JMIR AI

Mental health issues continue to increase worldwide, often intensified by stigma and lack of awareness. Social media has become a major space where individuals articulate their emotional and psychological experiences. However, little is known about how AI models interpret these narratives, particularly when contextual reasoning and alignment with human judgment are required.

Postcare instructions play a vital role in preventive health care. Traditionally, information on post–dental procedure care is provided through printed leaflets or brief verbal explanations, which often fail to engage patients or ensure adherence. In contrast, emerging artificial intelligence (AI) systems designed to emulate human interaction represent a promising yet understudied approach to improving patient communication and understanding.

Accurate reporting in nuclear medicine is essential for clinical decision-making. Trainees often generate preliminary reports with variable quality, and artificial intelligence (AI) tools such as ChatGPT-4o may enhance report clarity and accuracy, particularly in the impression section of the report.

Age-related cognitive dysfunction, including mild cognitive impairment and dementia, underscores the need for scalable and personalized predictive models. We present a conceptual artificial intelligence-driven digital twin framework to support early detection, real-time monitoring, and adaptive intervention. The system is structured around 4 core processes: perception, analytics, decision-making, and adaptive feedback, and is organized across 5 functional layers: data acquisition, integration, modeling, reasoning, and application. Multimodal behavioral, physiological, and clinical data are harmonized using Fast Healthcare Interoperability Resources and Observational Medical Outcomes Partnership standards. Predictive modeling uses convolutional and recurrent neural networks, gradient boosting, and reinforcement learning. The framework is designed for cloud-based deployment on platforms that support HIPAA-aligned implementation, including Amazon Web Services and Microsoft Azure, with 7 application modules spanning signal-based and pose-based assessment, personalized mind-body training, cognitive rehabilitation, and disease trajectory simulation. This architecture offers a foundation for precision cognitive care in aging populations.

A discharge summary should be a clinical report that documents a patient’s hospital stay, including test results, diagnoses, management, and follow-up. Currently, discharge summaries are written by clinicians who manually locate pertinent information across the electronic health record, of which approximately 80% is free text. This process is time-consuming and may be suitable for automation using large language models.

Screening for type 2 diabetes (T2D) is not optimal, leading to a large number of patients being undiagnosed. Recently, deep learning (DL) applied to chest radiographs (CXRs) has shown promise for opportunistic T2D prediction. A prior study in a predominantly suburban non-Hispanic White cohort achieved an area under the curve (AUC) of 0.84 for prevalence. In this study, we evaluate the performance and generalizability of this DL model in an urban cohort with greater racial diversity, higher social deprivation, and higher T2D prevalence. We further assess whether integrating DL predictions with BMI and demographic variables improves T2D prediction beyond demographics and BMI alone.

Clinician burnout has reached crisis levels in emergency medicine, with clinical documentation burden identified as a central contributing factor. Ambient artificial intelligence (AI) scribes offer a promising approach to reduce this burden, but objective evidence in the emergency department (ED) setting remains limited, and prior reports have been constrained by short observation windows and low adoption.

Millions of people now use leading generative artificial intelligence (AI) tools (chatbots) for psychological support. Despite the promise related to availability and scale, the single most pressing question in AI for mental health is whether these tools are safe. The field currently lacks a validated, automated benchmark for determining AI chatbot safety in mental health, including for users at risk of suicide. The Validation of Ethical and Responsible AI in Mental Health (VERA-MH) evaluation was recently proposed to meet this urgent need.

Radiology trainees require efficient, accurate, and accessible resources to master complex imaging techniques and identify findings that guide clinical decision-making. Large language models (LLMs) are emerging as promising tools for medical education and clinical workflows, offering the potential to enhance learning by providing instant feedback, aiding in diagnostic accuracy, and offering personalized learning experiences. However, systematic comparisons of LLMs for radiology education and clinical support remain limited, particularly regarding differences across subspecialties and resident experience levels.

The use of generative artificial intelligence (AI) by pharmaceutical companies and other organizations for preparing patient-facing documents reporting results of clinical research is becoming more common. This raises concerns about whether the accuracy and quality of these documents could be affected, as well as the potential impact on patient perceptions and trust. Accurate and trustworthy information is critical to health care decision-making. Little is known about patient perceptions of AI-generated content.

Adverse drug events (ADEs) remain a critical safety issue in pharmaceutical research and development (Pharma R&D), necessitating robust methods for early detection and surveillance. Language models (LMs) are increasingly used in ADE analysis, addressing safety challenges during drug development and postmarket surveillance. Language modeling approaches, ranging from static embeddings to large language models (LLMs), capitalize on diverse data sources, such as clinical trial datasets, electronic health records, and social media posts, to predict ADEs, analyze real-world evidence, and improve drug screening and pharmacovigilance systems.

Automated systematic literature review (SLR) may reduce the workload and errors associated with manual review, enabling faster, up-to-date reviews even with increasing publication volumes. Large language models (LLMs) have demonstrated strong capabilities in understanding unstructured languages. However, few studies have explored the potential of a comprehensive LLM platform to streamline the entire SLR process from article screening to data extraction.