Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 1 to 10 of 214,544 articles

Jianpi Bushen Qingchang Huashi formula alleviates DSS-induced colitis through butyrate-associated HDAC1 inhibition and Th17/Treg homeostasis.

Journal of ethnopharmacology
ETHNOPHARMACOLOGICAL RELEVANCE: Jianpi Bushen Qingchang Huashi Formula (JBQHF) is a traditional Chinese medicine compound of nine herbal ingredients clinically used to treat chronic diarrhea and dysentery syndromes, corresponding to ulcerative coliti... read more 

Integrating structurally defined DNA-carbon nanotube sensors with machine learning for cancer detection.

Science advances
Liquid biopsy is a promising, noninvasive approach for cancer detection, but current methods often trade off accuracy, operability, and cost. To address these limitations, we introduce an artificial perception system (APS) for liquid biopsy that comb... read more 

Machine learning approaches to predict early cardiac immune-related adverse events in patients receiving immune checkpoint inhibitors.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
PURPOSE: Immune checkpoint inhibitor (ICI)-induced cardiac immune-related adverse events (cardiac irAEs) are rare yet serious complications. Clinical assessment tools to identify at-risk patients would allow for more effective prevention strategies, ... read more 

The Use of Artificial Intelligence in Neonatal Seizure Detection: An Artificial Intelligence-Assisted Systematic Review.

Journal of paediatrics and child health
BACKGROUND: Artificial intelligence (AI) is increasingly used in health care. We systematically reviewed evidence on the accuracy of AI in detecting neonatal seizures. METHODS: We searched PubMed and CENTRAL (covering Medline, EMBASE, CINAHL and tria... read more 

Spatiotemporal Asymmetries of Longitudinal Screening Mammograms for Breast Cancer Risk Prediction.

Radiology. Artificial intelligence
Purpose To develop and evaluate a new deep learning-based risk model that explicitly captures bilateral and longitudinal asymmetries on sequential mammograms for predicting breast cancer risk. Materials and Methods In this institutional review board ... read more 

Comparing a Large Language Model to Human-Generated Retention Messages for a Family Healthy Weight Program.

American journal of health promotion : AJHP
BackgroundText messaging can improve attendance and retention in community health programs; however, message development can be resource intensive.PurposeTo compare human and large language model (LLM)-generated retention messages in terms of creatio... read more 

Accelerated brain aging and anterior white matter hyperintensity burden in chronic post-stroke aphasia: a lesion-aware MRI analysis.

Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
BACKGROUND AND PURPOSE: Chronic post-stroke aphasia is heterogeneous, and focal lesion models do not fully explain persistent language impairment. We tested whether accelerated brain aging and regional white matter hyperintensity (WMH) burden provide... read more 

AI-derived oocyte morphology and follicular fluid biomarkers in donors.

Reproduction (Cambridge, England)
Oocyte quality is a key determinant of reproductive success, yet its assessment in assisted reproduction largely relies on subjective morphological criteria. Artificial intelligence (AI)-based image analysis has introduced greater objectivity into oo... read more 

[18F]FDG-PET in Lung Cancer: From Staging to Therapy Response.

PET clinics
Lung cancer, the most commonly diagnosed malignancy, is the leading cause of cancer-related mortality worldwide. Advancements in molecular imaging have expanded the role of [18F] 2-Fluoro-2-deoxy-glucose (FDG) PET/CT in the management of lung cancer,... read more 

Safety design guidelines for clinician-AI interaction in computer-aided diagnosis systems using system-theoretic framework with explainability validation.

Scientific reports
Ensuring the safety of Artificial Intelligence-enabled Computer-Aided Diagnosis systems is critical because diagnostic errors can have serious consequences for patient care. However, existing regulatory and risk management frameworks often do not suf... read more