JMIR AI

Developing effective health care teams is critical to meet the rising complexity in patient care. However, optimizing team composition, interpersonal dynamics, and care processes in complex health care systems requires processing vast amounts of data that capture fluid interactions among professionals—a task that has been cumbersome, costly, and avoided by most organizations. Well-designed artificial intelligence (AI) tools can meaningfully advance the frontier of health care teamwork, but the application of AI in this regard has been lagging. To support this development, we outline the potential for AI to help optimize team composition, strengthen norms and relationships among professionals, and standardize team-based clinical care processes. These applications can improve the integration of health care teams. Given the importance of relevant data for realizing such advances, we describe the potential types and sources of data that can support AI development. Furthermore, we highlight enabling strategies, including data-sharing alliances and leadership engagement to address privacy, interoperability, and ethical considerations. We propose a sequenced roadmap for piloting these applications based on technological readiness and clinical feasibility, ensuring that human oversight remains central as AI tools are introduced into complex care environments.

Urinary tract infection (UTI) is a common emergency department (ED) presentation but can be challenging to diagnose; both overdiagnosis and underdiagnosis are common, and older adults may be at particular risk of misdiagnosis. Artificial intelligence (AI) shows promise in augmenting diagnosis, but performance across patient populations remains underexamined.

Large language models (LLMs) are increasingly used to generate patient-oriented medical information. In geriatrics, such information must balance accuracy, relevance, and safety, as older adults may be particularly susceptible to misleading or harmful advice. However, systematic evaluations of expert perceptions across multiple geriatric conditions remain limited.

The integration of Large Language and Vision Assistant models with food and nutrition data enables multimodal meal analysis and contextual dietary guidance. Despite this potential, the reliability and practical usefulness of such systems for supporting everyday dietary decision-making remain underexplored.

Repeated-measures datasets are common in biomechanics and digital health, where each participant contributes multiple correlated trials. If cross-validation (CV) ignores this structure, information can leak from training to test folds, inflating performance and undermining clinical credibility.

Despite its promising potential to transform medical care, particularly in the field of medical images, the integration of artificial intelligence (AI) into clinical practice remains a complex and multifaceted challenge. In real-world settings, AI tools may demonstrate limited clinical impact, suboptimal performance, and security vulnerabilities, and face regulatory constraints. This viewpoint explores how the principles of design thinking can provide a structured road map for AI implementation in radiology. By emphasizing user-centeredness, fostering multidisciplinary collaboration, and embedding iterative refinement, this approach offers practical guidance for identifying clinical and operational needs, selecting and validating appropriate solutions, and ensuring effective deployment with continuous improvement.

Generative artificial intelligence (AI) systems are increasingly used in health and community settings, yet empirical evidence on how they function within participatory, youth-led action frameworks remains limited. Large language models can provide structured feedback to support planning and critical reflection, and AI-based image transformation can generate realistic visual prototypes to enhance shared understanding. However, risks include output variability, feasibility gaps when AI-generated recommendations or visualizations imply solutions that are not operationally workable, and the potential to displace adolescent voice and agency if AI outputs are treated as authoritative rather than as inputs for collective deliberation.

The exponential growth of digital information has led to an unprecedented expansion in the volume of unstructured text data. Efficient classification of these data is critical for timely evidence synthesis and informed decision-making in health care. Machine learning techniques have shown considerable promise for text classification tasks. However, multiclass classification of papers by study publication type has been largely overlooked compared to binary or multilabel classification. Addressing this gap could significantly enhance knowledge translation workflows and support systematic review processes.

As Parkinson disease (PD) rates increase, so does interest in finding new technological solutions for PD management. Despite substantial efforts to explore potential applications of artificial intelligence (AI) in PD management, research from the perspectives of people with PD on AI remains limited.

Artificial intelligence (AI) integrated with point-of-care imaging is a promising approach to expand access in settings with limited specialist availability. However, no systematic review has comprehensively evaluated AI-assisted clinical decision support across multiple point-of-care imaging modalities, assessed explainability implementation, or quantified clinical impact evidence gaps.

Artificial intelligence (AI) offers new methods to improve diagnosis and treatment in mental health. However, its use raises legal and ethical concerns.

In recent years, there has been increasing interest in developing machine and deep learning models capable of annotating clinical documents with semantically relevant labels. However, the complex nature of these models often leads to significant challenges regarding interpretability and transparency.