Latest AI and machine learning research in fda general for healthcare professionals.
Modern day healthcare has seen an increase in polypharmacy, which is the prescription of multiple drugs as medication to treat illnesses simultaneously. Therefore there is an increased risk of adverse drug reactions resulting from drug-drug interactions. Existing techniques in the field of pharmacovigilance suffer from many drawbacks. Many machine learning approaches using single models face diffi...
Dengue fever, a mosquito-borne disease caused by dengue virus (DENV), has become a global health problem, and no FDA-approved drug is currently available. Qingwen Baidu Decoction (QBD) is used to treat the critical phase of dengue fever in China, but its mechanism of action remains unclear. In this work, we integrated bioinformatics analysis, machine learning, and network pharmacology to investiga...
Tuberculosis is a major global public health problem caused by Mycobacterium tuberculosis. Pyrazinamide (PZA), a key first-line drug, is activated to ...
The accurate simultaneous detection of uric acid (UA) and dopamine (DA) remains a challenge in clinical diagnostics due to the insufficient sensitivit...
Accurate RNA splicing is essential for gene expression and protein function, yet the mechanisms governing splice site recognition remain incompletely ...
OBJECTIVES: This study examined why artificial intelligence (AI)-based clinical decision support tools have had limited clinical translation in the em...
Cancer remains a leading cause of mortality globally, with the incidence projected to reach 28.4 million new cases annually by 2040. Traditional drug ...
BACKGROUND: Health technology assessment bodies increasingly emphasise the importance of preference-weighted health-related quality of life (HRQoL) ev...
BACKGROUND: To assess patient trust in AI-generated medication information, examine associated behavioral safety risks, and evaluate patient expectati...
BACKGROUND: Allergic rhinitis (AR) is a common Th2-mediated inflammatory disease of the nasal mucosa, in which eosinophils serve as pivotal effector c...
The emergence of structurally complex therapeutic modalities, including bispecific antibodies, antibody-drug conjugates, fusion proteins, incretins, r...
Non-technical losses (NTL) remain a major source of economic risk for electricity distribution utilities. Although extensive research has focused on c...
INTRODUCTION: The contribution and future of artificial intelligence (AI) in cleft lip and/or palate (CL/P) need exploration. The aim of this scoping ...
INTRODUCTION: Biologic therapies have transformed the management of moderate-to-severe psoriasis; however, long-term treatment requires optimization s...
INTRODUCTION: Morphologic evaluation of peripheral blood (PB) smears and bone marrow aspirates (BMA) remains central to the diagnosis of acute leukemi...
INTRODUCTION: Any tool that can reduce the administrative burden on healthcare providers while preserving safe, accountable and high-quality medical d...
BACKGROUND: Digital surgery technologies, including robotic systems, artificial intelligence (AI) algorithms, augmented reality platforms, and advance...
Digital authentication technologies are increasingly threatened by the rapid advance of artificial intelligence and sophisticated cyberattacks. Agains...
INTRODUCTION: The global prevalence of heart failure continues to increase, particularly in ageing populations. Many older patients receiving home-bas...
Targeted Maximum Likelihood Estimation, also referred to as Targeted Minimum Loss Estimation (TMLE), is a statistical method that enables causal infer...