Della Ripa Sara, Santos Nicole, Walker Dilys
Institute for Global Health Sciences, University of California San Francisco, San Francisco, CA, USA.
Department of Obstetrics, Gynecology and Reproductive Sciences, University of California San Francisco, San Francisco, CA, USA.
BMC Pregnancy Childbirth. 2025 Jul 4;25(1):729. doi: 10.1186/s12884-025-07796-6.
In many low- and middle-income countries (LMICs), widespread access to obstetric ultrasound is challenged by lack of trained providers, workload, and inadequate resources required for sustainability. Artificial intelligence (AI) is a powerful tool for automating image acquisition and interpretation and may help overcome these barriers. This study explored stakeholders' opinions about how AI-enabled point-of-care ultrasound (POCUS) might change current antenatal care (ANC) services in LMICs and identified key considerations for introduction.
We purposely sampled midwives, doctors, researchers, and implementors for this mixed methods study, with a focus on those who live or work in African LMICs. Individuals completed an anonymous web-based survey, then participated in an interview or focus group. Among the 41 participants, we captured demographics, experience with and perceptions of standard POCUS, and reactions to an AI-enabled POCUS prototype description. Qualitative data were analyzed by thematic content analysis and quantitative Likert and rank-order data were aggregated as frequencies; the latter was presented alongside illustrative quotes to highlight overall versus nuanced perceptions.
The following themes emerged: (1) priority AI capabilities; (2) potential impact on ANC quality, services and clinical outcomes; (3) health system integration considerations; and (4) research priorities. First, AI-enabled POCUS elicited concerns around algorithmic accuracy and compromised clinical acumen due to over-reliance on AI, but an interest in gestational age automation. Second, there was overall agreement that both standard and AI-enabled POCUS could improve ANC attendance (75%, 65%, respectively), provider-client trust (82%, 60%), and providers' confidence in clinical decision-making (85%, 70%). AI consistently elicited more uncertainty among respondents. Third, health system considerations emerged including task sharing with midwives, ultrasound training delivery and curricular content, and policy-related issues such as data security and liability risks. For both standard and AI-enabled POCUS, clinical decision support and referral strengthening were deemed necessary to improve outcomes. Lastly, ranked priority research areas included algorithm accuracy across diverse populations and impact on ANC performance indicators; mortality indicators were less prioritized.
Optimism that AI-enabled POCUS can increase access in settings with limited personnel and resources is coupled with expressions of caution and potential risks that warrant careful consideration and exploration.
在许多低收入和中等收入国家（LMICs），由于缺乏训练有素的医疗人员、工作量大以及可持续性所需资源不足，产科超声的广泛普及面临挑战。人工智能（AI）是一种用于自动化图像采集和解读的强大工具，可能有助于克服这些障碍。本研究探讨了利益相关者对人工智能即时超声（POCUS）如何改变LMICs当前产前护理（ANC）服务的看法，并确定了引入该技术的关键考虑因素。
我们为这项混合方法研究特意抽取了助产士、医生、研究人员和实施人员作为样本，重点关注那些在非洲LMICs生活或工作的人员。参与者先完成一份匿名的基于网络的调查问卷，然后参加访谈或焦点小组讨论。在41名参与者中，我们收集了人口统计学信息、对标准POCUS的经验和看法，以及对人工智能POCUS原型描述的反应。定性数据通过主题内容分析进行分析，定量的李克特量表和排序数据汇总为频率；后者与说明性引语一起呈现，以突出总体看法与细微差别。
出现了以下主题：（1）人工智能的优先功能；（2）对ANC质量、服务和临床结果的潜在影响；（3）卫生系统整合考虑因素；（4）研究重点。首先，人工智能POCUS引发了对算法准确性以及因过度依赖人工智能而导致临床敏锐度受损的担忧，但对孕周自动化存在兴趣。其次，总体上人们一致认为，标准POCUS和人工智能POCUS都可以提高ANC的就诊率（分别为75%和65%）、医患信任（分别为82%和60%）以及医疗人员对临床决策的信心（分别为85%和70%）。人工智能在受访者中始终引发更多的不确定性。第三，出现了卫生系统方面的考虑因素，包括与助产士的任务分担、超声培训的提供和课程内容，以及数据安全和责任风险等政策相关问题。对于标准POCUS和人工智能POCUS，临床决策支持和转诊强化都被认为是改善结果所必需的。最后，排名靠前的优先研究领域包括不同人群的算法准确性以及对ANC绩效指标的影响；死亡率指标的优先级较低。
人们对人工智能POCUS能够在人员和资源有限的环境中增加可及性感到乐观，但也表达了谨慎态度和潜在风险，这些都值得仔细考虑和探索。
