权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Bioethical Considerations for Building, Evaluating, and Implementing Artificial Intelligence in Perinatal Mood and Anxiety Disorders

构建、评估和实施人工智能治疗围产期情绪和焦虑症的生物伦理考量

基本信息

批准号：
10593284
负责人：
Michael B. Laskoff
金额：
$ 13.73万
依托单位：
IRIS OB HEALTH INC.
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-07-21 至 2024-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10593284
关键词：
Academia Academic Medical Centers Address Anxiety Disorders Artificial Intelligence Attention Attitude Bioethics Case Study Cause of Death Cellular Phone Child Collaborations Complex Conceptions Data Data Sources Development Devices Discipline of obstetrics Early Diagnosis Early treatment Electronic Health Record Ethics Ethnic Origin Expert Systems Explosion Feedback Female of child bearing age Fetus Fostering Future Future Generations Health Health Technology Industry Informatics Intervention Interview Investigation Iris Knowledge Leadership Left Link Maternal Health Medicine Mental Depression Mental Health Mental disorders Mentors Methods Modeling Mood Disorders Mothers Newborn Infant Output Parents Pathway interactions Patient Education Patients Peer Review Perinatal Perinatal Care Phase Postpartum Depression Postpartum Period Postpartum Women Pregnancy Prevention Psychiatry Publishing Qualitative Research Race Research Research Personnel Resources Review Literature Risk Sampling Small Business Technology Transfer Research Structure Subgroup Suicide Survey Methodology Surveys Technology Therapeutic Intervention Time Training Trust Woman Work algorithmic bias base dashboard data sharing design digital health experience health data model building multidisciplinary parent grant patient engagement pediatrician perinatal mental health preference prototype response routine screening shared decision making user centered design

项目摘要

PROJECT SUMMARY Postpartum depression (PPD) is a common, yet treatable illness if detected early, but it can also have deleterious effects to the mother and child if left untreated. Routine screening for PPD is considered best practice but does not consistently occur due to time and resource constraints. As a result, therapeutic interventions are initiated late and many PPD cases go undetected altogether. Artificial intelligence (AI) models can bridge the PPD identification gap and have been shown to proactively, accurately identify women with an elevated risk for PPD. Iris OB Health, digital health startup company, has built a predictive AI model for PPD. The Iris team is developing an interface that presents our AI model to patients and clinicians to facilitate shared decision-making about interventions to decrease risk. Through this work, we are recognizing the need to better understand the bioethical implications of patient-facing AI. Bioethics research in AI has focused on presenting model output and fostering trust in AI among clinicians. However, important ethical questions from the patient perspective remain unanswered. Specifically, it is unclear if patients are informed about or approve of their data being utilized for model building purposes. As patient engagement and shared decision-making continue to rise in importance, it is likely that AI output will also be presented to patients. PPD presents a complex bioethical case for studying patient-facing AI because, in pregnancy, the autonomy, harms, and benefits afforded to the perinatal patient, newborn/ fetus, and partner must be weighed simultaneously. Therefore, this supplement will focus on developing concrete guidance for creating and implementing patient-facing AI while upholding ethical principles to utilize patient data sensitively and equitably, using PPD as a use case. The specific aims are to: 1) triangulate perspectives for transparent, ethical, and equitable use of patient data in AI for PPD from diverse stakeholders through semi-structured interviews, and 2) evaluate knowledge, attitudes, and preferences related to the utilization of AI in PPD among women of child-bearing age via a nation-wide survey. To accomplish these aims, we will leverage our multidisciplinary team from the parent grant and include new investigators who have collective expertise in obstetrics, perinatal psychiatry, AI development, informatics, qualitative research, survey methods, and user-centered design. Given the explosion of bioethical questions related to AI but the lack of attention paid to patient-facing AI, this project fills an important gap in advancing bioethical AI research. This work will both inform future AI work in other mental health domains and be directly incorporated into Phase II development activities by Iris OB Health.

项目摘要产后抑郁症（PPD）是一种常见的，但如果早期发现可以治疗的疾病，但它也可以如果不治疗，对母亲和孩子都有有害影响。PPD的常规筛查是被认为是最佳做法，但由于时间和资源的限制，并不总是这样做。因此，治疗干预开始较晚，许多PPD病例未被发现全部人工智能（AI）模型可以弥合PPD识别差距，已被证明可以主动，准确地识别患有PPD风险升高的女性。虹膜OB 数字健康创业公司Health为PPD建立了一个预测AI模型。Iris团队开发一个界面，向患者和临床医生展示我们的AI模型，以促进共享关于降低风险的干预决策。通过这项工作，我们认识到我们需要更好地理解面向患者的人工智能的生物伦理学意义。生命伦理学研究人工智能专注于呈现模型输出，并在临床医生中培养对人工智能的信任。然而，在这方面，从病人的角度来看，重要的伦理问题仍然没有答案。具体来说就是不清楚患者是否被告知或批准其数据用于模型构建目的随着患者参与和共同决策的重要性不断上升， AI输出也可能呈现给患者。PPD提出了一个复杂的生物伦理研究面向患者的人工智能的案例，因为在怀孕期间，提供给围产期患者、新生儿/胎儿和伴侣的体重必须同时称重。因此，本补编将侧重于为创建和实施面向患者的人工智能，同时坚持利用患者数据的伦理原则敏感而公平地使用PPD作为用例。具体目标是：1）三角测量在人工智能中透明、道德和公平地使用患者数据的观点，不同的利益相关者通过半结构化访谈，和2）评估知识，态度，以及育龄妇女在PPD中使用AI的偏好，全国范围的调查。为了实现这些目标，我们将利用我们的多学科团队，家长补助金，包括新的研究人员谁拥有集体的专业知识，在产科，围产期精神病学，人工智能的发展，信息学，定性研究，调查方法，以用户为中心的设计考虑到与人工智能相关的生物伦理问题的爆发，关注面向患者的AI，该项目填补了推进生物伦理AI的重要空白 research.这项工作将为未来其他心理健康领域的人工智能工作提供信息， Iris OB Health直接将其纳入II期开发活动。