JMIR Formative Research

Artificial Intelligence (AI) has the capacity to transform healthcare by improving clinical decision-making, optimizing workflows, and enhancing patient outcomes. However, this potential remains limited by a complex set of technological, human, and ethical barriers that constrain safe and equitable implementation. This paper argues for a holistic, systems-based approach to AI integration that addresses these challenges as interconnected rather than isolated. It identifies key technological barriers including limited explainability, algorithmic bias, integration and interoperability issues, lack of generalizability, and difficulties in validation. Human factors such as resistance to change, insufficient stakeholder engagement, and education and resource constraints further impede adoption, while ethical and legal challenges related to liability, privacy, informed consent, and inequity compound these obstacles. Addressing these issues requires transparent model design, diverse datasets, participatory development, and adaptive governance. Recommendations emerging from this synthesis are: (1) establish standardized international regulatory and governance frameworks; (2) promote multidisciplinary co-design involving clinicians, developers, and patients; (3) invest in clinician education, AI literacy, and continuous training; (4) ensure equitable resource allocation through dedicated funding and public–private partnerships; (5) prioritize multimodal, explainable, and ethically aligned AI development; and (6) focus on long-term evaluation of AI in real-world settings to ensure adaptive, transparent, and inclusive deployment. Adopting these measures can align innovation with accountability, enabling healthcare systems to harness AI’s transformative potential responsibly and sustainably to advance patient care and health equity.

South Korea has the highest suicide rate among the OECD nations, with particularly elevated figures among persons with disabilities (PWD). Research has shown a strong correlation between suicidal ideation and suicide attempts.

Experience sampling methodology (ESM) is an assessment method utilised in psychosis research. Symptom severity and gender may be associated with ESM engagement. Exploring qualitative experiences of using ESM amongst people with psychosis should aid developing more relevant, accessible digital assessments.

Information provided by health professionals can be complex and is often not well understood by healthcare consumers, leading to adverse outcomes. Clinician-led communication approaches such as ‘teach-back’ can improve consumer understanding, yet are infrequently used by clinicians. A possible solution is to build consumers’ skills to proactively check their understanding rather than waiting for the clinician to do so; however, there are few educational resources to support consumers to build these skills.

Tobacco use remains the leading cause of preventable mortality in the United States; yet, evidence-based cessation services remain underused due to staffing constraints, limited access to counseling, and competing clinical priorities. Generative artificial intelligence (GenAI) chatbots may address these barriers by delivering personalized, guideline-aligned counseling through naturalistic dialogue. However, little is known about how GenAI chatbots support smoking cessation at both outcome and communication process levels.

For decades, the measurement of sleep and wake has relied upon watch-based Actigraphy as an alternative to expensive, obtrusive, clinical monitoring. To date, we have relied upon a handful of algorithms to score actigraphy data as sleep or wake. However, these algorithms have largely been tested and validated with only small samples of young healthy individuals.

Early identification of the etiology of spontaneous intracerebral hemorrhage (ICH) could significantly contribute to planning a suitable treatment strategy. A notable radiomics-based artificial intelligence (AI) model for classifying causes of spontaneous ICH from brain computed tomography (CT) scans has been previously proposed.

The application of large language models (LLMs) in medicine is rapidly advancing. However, evaluating LLM capabilities in specialized domains such as traditional Chinese medicine (TCM), which possesses a unique theoretical system and cognitive framework, remains a sizable challenge.

This pilot study offers preliminary evidence that a virtual meal-preparation task is feasible for older adults and highlights that the community engagement studios are an effective approach to generate community-informed strategies to enhance intervention designs and reach.

Early cancer detection is crucial, but recognising the significance of associated symptoms such as unintended weight loss in primary care remains challenging. Clinical Decision Support Systems (CDSS) can aid cancer detection, but face implementation barriers and low uptake in real-world settings. To address these issues, simulation environments offer a controlled setting to study CDSS usage and improve their design for better adoption in clinical practice.

While digital health solutions are becoming increasingly sophisticated, simple forms of everyday digital support may offer underexplored opportunities to promote health among older adults. However, evidence remains scarce on whether such teleassistance approaches can effectively enhance health literacy and daily self-care, particularly among populations facing socioeconomic and educational disparities.

The “Archive of German language general practice” (ADAM) stores about 500 paper based doctoral theses from 1965 till today. While they have been grouped in different categories no deeper systematic process of information extraction (IE) has been performed yet. Recently developed Large Language Models (LLMs) like ChatGPT have been attributed the potential to help in IE of medical documents. However, there are concerns about hallucination of LLM. Furthermore, there have not been reports regarding their usage in non-recent doctoral theses yet.