Latest AI and machine learning research in prescriptions for healthcare professionals.
Medication non-adherence remains a significant challenge in managing chronic conditions like diabetes and hypertension, leading to increased morbidity, preventable hospitalizations, and over $300 billion in annual healthcare costs. This burden is particularly pronounced in resource-limited settings, where fragmented data and limited resources hinder early risk identification. This study introduces...
Hepatic steatosis (HS) is a common cardiometabolic risk factor frequently present but under-diagnosed in patients with suspected or known coronary artery disease. We used artificial intelligence (AI) to automatically quantify hepatic tissue measures for identifying HS from CT attenuation correction (CTAC) scans during myocardial perfusion imaging (MPI) and evaluate their added prognostic value for...
Large language models (LLMs) have fundamentally changed how patients and clinicians retrieve information; however, it is unclear how accurate and cons...
The early detection of adverse drug events (ADEs) became a critical issue in clinical research after the thalidomide disaster in 1961, which resulted ...
Medication errors pose a significant threat to public health. Despite efforts by health agencies and the implementation of various interventions, such...
Falls are a leading cause of injury and reduced mobility, particularly among prosthetic users, older adults, and individuals with neuromuscular impair...
Early detection of adverse events and fall injuries may improve patient safety outcomes for clinical trials in geriatric populations. This study evalu...
Zidovudine (AZT), a key antiretroviral drug used for HIV treatment and preventing mother-to-child transmission, has insufficient post-marketing pharma...
Diabetes-related foot ulcers (DFUs) are a serious complication of diabetes, often resulting in infection, amputation, or even mortality. Offloading fo...
Breast cancer is the most frequently diagnosed malignancy among women worldwide and a major cause of mortality. Early and accurate detection is vital ...
The emergence of Large Language Models (LLMs) like ChatGPT presents significant opportunities for healthcare, yet raises concerns about accuracy, espe...
To identify post-marketing adverse event (AE) signals associated with isotretinoin using real-world data from the U.S. Food and Drug Administration (F...
By personalizing healthcare to an individual’s specific requirements, precision health promises to maximize benefit and minimize harm, thereby maximiz...
Disease heterogeneity and commonality pose significant challenges to precision medicine, as traditional approaches frequently focus on single disease ...
Opioid misuse remains a critical public health concern, associated with increased risk of overdose, psychiatric comorbidity, and societal costs. While...
This study addresses limitations of traditional medication adherence assessment tools by developing a machine learning model to evaluate post-discharg...
Patients recently discharged from psychiatric hospitalization are at increased risk of intentional self-harm, including suicide. Using linked populati...
Predicting variant-drug interactions is essential for advancing precision medicine across therapeutic areas. The Pharmacogenomics Knowledge Base (Phar...
Epistasis causes an individual’s genetic background to modulate a DNA variant’s effect on trait [1–6]. Epistatic interactions among different loci in ...
For accurate medication usage statistics and medication adherence calculations, we need to have an accurate days’ supply (DS) for each prescription. U...