JMIR AI

Over half of US adults with Alzheimer disease and related dementias (ADRD) remain undiagnosed. Speech-based screening algorithms offer a scalable approach, but the relative value of large language model (LLM) adaptation strategies is unclear.

Fuzzy logic has been progressively investigated as a viable alternative to traditional statistical and machine learning methods in health care modeling, especially in environments marked by uncertainty, nonlinearity, and missing information. Although its use in prediction, classification, and risk stratification is well established, its application to explicit causal inference remains limited, varied, and methodologically premature.

The workload that stems from writing clinical histories is one of the main sources of stress and overload for primary care professionals, accounting for up to 43% of the working day. The introduction of technology, specifically artificial intelligence (AI), in the field of health could significantly reduce the time spent writing clinical reports without compromising the quality of care.

Despite the growing potential of large language models (LLMs) in mental health services, evidence on its capabilities in diagnostic processes remains limited.

Antimicrobial resistance (AMR) poses a critical global health threat, undermining the efficacy of antibiotics and complicating clinical decision-making. Although scientific literature on AMR is extensive, retrieving and synthesizing relevant evidence remains time-consuming for clinicians and researchers. Recent advances in large language models (LLMs) offer opportunities to enhance access to domain-specific knowledge. However, the diversity of available models, ranging from open-source to commercial, necessitates a systematic comparison of their performance, cost, and scalability in real-world biomedical applications.

evaluation (ISE) methods create a digital twin or a computer simulation of actual care pathways, enabling a broader assessment of the potential impact of algorithm-based clinical decision support systems (CDSS) before implementation. A programmatic search of several academic research databases showed at least 886 CDSS development and evaluation studies in the past 3 decades. However, fewer than 3% applied ISE to evaluate the potential impact on broader clinical care pathways.

The United States health insurance system is at a critical crossroads. Inflating costs, fragmented care, and administrative inefficiencies have revealed the limitations of the Fee-for-Service (FFS) model. This long-standing structure, while once effective in expanding access, now struggles to deliver efficiency and value. Value-based care (VBC) aims to realign incentives toward outcomes, quality, and efficiency. This article explores how artificial intelligence (AI) can serve as the digital backbone to accelerate the transition from FFS to VBC. The article reviews evidence from bundled payment programs and Accountable Care Organizations (ACOs), examines AI-driven frameworks for cost prediction, outcome measurement, and risk adjustment, and discusses associated challenges and future considerations using an illustrative case. Bundled payment models, such as the Comprehensive Care for Joint Replacement program, have shown average savings of approximately $1012 per episode; whereas, the ACO REACH model achieved average savings of roughly $930 per beneficiary, relative to FFS benchmarks. AI applications provide scalable solutions for forecasting costs, optimizing care coordination, and supporting preventive interventions. A case vignette in congestive heart failure illustrates how AI-enabled VBC can reduce episode costs by approximately 20% under favorable implementation conditions. AI has the potential to accelerate the scaling of VBC by enhancing its efficiency, equity, and sustainability. However, realizing this promise requires safeguards for data quality, interoperability, fairness, and transparency. In the AI era, the defining measure of health insurance will shift from the number of claims processed to the number of lives improved.

Mandibular structures offer resilient features for forensic identification where partial remains are available in postmortem condition. Deep learning applied to cephalometric radiographs offers an opportunity to predict demographic attributes, such as age and sex, which are critical in forensic and clinical contexts.

Recently, research into chatbots (also known as conversational agents, artificial intelligence agents, or voice assistants), which are computer applications using artificial intelligence to mimic human-like conversation, has grown sharply. Despite this growth, sociology lags behind other disciplines (including computer science, medicine, psychology, and communication) in publishing about chatbots. We suggest sociology can advance the understanding of human-chatbot interaction and offer 4 sociological theories to enhance extant work in this field. The first 2 theories (resource substitution theory and power-dependence theory) add new insights to existing models of the drivers of chatbot use, which overlook sociological concerns about how social structure (eg, systemic discrimination and the uneven distribution of resources within networks) inclines individuals to use chatbots, including problematic levels of emotional dependency on chatbots. The second 2 theories (affect control theory and fundamental cause of disease theory) help inform the development of chatbot-driven interventions that minimize safety risks by integrating a sociologically informed normative framework (eg, affective norms) into chatbot alignment and enhance equity by enhancing access to community resources (eg, opportunities for civic participation). We discuss how the theories advance theorizing about human-chatbot interaction and developing chatbots for social good, which are chatbots that provide scalable solutions to social and environmental challenges facing humanity while supporting human agency.

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Large language model (LLM)–based chatbots have rapidly emerged as tools for digital mental health (MH) counseling. However, evidence on their methodological quality, evaluation rigor, and ethical safeguards remains fragmented, limiting interpretation of clinical readiness and deployment safety.

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