Latest AI and machine learning research in surveillance for healthcare professionals.
Marzouk et al. reviewed 147 studies on artificial intelligence (AI) applications for predicting drug-drug, drug-disease, and drug-nutrient interactions, providing a broad overview of current machine learning and deep-learning approaches. However, several methodological and conceptual limitations reduce the reproducibility and interpretability of the review. The search strategy appears largely rest...
OBJECTIVES: Cervical cancer is a leading female malignancy with high global morbidity/mortality, and remains high recurrence risk after standard treatment. Accurate prognostic feature identification is critical for personalized therapy and patient survival improvement, while traditional indicators and single biomarkers lack sufficient accuracy in prognostic prediction. METHODS: In this population-...
Programmed death-ligand 1 (PD-L1) expression, commonly quantified as tumour proportion score (TPS), is a key biomarker guiding immunotherapy in non-sm...
OBJECTIVE: Policy surveillance typically involves detailed, time-consuming manual screening of policies for inclusion in a final dataset. This screeni...
Existing studies on prognostic prediction for multidrug-resistant organisms (MDROs) are limited by single-model designs, incomplete consideration of w...
INTRODUCTION: Central venous cannulation is essential for life-saving interventions including resuscitation of critically ill patients, hemodynamic mo...
Benchmarking AI chatbots using structured questions derived from the EFP S3-level guideline for stage IV periodontitis showed high clarity but variabl...
BACKGROUND: Surgical site outcome (SSO) reporting lacks details of management, focuses on infection, and overlooks dehiscence, seroma and haematoma. A...
Generative AI, particularly large language models (LLMs), is reshaping clinical workflows in dermatology. However, cloud-based commercial models pose ...
BACKGROUND: Palliative care is increasingly recognized as essential for an aging population and rising life-limiting illnesses. Machine learning (ML) ...
BACKGROUND & AIMS: Cholangiocarcinoma (CCA) is a major complication of primary sclerosing cholangitis (PSC), with a 20-year incidence of ∼15%. Early d...
Clinical symptoms are critical for diagnosing and managing upper respiratory tract infections, yet systematic comparisons across common pathogens rema...
BACKGROUND: Sheep and goats are essential components of global livestock systems, supporting smallholder livelihoods and contributing substantially to...
BACKGROUND: Large language models (LLMs) are rapidly emerging in health care, offering opportunities in decision support, education, and research, but...
BACKGROUND: Semantic interoperability, the ability of disparate health information systems to exchange and consistently interpret clinical data, is a ...
This study aims to evaluate a Grok application programming interface (API)-based file-attachment structured reporting workflow for silicone breast imp...
Escherichia coli (E. coli) is a key indicator of fecal contamination in freshwater and can signal the presence of other harmful bacteria and viruses. ...
Hikikomori, or prolonged social withdrawal, is an issue of global relevance. The HRI-15 is a brief tool for its assessment. This study enhances its ut...
As patients increasingly access radiology reports through electronic portals, imaging reports are no longer private technical communications between c...
OBJECTIVES: A substantial proportion of adults with locally advanced gastric cancer derive limited benefit from neoadjuvant chemotherapy (NAC), with a...