I-Corps: Contextualization of Explainable Artificial Intelligence (AI) for Better Health

I-Corps：可解释人工智能 (AI) 的情境化以改善健康

• 批准号: 2331366
• 负责人: Ying Ding
• 金额: $ 5万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2331366&HistoricalAwards=false
• 关键词: Corps; Contextualization; Explainable; Artificial; Intelligence

The broader impact/commercial potential of this I-Corps project is the development of the explainable Artificial Intelligence (XAI) methods for healthcare data. Currently, the number of electronic medical records is increasing while machine learning and deep learning models, especially large language models, have been employed to address healthcare needs. However, the healthcare domain is highly regulated and explainability for the black-box AI model becomes increasingly critical for any AI application. Users need to comprehend and trust the results and output created by machine learning algorithms. The proposed XAI technology may be used to describe an AI model, its expected impact, and potential biases. Further, the proposed technology may be used to transfer AI predictions into explainable medical interventions to enable the last mile delivery of AI in healthcare The commercial potential of these technologies may impact three major groups: health insurance companies who may provide better care management interventions and achieve personalized care delivery based on XAI; health analytic companies who rely on explanation to further enhance their products and meet the government regulations; and medical device startups who demand explainable analytical outputs based on the collected data from medical devices to enrich their user experience.This I-Corps project is based on the development of explainable Artificial Intelligence (XAI) methods applied to the healthcare industry. Providing explainability is critical for AI health applications. Healthcare is a unique domain with multimodality data: tableau data about patient demographic information, textual data about medical notes, time series data about vital sign measures, images about medical scan, and wavelet data about EEG and ECG. To provide a holistic view of these data, deep learning is used to create universal embeddings on different modalities of data and build the prediction models for health risks. But deep learning methods lack transparency and demand explainability. The proposed technology combines integrated gradients with ablation studies to identify the contributing factors of different data components in the explanation. In addition, the proposed platform adds knowledge graphs into the prediction and explanation workflow to detect the relationships between contributing features to generate an explanation with a holistic view, and translates weights or feature importance into risk scores to enable the last mile delivery of AI in healthcare. The proposed XAI method may be used to explain the importance of input data components, identify the contributing features at the individual patient level and the patient cohort level; scale and save computational resources; and self-improve by using reinforcement learning to enhance positive feedback.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.

该 I-Corps 项目更广泛的影响/商业潜力是针对医疗数据开发可解释的人工智能 (XAI) 方法。 目前，电子病历的数量不断增加，而机器学习和深度学习模型，特别是大语言模型，已被用来满足医疗保健需求。 然而，医疗保健领域受到严格监管，黑盒人工智能模型的可解释性对于任何人工智能应用程序都变得越来越重要。 用户需要理解并信任机器学习算法创建的结果和输出。 拟议的 XAI 技术可用于描述人工智能模型、其预期影响和潜在偏差。 此外，所提出的技术可用于将人工智能预测转化为可解释的医疗干预措施，从而实现医疗保健领域人工智能的最后一英里交付。这些技术的商业潜力可能会影响三个主要群体：健康保险公司，他们可以提供更好的护理管理干预措施并实现基于XAI的个性化护理服务；依靠解释来进一步增强产品并满足政府法规的健康分析公司；以及需要基于从医疗设备收集的数据进行可解释的分析输出的医疗设备初创公司，以丰富其用户体验。这个 I-Corps 项目基于应用于医疗保健行业的可解释人工智能 (XAI) 方法的开发。 提供可解释性对于人工智能健康应用至关重要。医疗保健是一个具有多模态数据的独特领域：有关患者人口统计信息的表格数据、有关医疗记录的文本数据、有关生命体征测量的时间序列数据、有关医疗扫描的图像以及有关脑电图和心电图的小波数据。为了提供这些数据的整体视图，深度学习用于在不同模式的数据上创建通用嵌入，并构建健康风险的预测模型。但深度学习方法缺乏透明度并且需要可解释性。 所提出的技术将积分梯度与消融研究相结合，以确定解释中不同数据成分的影响因素。 此外，所提出的平台将知识图添加到预测和解释工作流程中，以检测贡献特征之间的关系，以生成具有整体视图的解释，并将权重或特征重要性转化为风险评分，以实现医疗保健中人工智能的最后一英里交付。 所提出的 XAI 方法可用于解释输入数据组件的重要性，识别患者个体水平和患者队列水平的贡献特征；扩展并节省计算资源；该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。