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Chen et al. demonstrate that large language models (LLMs) frequently prioritize agreement over accuracy when responding to illogical medical prompts, a behavior known as sycophancy. By reinforcing user assumptions, this tendency may amplify misinformation and bias in clinical contexts. The authors find that simple prompting strategies and LLM fine-tuning can markedly reduce sycophancy without impairing performance, highlighting a path toward safer, more trustworthy applications of LLMs in medicine.

In “A Randomized Controlled Trial of Mobile Intervention Using Health Support Bubbles to Prevent Social Frailty”, Hayashi et al. investigated the effects of using a mobile health app with family or individually. Greater improvements in social behavior and frailty were noted in participants who used the app with family. In an era of remote healthcare and app-based health interventions, Hayashi et al.’s study reminds of the importance of human connection.

Integrating large language models (LLMs) into oncology holds promise for clinical decision support. Woollie is an LLM recently developed by Zhu et al., fine-tuned using radiology impression notes from Memorial Sloan Kettering Cancer Center and externally validated on UCSF oncology datasets. This methodology prioritizes data accuracy, preempts catastrophic forgetting, and demonstrates unparalleled rigor in predicting the progression of various cancer types. This work establishes a foundation for reliable, scalable, and equitable applications of LLMs in oncology.

Wu et al.’s recent article, “Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features,” documents significant differences in retinal vascular features among women who develop preeclampsia and those with normotensive pregnancies. These findings provide evidence that retinal screening has the potential to be used as a low-cost, non-invasive screening strategy to support the earlier detection, prevention, and treatment of preeclampsia.

Winter et al.’s recent investigation, “A Comparison of Self-Reported COVID-19 Symptoms Between Android and iOS CoronaCheck App Users,” reveals differences in the demographics and COVID-19 symptoms reported by users of Android and iOS systems. These findings not only provide more information about the varied experiences of individuals during the COVID-19 pandemic but also suggest that conclusions reached in studies using one operating platform may not be generalizable to users of other platforms.

Deep vein thrombosis (DVT) causes significant morbidity/mortality and timely diagnosis often via ultrasound is critical. However, the shortage of trained ultrasound providers has been an ongoing challenge. Recently, Speranza and colleagues (2025) demonstrated that an artificial intelligence (AI) guided ultrasound system used by non-ultrasound-trained nurses with remote clinician review can achieve sensitivities of 90–98% and specificities of 74–100% for diagnosing DVT. This study highlights the potential for AI guided imaging to address important gaps in health care delivery.

Digital health tools have the potential to support patients in managing their chronic diseases. Recently, Ullrich and colleagues (2025) introduced PreventiPlaque, a mobile health application that provides patients with up-to-date ultrasound images of their carotid plaques and tracks their lifestyle habits. Through a randomized controlled trial, the authors provide evidence of PreventiPlaque’s efficacy in improving patients’ cardiovascular risk profiles. This study highlights the potential for digital health interventions to provide personalized health information to patients and empower them to take actionable steps to improve their cardiovascular health.

Cardiovascular disease is underdiagnosed and undertreated in women compared to men. Wearable technologies (wearables) help shed light on women’s cardiovascular by collecting continuous cardiovascular data and correlating it with hormonal fluctuations across the menstrual cycle. In this context, Jasinski et al. propose that the new metric, cardiovascular amplitude, enables non-invasive monitoring of female physiology and health across the menstrual cycle.

Alzheimer’s disease is the fifth-leading cause of death for adults over the age of 65. Retinal imaging has emerged to find more accurate diagnostic tool for Alzheimer’s Disease. This paper highlights Hao et al.’s development of a new deep learning tool, EyeAD, which studies Optical Coherence Tomography Angiography (OCT-A) of patients with Alzheimer’s. Integrating this model into clinical workflows may offer novel insights into the progression of this disease.

Liu et al.’s recent study reveals that telemedicine expanded access to cardiovascular care in China, enabling patients in poorer areas of the country to access care in cities with more resources. While these findings may support the global expansion of telemedicine, implementation often proves challenging. This article examines the potential and limitations of adopting similar telemedicine efforts within the U.S. to advance geographic health equity.

Qiao et al. recently investigated the ability of dual-energy X-ray absorptiometry (DXA) scans and a smartphone app to provide detailed body composition and shape data. In a healthcare system that continues to rely on crude and stigmatizing measurements like body-mass index (BMI), their findings point to the potential of newer technologies to capture markers (i.e., visceral adiposity and fat distribution patterns) that provide clearer insights into metabolic health.

Radin et al.’s recent study on patients with long COVID demonstrates that personal wearable data can provide critical insight into complex conditions. This editorial argues that research insights gained through personal wearables support the integration of personal wearables into healthcare. Challenges in incorporating wearable data in the clinic point towards AI data sorting, data sharing, device interoperability, FDA oversight, and expanded insurance coverage as first steps towards addressing these challenges.