In low-resource settings, access to timely emergency trauma care is often a matter of life and death. In Uganda, traffic law enforcement officers and medical trainees frequently serve as the initial first responders and frontline caregivers for road traffic injuries, particularly those involving motorcycles.

As artificial intelligence rapidly transforms clinical workflows, medical educators face a unique dilemma: How do we prepare trainees for an AI-driven future without undermining their core diagnostic skills?  A recent piece from the Yale School of Medicine caught our eye because it directly explores this double-edged sword. Written by Serena Crawford, Associate Communications Director at Yale School of Medicine, the Q&A highlights the pioneering work of Jaideep Talwalkar, MD, Associate Dean for Educational Technology and Innovation, as he examines how ambient AI scribes impact medical students.

Japan is the world's most rapidly aging society, with nearly 30% of its population aged 65 or older. Yet despite a 90% smartphone penetration rate, only 21.6% of Japanese adults regularly use mobile health (mHealth) apps.

Artificial intelligence is rapidly shifting from experimental code to point-of-care medical decision-making. As machine learning models gain autonomy in diagnosing diseases and tailoring treatments, ensuring true patient autonomy through informed consent has become a vital ethical challenge.

Parkinson disease (PD) is predominantly recognized for its motor symptoms, yet covert cognitive decline affects many patients long before dementia occurs. Traditional in-person cognitive testing presents substantial barriers: severe mobility impairments make traveling to clinics difficult, expert evaluations suffer from long waitlists, and screening tools like the Montreal Cognitive Assessment (MoCA) rely heavily on fine motor tasks like drawing, which can confound cognitive scores.

Virtual care expanded rapidly during the COVID-19 pandemic, but understanding which cancer patients use virtual oncology services, and how it connects to unplanned emergency department (ED) visits, remains critical for equitable care.

Every June, the scholarly publishing ecosystem undergoes a familiar ritual. Inboxes and industry feeds flood with identical, celebratory corporate press releases as Clarivate drops the latest Journal Citation Reports (JCR). But for publishing executives navigating a market defined by massive commercial consolidation, the real value of these metrics lies beneath the surface. The true strategic question isn't "Did our scores go up?" but rather, "What do these metrics tell us about the structural shift in where researchers want to publish?"

Postpartum hemorrhage (PPH) remains a leading cause of maternal morbidity worldwide, with blood transfusion rates hovering around 2% to 2.7%. While discussing these risks is a vital component of antenatal informed consent and shared decision-making, translating historical medical records into an accurate personal risk profile is notoriously difficult.

The rapid rise of generative artificial intelligence (GenAI) has created powerful new instructional opportunities alongside major pedagogical questions. While tools like ChatGPT can enhance lesson planning, case scenario design, and assessment development, many medical educators face a steep learning curve. Without structured guidance, integrating these fast-evolving and context-sensitive tools into high-stakes, discipline-specific medical curricula carries significant risks.

Artificial intelligence is fast becoming the digital front door of modern medicine. In the face of overwhelming patient demand, healthcare systems are rapidly deploying AI-enabled triage tools to sort symptoms, assess risk, and route patients before they ever step into a clinic.