the Society. The Institute provides preliminary documentation support, project coordination, and outcome organisation, ensuring that research services and review functions remain connected while preserving clear responsibility boundaries.
五大前沿领域，引领医疗创新突破
Five Frontier Domains Driving Healthcare Innovation Breakthroughs
基于基因组学、蛋白质组学和代谢组学的多组学整合分析，开发个体化疾病风险评估模型和精准治疗方案。重点关注恶性肿瘤、罕见遗传病和慢性代谢疾病的精准诊疗研究。
Through multi-omics integrative analysis based on genomics, proteomics, and metabolomics, we develop individualized disease risk assessment models and precision treatment protocols, with a particular focus on precision diagnosis and therapy for malignant tumors, rare genetic disorders, and chronic metabolic diseases.
探索人工智能、大数据和物联网技术在医学影像辅助诊断、智能临床决策支持、远程医疗和健康管理等领域的创新应用，推动医疗服务模式的数字化转型。
We explore innovative applications of artificial intelligence, big data, and Internet of Things technologies in medical imaging-assisted diagnosis, intelligent clinical decision support, telemedicine, and health management, driving the digital transformation of healthcare delivery models.
研究基础研究成果向临床应用转化的系统方法论和最佳实践框架，建立标准化的转化评估模型，提升医学研究的临床转化效率和成功率。
We investigate systematic methodologies and best-practice frameworks for translating basic research findings into clinical applications, establish standardized translational assessment models, and enhance the efficiency and success rates of clinical translation in medical research.
聚焦传染病预警监测、疫情快速响应、公共卫生政策评估和应急医疗资源调度等关键领域，构建区域性公共卫生应急协作研究网络。
Focusing on infectious disease early-warning surveillance, rapid epidemic response, public health policy evaluation, and emergency medical resource coordination, we build regional collaborative research networks for public health emergency preparedness.
围绕微创手术技术、新型生物材料、智能医疗器械和创新诊断方法等方向，推动临床技术的迭代升级和应用推广，改善患者诊疗体验和临床结局。
Centered on minimally invasive surgical techniques, novel biomaterials, intelligent medical devices, and innovative diagnostic methods, we advance the iterative development and broader application of clinical technologies to improve patient care experiences and clinical outcomes.
每年两轮公开申报，欢迎符合条件的研究者积极申请
Two Open Application Rounds Per Year — Eligible Researchers Are Encouraged to Apply
HIPDS课题研究项目实行年度双轮申报制度。第一轮申报时间为每年1月1日至1月31日，评审结果于3月公布；第二轮申报时间为每年7月1日至7月31日，评审结果于9月公布。逾期提交的申请将自动顺延至下一轮评审。
HIPDS research projects operate on a biannual application cycle. The first round accepts submissions from January 1 to January 31, with review results announced in March; the second round accepts submissions from July 1 to July 31, with results announced in September. Applications submitted after the deadline will automatically be deferred to the following review round.
登录HIPDS研究管理平台，填写课题申报书并上传支撑材料。
Log in to the HIPDS Research Management Platform, complete the project application form, and upload all supporting materials.
研究管理部在10个工作日内完成材料完整性和合规性审查。
The Research Management Office will complete a review of documentation completeness and compliance within 10 business days.
由3-5位相关领域专家进行独立评审，重点评估创新性和转化可行性。
Three to five domain experts conduct independent evaluations, with primary assessment of innovation merit and translational feasibility.
通过评审的课题进行公示，公示无异议后签署课题协议正式立项。
Approved projects are publicly announced; upon conclusion of the objection period, the project agreement is signed to formally establish the project.
科研创新与临床转化的卓越实践
Excellence in Research Innovation and Clinical Translation
该项目由HIPDS联合香港大学医学院、新加坡国立大学附属医院和深圳市人民医院等5家机构共同研发，历时两年完成。研究团队构建了包含超过50万张肺部CT影像的高质量标注数据集，并基于此开发了新型深度学习肺结节检测与良恶性分类算法。系统在多中心前瞻性临床验证中表现优异，对早期肺癌的检测灵敏度达到94.7%，特异度达到91.2%，显著优于传统影像诊断方法。目前该系统已进入医疗器械注册审批阶段，预计将于2027年上半年获批上市，惠及更多患者。
Developed over two years through a collaboration between HIPDS and five institutions — including the Li Ka Shing Faculty of Medicine at the University of Hong Kong, the National University Hospital of Singapore, and Shenzhen People's Hospital — this project produced a high-quality annotated dataset of over 500,000 pulmonary CT images and a novel deep learning algorithm for pulmonary nodule detection and benign-malignant classification. In multicenter prospective clinical validation, the system achieved a sensitivity of 94.7% and specificity of 91.2% for early-stage lung cancer detection, substantially outperforming conventional radiological diagnostic methods. The system has entered the medical device registration and regulatory approval process and is expected to receive market authorization in the first half of 2027.
该课题聚焦骨科临床中骨缺损修复的重大需求，由马来西亚大学医学中心牵头、联合亚太地区多家材料科学和骨科研究机构协同攻关。研究团队成功开发出一种具有优异生物相容性和可控降解特性的新型复合骨修复材料，该材料能够在植入后12-18个月内逐步降解并被自体骨组织替代，避免了传统金属植入物需二次手术取出的问题。临床试验数据显示，该材料在骨折愈合率和并发症控制方面均优于现有主流产品，目前已完成二期临床试验，相关技术专利已获授权。
Addressing a critical clinical need in orthopedic bone defect repair, this project was led by the University of Malaya Medical Centre in collaboration with multiple materials science and orthopedic research institutions across the Asia-Pacific region. The team successfully developed a novel composite bone repair material with superior biocompatibility and controllable degradation characteristics. The material gradually degrades and is replaced by autologous bone tissue within 12 to 18 months post-implantation, eliminating the need for secondary surgery to remove conventional metal implants. Clinical trial data demonstrate that the material outperforms currently available products in fracture healing rates and complication control. Phase II clinical trials have been completed, and relevant technology patents have been granted.
面对东南亚地区登革热疫情防控的严峻挑战，该项目整合气象数据、蚊媒密度监测、人口流动信息和历史疫情数据等多源信息，利用机器学习算法构建了区域尺度的登革热疫情预测模型和智能预警平台。平台已在泰国、马来西亚和越南三个国家的12个试点地区部署运行，能够提前2-4周预测疫情暴发风险，准确率超过82%。该平台的推广应用有效提升了区域公共卫生应急响应的时效性和精准性，为东南亚登革热防控提供了重要的技术支撑和决策参考。
In response to the formidable challenges of dengue fever prevention and control in Southeast Asia, this project integrates meteorological data, mosquito vector density monitoring, population mobility information, and historical epidemiological data from multiple sources, employing machine learning algorithms to construct a regional-scale dengue fever outbreak prediction model and intelligent early-warning platform. The platform has been deployed in 12 pilot regions across Thailand, Malaysia, and Vietnam, with the capability to forecast epidemic outbreak risk 2 to 4 weeks in advance at an accuracy rate exceeding 82%. The platform's wider adoption has effectively enhanced the timeliness and precision of regional public health emergency responses, providing important technical support and decision-making reference for dengue fever prevention and control across Southeast Asia.
课题申报按年度两轮开放，学术评审申请按相应流程受理，欢迎符合条件的医学专业人士积极参与
Research project applications open in two annual rounds, while academic review requests are accepted according to the relevant process. Qualified medical professionals are warmly invited to participate.
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