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Generalist AI systems in healthcare can handle multiple complex clinical tasks, unlike narrow AI tools that perform isolated functions. However, current payment systems struggle to capture the value of these integrated capabilities. We examine potential solutions, including value-based and tiered structures, balancing innovation, equitable access, continuous performance evaluation, and cost-effectiveness to realize generalist AI’s transformative potential.

Wearable artificial intelligence (AI) technologies show promise in healthcare, with early applications demonstrating diverse benefits for patient safety. These systems go beyond traditional data collection, using advanced algorithms to provide real-time clinical guidance. From infectious disease monitoring to AI-powered surgical assistance, these technologies enable proactive, personalized care while addressing critical safety gaps. However, successful implementation requires careful consideration of technical, operational, and ethical challenges.

Healthcare communication faces unprecedented challenges as the healthcare workforce is increasingly faced with increased administrative burdens and reduced time with patients. Conversational agents powered by generative AI may offer a potential solution by collecting information, answering questions, documenting encounters, and supporting clinical decision-making through fluid, contextual dialogue. However, realizing their potential requires rigorous validation, careful implementation, and a strong commitment to safety, equity, and preserving human-centered care.

The rise of digital health technologies has provided individuals with unprecedented access to biometric data and health insights. However, excess monitoring may contribute to fatigue, anxiety, and information overload, sometimes reducing engagement and worsening outcomes. This article explores how artificial intelligence-enabled assistants might help address this challenge by filtering, contextualizing, and personalizing health information, potentially supporting informed self-management while mitigating some unintended harms of digital health technologies.

Enabled by the rapid rise in data collected by technologies, Digital Biomarkers (DBx) have emerged as a novel mechanism for assessment, diagnosis, and monitoring. However, the exponential growth and ability to generate new data has also raised questions about ways of ensuring the authenticity and accuracy of digital data. A recent study highlights how Large Language Models (LLMs) generating human-like content amplify these risks, and propose watermarking as a scalable solution to ensure data integrity. This article examines the potential of digital watermarking to help safeguard the reliability and provenance of DBx data, whilst also addressing broader challenges in health systems.

Cognitive bias accounts for a significant portion of preventable errors in healthcare, contributing to significant patient morbidity and mortality each year. As large language models (LLMs) are introduced into healthcare and clinical decision-making, these systems are at risk of inheriting – and even amplifying – these existing biases. This article explores both the cognitive biases impacting LLM-assisted medicine and the countervailing strengths these technologies bring to addressing these limitations.

The rapid increase in real-time health information collected from wearable devices has allowed digital biomarkers to emerge as a promising tool to support perioperative care, including surgical prehabilitation, intra-operative guidance, and post-operative monitoring. Important challenges include the accuracy of generated information, data security risks, and slow adoption of new technologies. Active stakeholder engagement and following existing digital biomarker development/implementation frameworks may support using this technology to improve surgical outcomes.

Cancer care generates vast quantities of data including clinical records, pathology images, radiology scans, and molecular profiles, yet these modalities are rarely integrated in a systematic, automated manner within routine clinical workflows, remaining largely siloed across separate departmental and technical systems. Foundation model-driven embeddings—or numerical representations (vectors) that summarize complex data such as text, images, and molecular profiles —offer a framework to integrate these data streams into unified patient representations. Here we examine the HONeYBEE platform’s approach to multimodal integration in oncology, situate it within broader developments in representation learning, and clinical and technical challenges that may shape its path to implementation 1 .

Oral ulcers are a very frequent complaint of patients reporting to dental professionals, of which traumatic ulcers are the most common They are very painful and troublesome while the patient speaks, masticates, or brushes. Various treatment modalities, such as topical analgesics and topical or systemic antibiotics, are used conventionally. However, long-term non-healing painful conditions and drug resistance have boosted the rapid raising of an alternative wound healing method. In the presented cases, low-dose biophotonics, also called photobiomodulation (PBM) therapy by low-level laser, was used with the aim of alleviating pain and inflammation, modulating the immune response, and promoting wound healing and tissue regeneration.

Background Antineutrophil cytoplasmic antibody (ANCA)–associated vasculitis (AAV) is a rare autoimmune disease characterized by small- and medium-vessel inflammation. Although chronic immune dysregulation and cytotoxic therapy exposure have been hypothesized to increase malignancy risk, large-scale, real-world evidence quantifying these risks has been limited. This study aimed to characterize hematologic and solid-organ malignancy risk profiles in patients with AAV using a large, multi-institutional database. Methods We conducted a retrospective cohort study using the TriNetX Research Network, using de-identified electronic medical record data from 2014 to 2024. We identified 19,238 patients with AAV and performed 1:1 propensity score matching to seborrheic keratosis controls on demographic and clinical covariates, yielding 18,255 matched pairs. Five-year incidences of hematologic and solid-organ malignancies were compared using hazard ratios (HR) and 95% confidence intervals (95% CI) from Cox proportional hazards regressions. Results Here we show that patients with AAV have a significantly increased risk of overall cancer (HR 1.23; 95% CI 1.12–1.35), driven by higher rates of hematologic malignancies (HR 2.12; 95% CI 1.77–2.53) and specific solid-organ cancers including lung (HR 1.78), bladder (HR 1.99), brain (HR 2.22), and lip/oral cavity/pharyngeal cancers (HR 1.78). Myelodysplastic syndromes (HR 4.11) and leukemia (HR 2.72) show the strongest associations. Negative controls show no risk elevation. Conclusions Patients with AAV face distinct and heightened risks for select hematologic and solid-organ malignancies, supporting the need for tailored cancer surveillance strategies. These findings provide a large-scale, real-world evidence base for malignancy risk stratification and proactive screening in AAV. Plain language summary This study explores whether people with ANCA-associated vasculitis (AAV), a rare autoimmune condition that causes inflammation of the blood vessels and damage across multiple organs, are more likely to develop cancer. Using a large database, we compared nearly 20,000 people with AAV to similar individuals without the disease. We found that people with AAV have higher chances of developing certain blood cancers and specific solid tumors, including those of the lung, bladder, brain, mouth, and throat. These findings highlight the need for more tailored cancer screening and early-detection strategies in patients with AAV, and further research to check the study’s findings. Mahajan et al. analyze cancer risk in a large cohort of antineutrophil cytoplasmic antibody–associated vasculitis patients. They find significantly elevated rates of hematologic and select solid-organ cancers, underscoring the need for tailored cancer surveillance in this population.