98%
921
2 minutes
20
The potential of algorithm-based clinical decision support (CDS) in healthcare continues to increase with the growing field of artificial intelligence (AI)-enabled CDS. The use of these technologies to support clinicians, patients, and health systems is still quite new, and to date, implementors and regulators are still identifying the best processes and practices to ensure the effective, safe, and equitable use of these technology solutions. To assist individuals and organizations interested in implementation of algorithm-based CDS and AI-enabled CDS in healthcare, this article reviews the important regulatory decisions that form the landscape within which algorithm-based CDS has emerged, modern governance frameworks used to oversee these CDS systems, nuances in evaluation and monitoring throughout the CDS life cycle, best practices for real-world implementation, safety and equity considerations, and avenues for future collaboration and innovation.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1146/annurev-biodatasci-103123-094601 | DOI Listing |
Clin Epidemiol
September 2025
Department of Clinical Epidemiology, Department of Clinical Medicine, Center for Population Medicine, Aarhus University and Aarhus University Hospital, Aarhus, Denmark.
Purpose: To estimate the positive predictive value (PPV) of case ascertainment algorithm for hypocalcemia leading to hospitalization or emergency visit in the Swedish National Patient Register among women with postmenopausal osteoporosis (PMO) treated with antiresorptive agents. This was a regulator-requested validation study to support a multidatabase postauthorisation safety study (PASS) of antiresorptive treatment.
Methods: The Swedish part of the PASS was based on data from Swedish population registries.
Int J Cardiol Heart Vasc
October 2025
Department of Automation, Tsinghua University, Beijing, People's Republic of China.
Background: Current coronary artery disease (CAD) guidelines recommend to rule-out or rule-in patients for further examination by assessing a pretest probability (PTP) ≤ 5 % or ≥ 15 %. We developed and validated a deep-learning algorithm for rule-in or rule-out based on electrocardiogram (ECG) without myocardial ischemia evidence.
Methods: Between October 2019 and June 2022, data from two centers (Fuwai Hospital [Beijing] and Yunnan Fuwai Hospital) of CAD-suspected patients undergoing either coronary angiography or coronary computed tomography were used.
IEEE J Biomed Health Inform
September 2025
Deep learning models are increasingly used for making predictions based on clinical time series data, but model generalization remains a challenge. Continual learning approaches, which preserve representations while learning new distributions, are suitable for addressing this challenge. We propose Continual Bayesian Long Short Term Memory (C-BLSTM), a continual learning algorithm based on the Bayesian LSTM model for domain incremental learning.
View Article and Find Full Text PDFJ Dtsch Dermatol Ges
August 2025
Department of Dermatology, Venereology and Allergology, University Medical Center Leipzig, Leipzig, Germany.
Background And Objectives: Drug-induced sarcoidosis-like reaction (DISR) is an adverse event with emerging importance during immune checkpoint inhibitor (ICI) treatment in melanoma patients. Its reported frequency varies widely, making accurate diagnosis crucial. Distinguishing DISR from tumor progression is challenging, and misdiagnosis may lead to detrimental treatment changes.
View Article and Find Full Text PDFClin Chem Lab Med
September 2025
University Hospital of Reims, Laboratory of Biochemistry, Reims, France.
Introduction: The increasing use of therapeutic monoclonal antibodies (t-mAbs) has improved cancer and autoimmune disorder treatment. These therapeutics can interfere with serum protein electrophoresis (SPEP) and immunofixation (IF), potentially leading to the appearance of monoclonal bands that may be misinterpreted as monoclonal gammopathies. Identifying the migration patterns and detection thresholds of t-mAbs is crucial to avoid misinterpretation in clinical laboratories.
View Article and Find Full Text PDF