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Introduction: The emergence of handheld ultrasound devices capable of connecting to cell phones or tablets heralds a significant advancement in medical technology, particularly within the field of obstetrics. These devices offer the promise of immediate bedside ultrasound examinations, potentially revolutionizing patient care by enabling fetal assessments in diverse settings. Material and Methods: This prospective study aimed to validate the reliability of non‐piezo, chip‐based handheld ultrasound devices in clinical obstetric practice. Conducted in a university hospital obstetric ward, the study included 100 pregnant women between 17 and 41 weeks of gestation. Participants underwent ultrasound examinations using both conventional and portable point‐of‐care ultrasound (POCUS) devices to compare the accuracy in estimating fetal weight and other parameters, such as cardiac activity, fetal presentation, placental location, and amniotic fluid volume. The reliability and agreement between the devices were assessed using intraclass correlation coefficients, Bland–Altman plots, and Pearson correlation coefficients. Results: The results show a near‐perfect agreement (0.98) and correlation (r = 0.98, p < 0.001) for estimated fetal weight and most biometry measurements between the two types of ultrasound devices, with slight deviations in head circumference and amniotic fluid index measurements. Subgroup analysis revealed variations in agreement and correlation rates with higher BMI and advanced gestational age, indicating areas for further refinement. Conclusions: These findings affirm the high reliability of handheld ultrasound devices for basic obstetric ultrasound evaluations, supporting their integration into daily clinical practice. This technology improves the flexibility and immediacy of prenatal care, although further research is needed to optimize its application across patient populations and treatment settings. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has gained prominence in medical imaging, particularly in obstetrics and gynecology (OB/GYN), where ultrasound (US) is the preferred method. It is considered cost effective and easily accessible but is time consuming and hindered by the need for specialized training. To overcome these limitations, AI models have been proposed for automated plane acquisition, anatomical measurements, and pathology detection. This study aims to overview recent literature on AI applications in OB/GYN US imaging, highlighting their benefits and limitations. For the methodology, a systematic literature search was performed in the PubMed and Cochrane Library databases. Matching abstracts were screened based on the PICOS (Participants, Intervention or Exposure, Comparison, Outcome, Study type) scheme. Articles with full text copies were distributed to the sections of OB/GYN and their research topics. As a result, this review includes 189 articles published from 1994 to 2023. Among these, 148 focus on obstetrics and 41 on gynecology. AI-assisted US applications span fetal biometry, echocardiography, or neurosonography, as well as the identification of adnexal and breast masses, and assessment of the endometrium and pelvic floor. To conclude, the applications for AI-assisted US in OB/GYN are abundant, especially in the subspecialty of obstetrics. However, while most studies focus on common application fields such as fetal biometry, this review outlines emerging and still experimental fields to promote further research. [ABSTRACT FROM AUTHOR]