Latest AI and machine learning research in alternative medicine for healthcare professionals.
The application of machine learning (ML) techniques in classification and prediction tasks has greatly advanced our comprehension of biological systems. There is a notable shift in the trend towards integration methods that specifically target the simultaneous analysis of multiple modes or types of data, showcasing superior results compared to individual analyses. Despite the availability of diver...
Variants of uncertain significance (VUS) pose a significant challenge for those undergoing genetic testing, leading to prolonged uncertainty and inappropriate medical care. VUS rate reduction is critical to fully realize the utility of genetic testing for all populations. With the growth of large-scale biological data sources and modern Machine Learning (ML) techniques, predictive modeling has eno...
Introduction: The increasing complexity of clinical cancer research necessitates the development of automated tools capable of integrating clinical an...
This study constructs an acupuncture knowledge graph (AcuKG) to systematically organize and represent acupuncture-related knowledge in a structured an...
The WNT signaling pathway plays a critical role in colorectal cancer (CRC) initiation and progression, particularly in early-onset cases among underse...
Cardiovascular medicine is rapidly evolving, as it integrates digital technologies intended to decentralize care from the clinic and/or hospital setti...
Cancer is increasingly recognized as a metabolic disease with strong nutritional determinants. Recent advances in multi-omics technologies and artific...
Deep learning has emerged as a promising tool for automating gastrointestinal (GI) disease diagnosis. However, multi-class GI disease classification r...
Data extraction is a critical but resource-intensive step of the evidence review process. Whilst there is evidence that artificial intelligence (AI) a...
Musculoskeletal (MSK) modeling and ultrasound imaging (USI) are complementary techniques that, when combined with three-dimensional gait analysis (3DG...
The growing use of large language models (LLMs) for biomedical question answering raises concerns about the accuracy and evidentiary support of their ...
Spinal cord injury (SCI) remains a devastating neurological condition with high global incidence and minimal curative options. The pathobiology is mul...
Childhood maltreatment is a well-established risk factor for substance misuse. However, it remains unclear whether risk for specific illicit substance...
Accurate medical image classification is critical for early diagnosis and effective treatment planning. However, conventional deep learning models oft...
The incidence of early-onset colorectal cancer (EOCRC; <50 years) continues to rise, with the most rapid increases observed among Hispanic/Latino (H/L...
We previously developed the Evaluation of Autism Gene Link Evidence (EAGLE) manual curation framework and used it to characterise 219 autism-associate...
Although living donor kidney transplantation (LDKT) generally achieves excellent outcomes, 5–12% of recipients experience early graft dysfunction, whi...
A major concern in applying large language models (LLMs) to medicine is their reliability. Because LLMs generate text by sampling the next token (or w...
Should original research routinely contain prominent policy claims, such as recommendations for policymakers or broad calls to action? Growing emphasi...
We present a Bayesian machine learning framework integrating genetic, molecular, and wearable sensor biomarkers for precision medicine in Parkinson’s ...