SBIR Phase I: MedSwarm, an Artificial Intelligence System for Medical Decision Making

SBIR 第一阶段：MedSwarm，用于医疗决策的人工智能系统

• 批准号: 1840937
• 负责人: Louis Rosenberg
• 金额: $ 22.46万
• 依托单位: Unanimous A.I., Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-02-01 至 2020-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1840937&HistoricalAwards=false
• 关键词: SBIR; Phase; MedSwarm; Artificial; Intelligence

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is to demonstrate the feasibility of MedSwarm, a software product for radiology that will significantly improve patient outcomes and reduce medical costs. MedSwarm is an AI-based collaborative decision-making system that will significantly amplify diagnostic accuracy across a wide range diagnoses, not by replacing radiologists with AI, but by amplifying their human expertise. MedSwarm employs a technology called Artificial Swarm Intelligence that collects input from small groups of doctors and uses AI to optimize their combined accuracy. This hybrid human-AI system will achieve diagnostic accuracies that significantly outperform (a) individual radiologists using standard techniques, and (b) current state-of-the-art in software-only AI. As a product, the MedSwarm system will be deployed as downloadable software compatible with all existing radiological workstations. Distribution to diagnostic teams will be as easy as opening a web-browser and connecting to a cloud-based server. The market opportunity is best modeled on the Computer Aided Detection (CAD) market, which is projected at $1.9 Billion by 2022. MedSwarm will not compete with current CAD products, as it will provide additional accuracy on all radiology workstations, whether or not other CAD tools are in use.This Small Business Innovation Research (SBIR) Phase I project aims to develop, test, and validate MedSwarm, the first commercially viable system for amplifying diagnostic accuracy of radiologists by combining human and machine intelligence. The technology, called Swarm AI, does not replace radiologists with AI, but connects small groups of radiologists together and optimizes their combined insights, enabling accuracy levels that greatly exceed baseline abilities. The company anticipates diagnostic error reductions of at least 30% using the system developed in Phase I, with the majority of the improvement being in the reduction of false-positives. The proposed research requires the development of new software modules, new optimization algorithms, and new interface protocols for enabling radiologists to connect online, view the same images at the same time, and work together as a real-time system to converge on unified diagnoses. Development also requires machine learning optimization across multiple experimental parameters, to reduce the time required for each diagnosis, increasing throughput and reducing costs. The company's goals for Phase I include a proof-of-concept system that (i) significantly outperforms individual radiologists, (ii) significantly outperforms state-of-the-art machine learning systems, and (iii) can identify which cases are most appropriate for this system, as compared to alternate methods.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.

这个小型企业创新研究（SBIR）第一阶段项目的更广泛的影响/商业潜力是证明MedSwarm的可行性，MedSwarm是一种放射学软件产品，将显着改善患者预后并降低医疗成本。MedSwarm是一个基于人工智能的协作决策系统，它将显著提高各种诊断的诊断准确性，不是通过用人工智能取代放射科医生，而是通过增强他们的人类专业知识。MedSwarm采用了一种名为Artificial Swarm Intelligence的技术，该技术收集了一小群医生的输入，并使用AI来优化他们的组合准确性。这种混合人工智能系统将实现诊断准确性，显著优于（a）使用标准技术的个体放射科医生，以及（B）当前最先进的纯软件AI。作为一种产品，MedSwarm系统将作为与所有现有放射工作站兼容的可下载软件部署。向诊断团队分发将像打开Web浏览器并连接到基于云的服务器一样简单。市场机会最好以计算机辅助检测（CAD）市场为模型，预计到2022年将达到19亿美元。MedSwarm将不会与当前的CAD产品竞争，因为它将在所有放射工作站上提供额外的准确性，无论是否使用其他CAD工具。这个小企业创新研究（SBIR）第一阶段项目旨在开发，测试和验证MedSwarm，第一个商业上可行的系统，通过结合人机智能来提高放射科医生的诊断准确性。这项名为Swarm AI的技术不会用AI取代放射科医生，而是将一小群放射科医生连接在一起，优化他们的综合见解，使准确性水平大大超过基线能力。该公司预计，使用第一阶段开发的系统，诊断错误至少减少30%，其中大部分改进是减少假阳性。拟议的研究需要开发新的软件模块，新的优化算法和新的接口协议，使放射科医生能够在线连接，同时查看相同的图像，并作为一个实时系统共同工作，以实现统一的诊断。开发还需要跨多个实验参数进行机器学习优化，以减少每次诊断所需的时间，提高吞吐量并降低成本。该公司第一阶段的目标包括一个概念验证系统，该系统（i）显著优于单个放射科医生，（ii）显著优于最先进的机器学习系统，（iii）可以识别哪些病例最适合该系统，该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识产权进行评估来支持。优点和更广泛的影响审查标准。

项目成果

Sales Forecasting, Polls vs Swarms

销售预测、民意调查与群体预测

• DOI: 10.2139/ssrn.3390043
• 发表时间: 2019
• 期刊: SSRN Electronic Journal
• 作者: Willcox, Gregg;Rosenberg, Louis;Schumann, Hans
• 通讯作者: Schumann, Hans

Artificial Swarm Intelligence employed to Amplify Diagnostic Accuracy in Radiology

人工智能群体智能用于提高放射学诊断的准确性

• DOI: 10.1109/iemcon.2018.8614883
• 发表时间: 2018
• 期刊: IEMCON 2018
• 作者: Rosenberg, Louis;Lungren, Matthew;Halabi, Safwan;Willcox, Gregg;Baltaxe, David;Lyons, Mimi
• 通讯作者: Lyons, Mimi

"Human Swarming" Amplifies Accuracy and ROI when Forecasting Financial Markets

“人类蜂拥而至”提高了金融市场预测的准确性和投资回报率

• 发表时间: 2019
• 期刊: HCC 2019
• 作者: Rosenberg, Louis;Willcox, Gregg;Schumann, Hans;Pescetelli, Niccolo
• 通讯作者: Pescetelli, Niccolo

Swarm Intelligence Amplifies the IQ of Collaborating Teams

群体智能增强协作团队的智商

• DOI: 10.1109/ai4i.2019.00036
• 发表时间: 2019
• 期刊: ai4i 2019
• 作者: Rosenberg, Louis;Willcox, Gregg
• 通讯作者: Willcox, Gregg

Measuring Group Personality with Swarm AI

使用 Swarm AI 测量群体个性

• 发表时间: 2019
• 期刊: TransAI 2019
• 作者: Rosenberg, Louis;Willcox, Gregg;Askay, David;Metcalf, Lynn;Kwong, Bryon;Liu, Richard
• 通讯作者: Liu, Richard