Clinical obstacles to machine-learning POCUS adoption and system-wide AI implementation (The COMPASS-AI survey)

Point-of-care ultrasound (POCUS) has become indispensable in various medical specialties. The integration of artificial intelligence (AI) and machine learning (ML) holds significant promise to enhance POCUS capabilities further. However, a comprehensive understanding of healthcare professionals' perspectives on this integration is lacking.

This study aimed to investigate the global perceptions, familiarity, and adoption of AI in POCUS among healthcare professionals.

An international, web-based survey was conducted among healthcare professionals involved in POCUS. The survey instrument included sections on demographics, familiarity with AI, perceived utility, barriers (technological, training, trust, workflow, legal/ethical), and overall perceptions regarding AI-assisted POCUS. The data was analysed by descriptive statistics, frequency distributions, and group comparisons (using chi-square/Fisher's exact test and t-test/Mann-Whitney U test).

This study surveyed 1154 healthcare professionals on perceived barriers to implementing AI in point-of-care ultrasound. Despite general enthusiasm, with 81.1% of respondents expressing agreement or strong agreement, significant barriers were identified. The most frequently cited single greatest barriers were Training & Education (27.1%) and Clinical Validation & Evidence (17.5%). Analysis also revealed that perceptions of specific barriers vary significantly based on demographic factors, including region of practice, medical specialty, and years of healthcare experience.

This novel global survey provides critical insights into the perceptions and adoption of AI in POCUS. Findings highlight considerable enthusiasm alongside crucial challenges, primarily concerning training, validation, guidelines, and support. Addressing these barriers is essential for the responsible and effective implementation of AI in POCUS.