JMIR Formative Research

Large Language Models (LLMs) have great potential to improve and make the work of clinicians more efficient. Previous studies have mainly focused on web-based services such as ChatGPT, often with simulated cases. For the processing of personalized patient data, web-based services have major data protection concerns. Ensuring compliance with data protection and medical device regulations therefore remains a critical challenge for adopting LLMs in clinical settings.

The integration of Electronic Health Records (EHRs) with telehealth platforms represents a transformative approach in healthcare, providing critical accessibility and engagement solutions, especially during the COVID-19 pandemic. In Riyadh's hospitals, the adoption of EHR-integrated telehealth has significantly increased and offers enhanced patient care options. However, there is a need to examine its continued relevance, effectiveness, and challenges in a post-pandemic context.

Reducing patient harm and improving patient safety is a central objective in global healthcare. Effective communication and meaningful patient engagement are considered essential strategies to achieve this goal. However, implementation of structured and strategic patient engagement at the organizational level remains limited, particularly in the context of patient safety. Patient and family advisory councils (PFACs) offer a promising model to enhance organizational-level patient engagement, yet guidance on implementation and targeted training for PFAC members is scarce.

Novel glucagon-like peptide 1 receptor agonists (GLP1RAs) for obesity treatment have generated much dialogue on digital media platforms. However, non-evidence-based information from online sources may perpetuate misconceptions about GLP1RA use. A promising new digital avenue for patient education is large language models (LLMs), which could potentially be used as an alternative platform to clarify questions about GLP1RA therapy.

Breast cancer remains the most common cancer among women globally. Mammography is a key diagnostic modality; however, interpretation is increasingly challenged by rising imaging volumes, a global shortage of breast radiologists, and variability in reader experience. Artificial intelligence (AI) has been proposed as a potential adjunct to address these issues, particularly in settings with high breast density, such as Asian populations. This study aimed to evaluate the impact of AI assistance on mammographic diagnostic performance among resident and consultant radiologists in Singapore.

While recent studies showed the potential of conversational agents (CAs) to help alleviate depressive moods, the dynamics of user-chatbot interactions in mental health support remain underexplored.

Physical inactivity increases the risk of chronic disease and reduces life expectancy, yet adherence to physical activity (PA) guidelines remains low. SMS text messages are promising for promoting PA, but it is not clear what type of messaging is most effective. Messages with causal information, which explain why a recommendation is being made, may be more persuasive than messages containing only recommendations.

The rising burden of disease associated with mental disorders calls for evidence-based psychological interventions that can be swiftly scaled up. Blending smartphone-based mental health apps (MHapps) for delivering ecological momentary interventions (EMIs) with traditional in-person interventions may have the benefits of improving treatment adherence, the application of learned techniques into everyday life and, in turn, enhancing clinical response. However, previous work has shown that most existing MHapps were developed for specific research studies or for profit, thereby making them difficult to adapt, particularly in time-limited and resource-scarce settings.

Generative Artificial Intelligence (AI) has shown great potential in various fields, including healthcare. However, its application in developing patient education materials(PEMs), particularly those with coronary heart disease (CHD), remains underexplored. Traditional methods for creating these materials are time-consuming and lack personalization, which limits their effectiveness.

Older people are particularly vulnerable to loneliness and social isolation due to common age-related changes. The ability to maintain social relationships is considered important for health and well-being and is an essential aspect of healthy aging. The use of information and communication technology has been shown to promote social connectedness and social support among older people; however, many existing solutions require already established contacts and are not developed based on expressed needs among older people experiencing loneliness or social isolation.

The integration of artificial intelligence (AI) in medical education is evolving, offering new tools to enhance teaching and assessment. Among these, script concordance tests (SCT) are well suited to evaluate clinical reasoning in contexts of uncertainty. Traditionally, SCTs require expert panels for scoring and feedback, which can be resource intensive. Recent advances in generative AI, particularly large language models (LLM), suggest the possibility of replacing human experts with simulated ones, though this potential remains underexplored.

Key information pages for informed consent require a concise summary of information to improve participant understanding but have not widely incorporated health literacy best practices.