Abstract
No public abstract is available from the verified source records.
Yahia Gobran
Abstract
Authors
Institutions
No ROR-resolved institution is linked to this work yet.
Provenance
crossref
Confidence 100%
pubmed
Confidence 98%
unpaywall
Confidence 95%
datacite
Confidence 0%
No local reference links have been materialized yet.
No local citing links have been materialized yet.
Unresolved referenced work
2017
An overview of clinical decision support systems: benefits, risks, and strategies for success
10.1038/s41746-020-0221-y · 2020
Implementation frameworks for end-to-end clinical AI: derivation of the SALIENT framework
10.1093/jamia/ocad088 · 2023
Reporting guideline for the early-stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI
10.1038/s41591-022-01772-9 · 2022
Unresolved referenced work
2021
Pharmacy futures: summit on artificial intelligence in pharmacy practice
10.1093/ajhp/zxae279 · 2024
ASHP statement on artificial intelligence in pharmacy
10.1093/ajhp/zxaf107 · 2025
Development of a hybrid artificial intelligence framework for pharmacotherapy optimization
10.1007/s11096-026-02205-0 · 2026
Guidelines for reporting artificial intelligence studies in medicines, pharmacotherapy, and pharmaceutical services: meDinAI development, validation and statement
10.1007/s11096-025-01905-3 · 2025
Framework for artificial intelligence implementation research in healthcare: synthesizing current evidence on barriers and facilitators
10.1038/s41746-026-02705-3 · 2026
A practical framework for operationalising responsible and equitable artificial intelligence in health care: tackling bias, inequity, and implementation challenges
2026
Artificial intelligence and machine learning in pharmaceutical research and health care: ethical challenges and a framework for responsible implementation
10.1016/j.japh.2026.103490 · 2026
A machine learning-based clinical decision support system to identify prescriptions with a high risk of medication error
10.1093/jamia/ocaa154 · 2020
Using a machine learning system to identify and prevent medication prescribing errors: a clinical and cost analysis evaluation
2020
Assessing the international transferability of a machine learning model for detecting medication error in the general internal medicine clinic: multicenter preliminary validation study
10.2196/23454 · 2021
Predicting self-intercepted medication ordering errors using machine learning
10.1371/journal.pone.0254358 · 2021
Pharmacists' perceptions of a machine learning model for the identification of atypical medication orders
10.1093/jamia/ocab071 · 2021
Assessment of a hybrid decision support system using machine learning with artificial intelligence to safely rule out prescriptions from medication review in daily practice
10.1007/s11096-021-01366-4 · 2022
Predicting adverse drug events in older inpatients: a machine learning study
10.1007/s11096-022-01468-7 · 2022
Development and validation of a machine learning-based detection system to improve precision screening for medication errors in the neonatal intensive care unit
10.3389/fphar.2023.1151560 · 2023
A machine learning-based clinical predictive tool to identify patients at high risk of medication errors
10.1038/s41598-024-83631-w · 2024
Machine learning-based risk prediction model for medication administration errors in neonatal intensive care units: a prospective direct observational study
2024
Using machine learning to predict pharmaceutical interventions during medication prescription review in a hospital setting
10.1093/ajhp/zxaf089 · 2025
Improving medication error classification using a reasoning large language model
10.1093/jamiaopen/ooag004 · 2026
AI-enhanced clinical decision support reduces medication errors and adverse drug events in a multicenter teaching hospital network: a prospective randomized controlled trial
10.1016/j.ijmedinf.2026.106457 · 2026
Development and temporal validation of machine learning models for predicting clinically relevant medication reconciliation discrepancies at the emergency department: a single-center retrospective study
10.1016/j.ijmedinf.2026.106309 · 2026
Development and validation of a machine learning-based clinical decision support tool for stratifying intravenous medication risk in hospitalized patients with heart failure
10.1007/s11096-026-02147-7 · 2026
Effects of AI assistance timing on pharmacists' trust in automated pill recognition technology: within-participants experimental study
10.2196/92822 · 2026
Locally deployed large language models for AI-assisted outpatient prescription review: crossover study
10.2196/97520 · 2026
TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods
10.1136/bmj-2023-078378 · 2024
Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
10.1038/s41591-020-1034-x · 2020
Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
10.1038/s41591-020-1037-7 · 2020
A path for translation of machine learning products into healthcare delivery
2020
The risks to patients associated with using artificial intelligence tools in pharmacy practice: a scoping review
2026
Bridging the gap between model performance and clinical practice: a scoping review of artificial intelligence models for drug-drug interaction prediction
10.1016/j.sapharm.2026.08.002 · 2026
Machine learning for medication error detection: a scoping review protocol
10.1136/bmjopen-2025-109192 · 2026
Using machine learning or deep learning models in a hospital setting to detect inappropriate prescriptions: a systematic review
10.1136/ejhpharm-2023-003857 · 2024
Machine learning to predict adverse drug events based on electronic health records: a systematic review and meta-analysis
10.1177/03000605241302304 · 2024
No additional external references are available.