Recent advances in large language models (LLMs) have demonstrated notable performance in medical licensing exams. However, comprehensive evaluation of LLMs across various healthcare roles, particularly in high-stakes clinical scenarios, remains a challenge. Existing benchmarks are typically text-based, English-centric, and focus primarily on medicines, which limits their ability to assess broader healthcare knowledge and multimodal reasoning. To address these gaps, we introduce KokushiMD-10, the first multimodal benchmark constructed from ten Japanese national healthcare licensing exams. This benchmark spans multiple fields, including Medicine, Dentistry, Nursing, Pharmacy, and allied health professions. It contains over 11588 real exam questions, incorporating clinical images and expert-annotated rationales to evaluate both textual and visual reasoning. We benchmark over 30 state-of-the-art LLMs, including GPT-4o, Claude 3.5, and Gemini, across both text and image-based settings. Despite promising results, no model consistently meets passing thresholds across domains, highlighting the ongoing challenges in medical AI. KokushiMD-10 provides a comprehensive and linguistically grounded resource for evaluating and advancing reasoning-centric medical AI across multilingual and multimodal clinical tasks.

When I interviewed a nurse practitioner in California about what she cherished most about nursing, it was the "human element" of being present with others. "I think we all just want acknowledgment of our suffering, even if you can't cure it or do anything about it," she told me. She still remembered when a homeless man came into her clinic, his back hunched, feet gnarled and callused from being on the streets for years, and she "just sat and did wound care for his feet". The moment stood out for her, in part because the opportunity to take that kind of time is getting rarer in clinics and hospitals as drives for efficiency impose time constraints. Washing his feet captured what nursing was about for her: the humility, the service, the witnessing.

This paper explores the application of large language models (LLMs) in nursing and elderly care, focusing on AI-driven patient monitoring and interaction. We introduce a novel Chinese nursing dataset and implement incremental pre-training (IPT) and supervised fine-tuning (SFT) techniques to enhance LLM performance in specialized tasks. Using LangChain, we develop a dynamic nursing assistant capable of real-time care and personalized interventions. Experimental results demonstrate significant improvements, paving the way for AI-driven solutions to meet the growing demands of healthcare in aging populations.

At many American hospitals, bedside monitors that measure everything from patients' intracranial pressure to heart, respiration and blood pressure rates feed numbers into artificial intelligence systems that constantly assess their risk of suffering things like stroke, sepsis and heart attack. But because the algorithms that feed such predictive health care AI systems are often based on data from homogenous populations, AI that's meant to improve care for everyone sometimes falls short – and may even prove harmful. Supported by a new $5.9 million National Institutes of Health grant, two University of Virginia researchers will explore ways to improve the use of artificial intelligence in health care for a wider diversity of patient populations. Ishan Williams, an associate professor in the School of Nursing, and Randall Moorman, a UVA Health cardiologist, will lead the multi-institutional research effort. Primary co-investigators Ishan Williams, an associate professor in the School of Nursing, and Randall Moorman, a UVA Health cardiologist, will develop, test and deploy best practices for artificial intelligence health care systems that aggregate data from a more diverse pool of patients by taking into account their race, ethnicity, socio-economic status and geography.

In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions. "AI may more accurately predict fall risk without manual calculation and provide automatic warning systems," said Prof. Ramesh and Dr Indira. They have also highlighted the role of mobile health technologies (smartphones, smartphone apps, and wearable technologies) to help manage chronic illnesses by receiving and sending data directly between patients and care-providers, creating a comprehensive picture of the dynamic state of a patient's health in their everyday environments. According to the duo, sensor-based technologies, when placed in the home or hospital environment and used in combination, help nurses compose text and multimedia messages (for sharing photos and videos), measure body movement and collect weight, movement, and environmental (temperature, light, sound, air quality) data. In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions.

Nellore: The conference on Futuristic Nursing being held at Narayana Nursing College here has discussed at length aspects of patient safety as also use of artificial intelligence and tele-medicine, apart from mobile health and sensor-based technologies (smartphones, smartphone apps and wearable technologies). More than 800 nurses are participating in the meet and around 40 eminent nursing leaders across the globe discussing the latest in nursing practices during the 3-day conference from Saturday. In a paper on'Artificial Intelligence in Nursing' presented jointly by Dr Ramesh M.Sc Phd, HoD Medical Surgical Nursing, St Paul's Hospital Millennium Medical College, Ethiopia, and Dr S. Indira, Dean of Narayana Nursing College, said AI offers three advantages over traditional methods -- the ability to quickly consider large volumes of data in risk prediction, increased intervention specificity (accurately flagging patients most at-risk) and automated adjustments in variable selection and calculation. "AI can help detect which patient features are most important in public health applications, allowing for more focused preventive interventions. Robots may aid nursing care tasks in hazardous clinical environments and they have the potential to automate some tasks."

As technology and automation rapidly remake a very different future of work, some economists predict that women will benefit the most from the coming disruptions. Although women have no doubt been hardest hit by the COVID-19 economy, in the coming years, women-dominated caring jobs--like nursing, teaching, and providing child and elder care--that aren't easily replaced by machines will be among the fastest-growing occupations and thus more likely to be "future-proof." It's not that many women's jobs won't be automated away. Just as men-dominated mechanical and machine operating jobs are predicted to disappear, so too are women-dominated administrative and clerical jobs. But most of these future-of-work predictions assume women will continue to dominate the care economy. And all because men aren't expected to care.

Much like other aspects of health care, nursing has become increasingly saturated with technology over the past several decades. Existing technolog...

As often happens with new developments in health IT, there's been no shortage of hype related to the emergence of AI and the changes it can bring. But as often also happens, much of the success of AI will depend on the nurses who care for patients from hour to hour and day to day. Writing recently at DailyNurse, Catherine Burger, a board-certified executive nurse leader, points out that "(b)edside nurses are the key to AI as it relates to predictability models and telemedicine. Data points such as temperature, blood pressure, and physical assessment values, entered into the EHR in a timely manner, can literally make the difference between life and death as the health information technology is scanning thousands of factors to provide outcome information. Getting nurses onboard with real-time, accurate documentation (not just copying the assessment from the previous shift) is essential."

Nurses have always played a critical role at the bedside while bearing witness to numerous changes in technology. In the past 50 years alone, the advancements seem unfathomable to nurses of the not-so-distant past such as "test-tube" babies, medical lasers, the artificial heart, genome mapping, CT and MRI imaging, angioplasty, dialysis, endoscopic procedures, bionic prosthetics, the internet and health information technology (IT), the electronic health record (EHR), and robotic surgeries. However, as health care races toward telemedicine and artificial intelligence, nurses must strategically position themselves to stay relevant. In a recent article in Nursing Management, the author stated: "Artificial Intelligence (AI) is a branch of computer science dealing with the simulation of intelligent behavior in computers. Combining the experience, knowledge, and human touch of clinicians with the power of AI will improve the quality of patient care and lower its cost."