JMIR Medical Informatics

Ambient AI technologies, commonly known as AI scribes, are transforming clinical practice by autonomously capturing patient-provider conversations and structuring them into clinical notes. Short-term studies suggest that ambient AI can significantly reduce documentation time and improve job satisfaction. However, as health care systems accelerate the adoption of these tools, the medical education community must seek to answer unresolved questions regarding their full impact on learners. The inevitable integration of ambient AI in teaching hospitals presents both transformative opportunities and significant challenges for medical education. This viewpoint discusses the potential benefits and risks of ambient AI in both undergraduate and graduate medical education and considers its impact on learning and clinical skills acquisition. Robust research studies are urgently needed to navigate this new frontier and ensure that the integration of ambient AI in the clinical learning environment ultimately enriches, rather than diminishes, the practice of medicine.

Perioperative computed tomography (CT) imaging is essential for detecting postoperative recurrence and metastasis in cancer patients. However, large-scale automated extraction of oncological outcomes from CT reports remains limited by the unstructured nature of report text and wide variability in reporting styles. Radiology reports frequently contain linguistic ambiguities, including negations, hedging, and expressions conveying diagnostic uncertainty (eg, “cannot exclude recurrence” or “possibly metastatic”). Manual review is labor-intensive and constrains consistent extraction at scale. The inability to systematically account for diagnostic uncertainty represents a major barrier to reliable automated surveillance systems.

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The Charlson Comorbidity Index (CCI) is widely used to quantify comorbidity burden in observational research. Applying it to real-world data requires accurate International Classification of Diseases–based mappings. The commonly used mapping does not reflect current German coding standards.

Personalized follow-up for type 2 diabetes may improve the alignment between monitoring intensity and patient needs, but operational approaches that jointly consider follow-up timing, modality, expected health benefits, and resource use remain limited.

Augmented reality (AR) has emerged as a promising tool to enhance surgical precision during robot-assisted partial nephrectomy (RAPN), particularly by enabling the intraoperative overlay of 3D anatomical models. However, real-time AR implementation requires robust image segmentation of anatomical structures, such as the kidney, which remains technically challenging in dynamic laparoscopic environments.

Following 2 decades of electronic medical record (EMR) adoption, most large public health systems hold comprehensive digital clinical data but lack the complementary informatics capability to return those data to clinicians, coders, and operational teams in a usable form. In South Australia (SA), the statewide Sunrise (Altera Digital Health) EMR system has digitized documentation and ordering since 2017; however, tools for back-end data extraction and enriched clinical information displays were not prioritized, and clinical, operational, and research users have reported ongoing difficulties accessing timely data.

Call abandonment is a critical barrier to patient access in health care call centers; yet, predictive modeling efforts are limited by strict privacy regulations that restrict the use of personal or behavioral data. Whether abandonment can be accurately predicted using only anonymized operational metrics remains unclear.

Population aging and geographic health disparities challenge health care delivery in rural settings worldwide. Point-of-care ultrasound (PoCUS) combined with teleconsultation may enhance home-based medical care by extending specialist expertise to underserved communities, yet evidence from real-world implementation in Asian settings remains scarce. Taiwan, with its high digital literacy and universal National Health Insurance (NHI) system, provides a unique context for evaluating integrated PoCUS-teleconsultation service delivery.

Ambient AI technologies are increasingly marketed as solutions to reduce clinician burden and improve care efficiency; however, real-world performance varies widely across clinical settings. Health care provider organizations face challenges in determining which aspects of ambient AI performance matter most and how to obtain meaningful information about those aspects from vendors or through internal evaluation. This article presents a shared mental model to guide health system leaders in conceptualizing ambient AI performance across 3 interdependent dimensions: technical, interface, and system level. For each dimension, we outline the types of information relevant to assessment; what vendors should reasonably be expected to provide; and how health care provider organizations can conduct their own evaluations to contextualize, verify, or supplement vendor claims. By integrating both vendor and health system perspectives, this work offers a grounded, practical structure to support organizations of all sizes in understanding and making informed decisions about ambient AI technologies.

The growing need for and interest in geomatics in the medical sector, as well as the pandemic crisis, led us to create a France-wide geomatics project aimed at producing several atlases of all-cause mortality at the municipal and submunicipal district levels via uMap France, a free and open-source collaborative map-sharing platform. In 2020, we decided to circumvent the obstacle of accessing detailed COVID-19 data by adopting a mortality-based approach to map the consequences of the crisis.

Health care systems face growing fiscal pressure while AI reaches clinical parity in several domains. UK National Health Service expenditure rose by 52%, while Australia's health expenditure grew by 29% between 2019 and 2023. Yet large-scale cost savings from AI remain limited, largely because implementation constraints continue to outweigh technical capability.