Gastric cancer patients frequently experience skeletal muscle loss during the perioperative and adjuvant treatment period, which has been associated with poorer treatment tolerance and adverse clinical outcomes. Early identification of patients at hi... read more
Author Summary In many low- and middle-income countries, mothers who are overweight often live in the same household as children who are too short or too thin for their age. Nutrition programmes that try to reach such families have limited resources,... read more
Systematic Reviews (SRs) are the gold standard for evidence synthesis, but the manual title and abstract screening of thousands of references creates a severe bottleneck. Existing automated tools have historically struggled to achieve the near-perfec... read more
Machine learning models that can utilize high-dimensional data to make predictions and derive biological insights can improve understanding of diseases. Here, we develop a biologically annotated neural network model for proteomics data (P-BANN) which... read more
Traditional audit methods that rely on written records often miss the nuances of clinical reasoning that influence patient care. Ambient artificial intelligence captures spoken clinical encounters, allowing the analysis of real clinicianpatient dialo... read more
Introduction: Polypharmacy in older adults is associated with increased risks of adverse drug events and functional decline. Discharge summaries often contain deprescribing recommendations, but these are frequently overlooked due to documentation com... read more
Adverse drug effects remain a major barrier to safe and effective cancer therapy, underscoring the need for tools that predict treatment-related toxicities. We analyzed multimodal real-world data from 14,596 cancer patients across 38 cancer entities,... read more
This paper focuses on forecasting minute-by-minute stress, anxiety, and affective states using wearable sensor data. It addresses mental health as a growing concern and the limitations of traditional assessment methods. A time-series machine learning... read more
Shortcut learning poses a significant challenge in clinical artificial intelligence, as models may rely on spurious signals rather than clinically relevant features, leading to biased predictions and poor generalization. Existing detection methods ar... read more
Abstract Background: Healthcare providers in low- and middle- income countries (LMICs) are increasingly relying on Artificial Intelligence (AI) tools, yet most available AI assistants are general-purpose systems not designed for the specific clinical... read more
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