Medical AI, including clinical language models, vision-language models, and multimodal health record models, already summarizes notes, answers questions, and supports decisions. Their adaptation to new populations, specialties, or care settings often relies on fine-tuning, prompting, or retrieval from external knowledge bases. These strategies can scale poorly and risk contextual errors: outputs that appear plausible but miss critical patient or situational information. We envision context switching as a solution. Context switching adjusts model reasoning at inference without retraining. Generative models can tailor outputs to patient biology, care setting, or disease. Multimodal models can reason on notes, laboratory results, imaging, and genomics, even when some data are missing or delayed. Agent models can coordinate tools and roles based on tasks and users. In each case, context switching enables medical AI to adapt across specialties, populations, and geographies. It requires advances in data design, model architectures, and evaluation frameworks, and establishes a foundation for medical AI that scales to infinitely many contexts while remaining reliable and suited to real-world care.

Frontier artificial intelligence (AI) models, such as OpenAI's GPT-5 and Meta's DINOv3, have advanced rapidly through training on internet-scale public data, yet such systems lack access to private clinical data. Neuroimaging, in particular, is underrepresented in the public domain due to identifiable facial features within MRI and CT scans, fundamentally restricting model performance in clinical medicine. Here, we show that frontier models underperform on neuroimaging tasks and that learning directly from uncurated data generated during routine clinical care at health systems, a paradigm we call health system learning, yields high-performance, generalist neuroimaging models. We introduce NeuroVFM, a visual foundation model trained on 5.24 million clinical MRI and CT volumes using a scalable volumetric joint-embedding predictive architecture. NeuroVFM learns comprehensive representations of brain anatomy and pathology, achieving state-of-the-art performance across multiple clinical tasks, including radiologic diagnosis and report generation. The model exhibits emergent neuroanatomic understanding and interpretable visual grounding of diagnostic findings. When paired with open-source language models through lightweight visual instruction tuning, NeuroVFM generates radiology reports that surpass frontier models in accuracy, clinical triage, and expert preference. Through clinically grounded visual understanding, NeuroVFM reduces hallucinated findings and critical errors, offering safer clinical decision support. These results establish health system learning as a paradigm for building generalist medical AI and provide a scalable framework for clinical foundation models.

Hospitals and healthcare systems rely on operational decisions that determine patient flow, cost, and quality of care. Despite strong performance on medical knowledge and conversational benchmarks, foundation models trained on general text may lack the specialized knowledge required for these operational decisions. We introduce Lang1, a family of models (100M-7B parameters) pretrained on a specialized corpus blending 80B clinical tokens from NYU Langone Health's EHRs and 627B tokens from the internet. To rigorously evaluate Lang1 in real-world settings, we developed the REalistic Medical Evaluation (ReMedE), a benchmark derived from 668,331 EHR notes that evaluates five critical tasks: 30-day readmission prediction, 30-day mortality prediction, length of stay, comorbidity coding, and predicting insurance claims denial. In zero-shot settings, both general-purpose and specialized models underperform on four of five tasks (36.6%-71.7% AUROC), with mortality prediction being an exception. After finetuning, Lang1-1B outperforms finetuned generalist models up to 70x larger and zero-shot models up to 671x larger, improving AUROC by 3.64%-6.75% and 1.66%-23.66% respectively. We also observed cross-task scaling with joint finetuning on multiple tasks leading to improvement on other tasks. Lang1-1B effectively transfers to out-of-distribution settings, including other clinical tasks and an external health system. Our findings suggest that predictive capabilities for hospital operations require explicit supervised finetuning, and that this finetuning process is made more efficient by in-domain pretraining on EHR. Our findings support the emerging view that specialized LLMs can compete with generalist models in specialized tasks, and show that effective healthcare systems AI requires the combination of in-domain pretraining, supervised finetuning, and real-world evaluation beyond proxy benchmarks.

Abstract--The United States spends more on healthcare than any other nation - nearly 17% of GDP as of the early 2020s - yet struggles with uneven access and outcomes [1] [2]. This paradox of high cost, variable quality, and inequitable access is often described by the "Iron Triangle" of healthcare [3], which posits that improvements in one dimension (access, cost, or quality) often come at the expense of the others. This paper explores how an Intelligent Healthcare Ecosystem (iHE) - an integrated system leveraging advanced technologies and data-driven innovation - can "bend" or even break this iron triangle, enabling simultaneous enhancements in access, cost-efficiency, and quality of care. We review historical and current trends in U.S. healthcare spending, including persistent waste and international comparisons, to underscore the need for transformative change. We then propose a conceptual model and strategic framework for iHE, incorporating emerging technologies such as generative AI and large language models (LLMs), federated learning, interoperability standards (FHIR) and nationwide networks (TEFCA), and digital twins. We introduce an updated healthcare value equation that integrates all three corners of the iron triangle, and we hypothesize that an intelligently coordinated ecosystem can maximize this value by delivering high-quality care to more people at lower cost. Methods include a narrative synthesis of recent literature and policy reports, and Results highlight key components and enabling technologies of an iHE. We discuss how such ecosystems can reduce waste, personalize care, enhance interoperability, and support value-based models, all while addressing challenges like privacy, bias, and stakeholder adoption. The paper is formatted per MDPI guidelines, with APA-style numbered references, illustrative figures (U.S. spending trends, waste breakdown, international spending comparison, conceptual models), equations, and a structured layout. Our findings suggest that embracing an Intelligent Healthcare Ecosystem is pivotal for optimizing the long-standing trade-offs in healthcare's iron triangle, moving towards a system that is more accessible, affordable, and of higher quality for all.

Modern computer systems often rely on syslog, a simple, universal protocol that records every critical event across heterogeneous infrastructure. However, healthcare's rapidly growing clinical AI stack has no equivalent. As hospitals rush to pilot large language models and other AI-based clinical decision support tools, we still lack a standard way to record how, when, by whom, and for whom these AI models are used. Without that transparency and visibility, it is challenging to measure real-world performance and outcomes, detect adverse events, or correct bias or dataset drift. In the spirit of syslog, we introduce MedLog, a protocol for event-level logging of clinical AI. Any time an AI model is invoked to interact with a human, interface with another algorithm, or act independently, a MedLog record is created. This record consists of nine core fields: header, model, user, target, inputs, artifacts, outputs, outcomes, and feedback, providing a structured and consistent record of model activity. To encourage early adoption, especially in low-resource settings, and minimize the data footprint, MedLog supports risk-based sampling, lifecycle-aware retention policies, and write-behind caching; detailed traces for complex, agentic, or multi-stage workflows can also be captured under MedLog. MedLog can catalyze the development of new databases and software to store and analyze MedLog records. Realizing this vision would enable continuous surveillance, auditing, and iterative improvement of medical AI, laying the foundation for a new form of digital epidemiology.

Researchers have developed AI tools that can help detect brain abnormalities. Researchers have developed AI tools that can help detect brain abnormalities. 'Impressive for a robot': home care chatbots among AI tools being embraced by Australia's health system From GPs using the technology to record consultations to AI'detectives' finding brain lesions on scans, experts say it's only the beginning Fri 3 Oct 2025 11.00 EDTLast modified on Fri 3 Oct 2025 11.02 EDT Peta Rolls came to expect Aida's call at 10am each morning. A daily check-in call from an AI voice bot was not part of the service Rolls expected when she signed up for St Vincent's home care but when they asked her to be part of the trial four months ago, the 79-year-old said yes because she wanted to help. Although, truth be told, her expectations were low. Nevertheless, when she got the call, she says: "I was so overtaken by how responsive she was.

Hospitals and health systems are looking at AI-enabled solutions that target their most urgent pain points: staffing shortages, clinician burnout, rising costs, and patient bottlenecks. These operational realities keep leadership up at night, and AI solutions must directly address them. For instance, hospitals and health systems are eager for AI tools that can reduce documentation burden for physicians and nurses. Natural language processing (NLP) solutions that auto-generate clinical notes or streamline coding to free up time for direct patient care are far more compelling pitches than generic efficiency gains. Similarly, predictive analytics that help optimize staffing levels or manage patient flows can directly address operational workflow and improve throughput.

Beirut, Lebanon – By the time Zainab Dhaher and her family fled their southern Lebanese village last September, Israeli shelling had become relentless. They packed what they could and drove 13 hours to Beirut, only to find themselves once again within range of Israeli bombardment. The cycle of displacement repeated. I didn't have time to pack clothes for my children," the 34-year-old mother of two recalls, her voice cracking during a phone interview. "We moved from place to place, and no one helped us. Months after a United States-brokered ceasefire took effect in November, the fear still lingers.

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications-focused on supply chain, patient management, and capacity building, among other use cases-can improve the health system and public health performance. We present an Artificial Intelligence and Reinforcement Learning platform that allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices and send personalized recommendations based on past data and predictions can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be more decisive, is discussed specifically. This framework is, however, similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Generative artificial intelligence (GenAI) has the potential to improve healthcare through automation that enhances the quality and safety of patient care. Powered by foundation models that have been pretrained and can generate complex content, GenAI represents a paradigm shift away from the more traditional focus on task-specific classifiers that have dominated the AI landscape thus far. We posit that the imminent application of GenAI in healthcare will be through well-defined, low risk, high value, and narrow applications that automate healthcare workflows at the point of care using smaller foundation models. These models will be finetuned for different capabilities and application specific scenarios and will have the ability to provide medical explanations, reference evidence within a retrieval augmented framework and utilizing external tools. We contrast this with a general, all-purpose AI model for end-to-end clinical decision making that improves clinician performance, including safety-critical diagnostic tasks, which will require greater research prior to implementation. We consider areas where 'human in the loop' Generative AI can improve healthcare quality and safety by automating mundane tasks. Using the principles of implementation science will be critical for integrating 'end to end' GenAI systems that will be accepted by healthcare teams.