I-Corps: Connected Rapid Innovation Software System to meet Medtech Development Needs

I-Corps项目的更广泛影响/商业潜力是开发人工智能（AI）驱动的技术，旨在加强跨学科合作、指导和人才寻找，以加速医疗和生物技术研究和产品开发。目前对专家的搜索通常依赖于口口相传或互联网搜索，这通常是耗时的，而且可能不准确。拟议的技术可以克服以下障碍：1)促进配对，以确定合作者和导师，并在自愿的技术劳动力背景下建立团队；2)加深对导致专家或导师推荐的因素的理解；3)确定设备创新开发中的成功/失败。拟议系统的目标是在机构内部和机构之间建立有效、持续的联系，并使研究人员能够开展合作研究和产品部署，从而改善健康状况。I-Corps项目基于使用自然语言处理、机器学习和深度学习的软件系统的开发，以加强跨学科协作、指导和人才识别，从而加速医学和生物技术研究和产品开发。它利用公共领域和私有公司中的叙述性文档，并跨多个孤立数据源执行数据融合。根据用户输入的关键词、摘要等信息，通过人工智能推理引擎，用户可以有效地识别出排名靠前的合作者、导师和类似的医疗产品。提出的人工智能技术利用分布式自由文本数据库来提高搜索性能和冗余度。此外，这种强有力的产品监管途径的实施可以满足早期医疗技术开发人员的需求，而不管他们当地的开发生态系统和联系如何。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The broader impact/commercial potential of this I-Corps project is the development of artificial intelligence (AI)-driven technology aimed at enhancing interdisciplinary collaboration, mentorship, and talent searching in order to accelerate medical and biotechnological research and product development. A current search for experts commonly depends on word-of-mouth or internet search, which is often time consuming and may not be accurate. The proposed technology may overcome obstacles by 1) facilitating matchmaking to identify collaborators and mentors, and build teams in the context of a voluntary, technical workforce; 2) advancing the understanding of the factors leading to expert or mentor recommendation; and 3) identifying successes/failures in the development of device innovations. The goal of the proposed system is to create impactful, sustained connections within and across institutions, and enable researchers to conduct collaborative research and product deployment that will improve health. This I-Corps project is based on the development of a software system using natural language processing, machine learning, and deep learning to enhance interdisciplinary collaboration, mentorship, and talent identification in order to accelerate medical and biotechnological research and product development. It leverages narrative documents in the public domain and private corporations and performs data fusion across multiple siloed data sources. The proposed innovation may enable users to effectively identify ranked collaborators, mentors, and similar medical products through the AI inference engine based on user's input such as the keywords and abstract. The proposed AI technology leverages distributed free-text databases for search performance and redundancy. Additionally, the this implementation of robust product regulatory pathways may meet the needs of early-stage med-tech developers regardless of their local development ecosystem and connections.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.