Patients’ voices are often difficult to capture directly in healthcare decisions. This scoping review examines how artificial intelligence (AI) has been applied to measure and predict patient values and preferences, aiming to evaluate its…
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Emergency care systems are challenged by the emergence of an ageing population, requiring tailored inputs facilitated by early care needs assessment. We examined the potential of Machine Learning algorithms to identify in-hospital healthcare contacts…
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Modern medicine increasingly relies on multimodal data, ranging from clinical notes to imaging and genomics, to guide diagnosis and treatment. However, integrating these heterogeneous data sources in a principled and interpretable manner remains a…
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Diagnosing language disorders associated with autism is a complex challenge, often hampered by the subjective nature and variability of traditional assessment methods. In this study, we explored large language models (LLMs) to overcome the…
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Based on the literature and collective experience and consensus of the authors, this article provides a definition for an event-driven electronic diary (EDeD), outlines considerations for the use of EDeDs, and provides best practice…
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This Matters Arising questions the MCID threshold for the Knee Society Score in Xiaodi Liu et al.’s study, noting that it represents an excessively large proportion of the total score and may not accurately…
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The demand for demographically and geographically diverse, high-quality, fit-for-purpose real-world data has been increasing to support regulatory and other healthcare decision making. Accessing and sharing healthcare data across sites, regions, and countries while ensuring…
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The efficacy of immunotherapy in bladder cancer (BCa) is highly influenced by tumor heterogeneity, underscoring the urgent need for mechanistic insights and precise molecular subtyping. Adenosine-to-inosine RNA editing (AIRE), a critical post-transcriptional mechanism driving…
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Digital mental health interventions (DMHIs) are playing an increasingly important role in supporting mental healthcare. Despite their widespread adoption during and after the COVID-19 pandemic, there has been a lack of review focused on…
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Sleep disturbances are an underrecognized factor associated with reduced workplace productivity (“presenteeism”). Previous studies have largely relied on self-reported sleep data, limiting their scalability and accuracy. We investigated associations between smartphone-based sleep metrics and…
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