JMIR AI

Relaxation techniques, such as the “safe place” imagery exercise, are simple and accessible strategies to cope with the negative effects of stress. While virtual reality (VR) has been applied in relaxation research, it is still unclear whether it enhances the relaxing effect of such exercises or is merely an alternative and similarly effective form of delivery.

Task-shifting can help close the mental health treatment gap in low- and middle-income countries, but its effectiveness depends on ongoing supervision, which is hard to scale. AI tools that process session recordings and generate structured fidelity feedback could offer a scalable alternative; yet, to our knowledge, none have been developed or validated for lay-delivered, multilingual, group-format interventions in low-resource settings.

Anxiety disorders are highly prevalent among adults with autism, with 20%‐65% experiencing at least one diagnosable anxiety disorder. While mindfulness-based interventions have demonstrated efficacy for anxiety reduction, treatment response varies considerably across individuals. Machine learning approaches offer potential for identifying who is most likely to benefit from smartphone-based mindfulness interventions, enabling personalized treatment recommendations.

OpenNotes allows patients to access their electronic health record (EHR) notes through online patient portals. However, EHR notes contain abundant medical jargon, which can be difficult for patients to comprehend. One way to improve comprehension is by reducing information overload and helping patients focus on the medical terms that matter most to them.

Acquiring generative AI (GenAI) literacy and avoiding knowledge collapse and inadequate skill development are critical for resident physicians in the GenAI era, who are highly susceptible to its influence, including those who are current nonusers.

The evaluation and improvement of medical large language models (LLMs) are critical for their real-world deployment, particularly in ensuring accuracy, safety, and ethical alignment. However, existing frameworks are inadequate for dissecting domain-specific error patterns or addressing cross-modal challenges.

Chatbots have recently emerged as an alternative approach for delivering cancer risk assessment and genetic counseling. Understanding the metrics used to describe the user-chatbot experience highlights the strengths and weaknesses of chatbot-assisted health care applications, ensuring safe and reliable medical care. While research supports chatbots in cancer genetic risk assessment and counseling, the evaluation measures remain inconsistent and unsystematic.

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.