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.

项目概要产后抑郁症 (PPD) 是一种常见疾病，但如果及早发现，是可以治疗的，但它也可能如果不及时治疗，会对母亲和孩子产生有害影响。 PPD 的常规筛查是被认为是最佳实践，但由于时间和资源的限制，并没有始终如一地发生。因此，治疗干预措施启动较晚，许多产后抑郁症病例未被发现共。人工智能 (AI) 模型可以弥合 PPD 识别差距，并具有已被证明能够主动、准确地识别产后抑郁症风险较高的女性。虹膜OB Health 是一家数字健康初创公司，为 PPD 构建了预测人工智能模型。艾瑞斯团队是开发一个界面，向患者和临床医生展示我们的人工智能模型，以促进共享有关降低风险的干预措施的决策。通过这项工作，我们认识到需要更好地理解面向患者的人工智能的生物伦理影响。生物伦理学研究人工智能专注于呈现模型输出并培养临床医生对人工智能的信任。然而，从患者角度来看，重要的伦理问题仍未得到解答。具体来说，就是不清楚患者是否被告知或批准他们的数据被用于模型构建目的。随着患者参与和共同决策的重要性不断上升，人工智能输出很可能也会呈现给患者。 PPD 提出了复杂的生物伦理学研究面向患者的人工智能的案例，因为在怀孕期间，自主性、危害和好处提供给围产期患者、新生儿/胎儿和伴侣的体重必须同时进行。因此，本增刊将重点关注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道德原则敏感且公平地使用 PPD 作为用例。具体目标是：1）三角测量在 AI 中透明、合乎道德且公平地使用 PPD 患者数据的观点通过半结构化访谈来评估不同的利益相关者，以及 2) 评估知识、态度、以及与育龄妇女在 PPD 中使用人工智能相关的偏好全国范围内的调查。为了实现这些目标，我们将利用我们的多学科团队家长资助并包括具有产科集体专业知识的新研究人员，围产期精神病学、人工智能开发、信息学、定性研究、调查方法和以用户为中心的设计。鉴于与人工智能相关的生物伦理问题激增，但缺乏关注面向患者的人工智能，该项目填补了推进生物伦理人工智能的重要空白研究。这项工作将为其他心理健康领域的未来人工智能工作提供信息，并成为直接纳入 Iris OB Health 的 II 期开发活动。

项目成果

期刊论文数量（2）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Preparing for the bedside-optimizing a postpartum depression risk prediction model for clinical implementation in a health system.

为床边优化产后抑郁症风险预测模型做好准备，以便在卫生系统中进行临床实施。

DOI：
发表时间：
2024-03-26
期刊：
影响因子：
0
作者：
Liu, Yifan;Joly, Rochelle;Reading Turchioe, Meghan;Benda, Natalie;Hermann, Alison;Beecy, Ashley;Pathak, Jyotishman;Zhang, Yiye
通讯作者：
Zhang, Yiye

Recentering responsible and explainable artificial intelligence research on patients: implications in perinatal psychiatry.

近期对患者进行负责任且可解释的人工智能研究：对围产期精神病学的影响。