Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

Factors influencing clinician and patient interaction with machine learning-based risk prediction models: a systematic review
Rebecca Giddings, Anabel Joseph, Thomas Callender, et al.
The Lancet Digital Health (2024) Vol. 6, Iss. 2, pp. e131-e144
Open Access | Times Cited: 21

Showing 21 citing articles:

Enhancing predictive accuracy for urinary tract infections post-pediatric pyeloplasty with explainable AI: an ensemble TabNet approach
Hongyang Wang, Junpeng Ding, Shuochen Wang, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Machine-Learning Parsimonious Prediction Model for Diagnostic Screening of Severe Hematological Adverse Events in Cancer Patients Treated with PD-1/PD-L1 Inhibitors: Retrospective Observational Study by Using the Common Data Model
Seok Jun Park, Seungwon Yang, Su-Hyun Lee, et al.
Diagnostics (2025) Vol. 15, Iss. 2, pp. 226-226
Open Access
Intricacies of human–AI interaction in dynamic decision-making for precision oncology
Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov, et al.
Nature Communications (2025) Vol. 16, Iss. 1
Open Access
Prognostic predictions in psychosis: exploring the complementary role of machine learning models
Violet van Dee, Seyed Mostafa Kia, Caterina Fregosi, et al.
medRxiv (Cold Spring Harbor Laboratory) (2025)
Closed Access
The use of machine learning based models to predict the severity of community acquired pneumonia in hospitalised patients: A systematic review
Caitlin Lythgoe, David Hamilton, Brian Johnston, et al.
Journal of the Intensive Care Society (2025)
Closed Access
Developing an electronic Surprise Question to predict end-of-life prognosis in a prospective cohort study of acute hospital admissions
Baldev Singh, Nisha Kumari-Dewat, Adam Ryder, et al.
Clinical Medicine (2025), pp. 100292-100292
Open Access
Anticipatory Moral Distress in Machine Learning-Based Clinical Decision Support Tool Development: A Qualitative Analysis
Clare Whitney, Heidi Preis, A. Vargas
SSM - Qualitative Research in Health (2025), pp. 100540-100540
Open Access
Prediction Models for Diabetes in Children and Adolescents: A Review
L. Cvetićanin, Marko Arsenović
Applied Sciences (2025) Vol. 15, Iss. 6, pp. 2906-2906
Open Access
Predicting total healthcare demand using machine learning: separate and combined analysis of predisposing, enabling, and need factors
Fatih Orhan, Mehmet Nurullah Kurutkan
BMC Health Services Research (2025) Vol. 25, Iss. 1
Open Access
Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases
Caiyu Shen, Shuai Wang, Ruiheng Huo, et al.
BMC Cardiovascular Disorders (2025) Vol. 25, Iss. 1
Open Access
“Will artificial intelligence platforms replace designers in the future?” analyzing the impact of artificial intelligence platforms on the engineering design industry through color perception
Yu Li
Engineering Applications of Artificial Intelligence (2024) Vol. 138, pp. 109369-109369
Closed Access | Times Cited: 3
The ethical dimensions of utilizing Artificial Intelligence in palliative care
Oonjee Oh, George Demiris, Connie M. Ulrich
Nursing Ethics (2024)
Closed Access | Times Cited: 2
Cutting through the hype: the true economic impact and ROI of AI in radiology
Hugues Brat
European Radiology (2024) Vol. 34, Iss. 12, pp. 7904-7906
Closed Access | Times Cited: 1
Influence of Generative AI on Healthcare Industries Enhanced by the Metaverse
Rashmi Kumari, Raghwendra Kishore Singh
Advances in medical technologies and clinical practice book series (2024), pp. 129-166
Closed Access | Times Cited: 1
Assessments of Generative AI as Clinical Decision Support Ought to be Incorporated into Randomised Controlled Trials of Electronic Alerts for Acute Kidney Injury
Donal J. Sexton, Conor Judge
Mayo Clinic Proceedings Digital Health (2024) Vol. 2, Iss. 4, pp. 606-610
Open Access | Times Cited: 1
Women's views on using artificial intelligence in breast cancer screening: A review and qualitative study to guide breast screening services
Stacy M. Carter, Diana Popic, M. Luke Marinovich, et al.
The Breast (2024) Vol. 77, pp. 103783-103783
Open Access | Times Cited: 1
Pioneering Methods in Brain Optimization and Mental Health Treatment
Robert B. Kargbo
ACS Medicinal Chemistry Letters (2024) Vol. 15, Iss. 3, pp. 337-339
Open Access
Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy
Dipesh Niraula, Kyle C. Cuneo, Ivo D. Dinov, et al.
medRxiv (Cold Spring Harbor Laboratory) (2024)
Open Access
User and developer views on using Artificial Intelligence technologies to facilitate the early diagnosis of skin cancers in primary care settings: A qualitative study (Preprint)
O. T. G. Jones, Natália Calanzani, Suzanne E. Scott, et al.
(2024)
Open Access
Smart Investigation Through Machine Learning: Study On Faculty Selection In A Large Multi-Disciplinary University Of North-Eastern India
Kaberi De, Prasanjit Dasgupta, Subhrajyoti Deb
(2024)
Open Access
When time is of the essence: ethical reconsideration of XAI in time-sensitive environments
Andreas Wabro, Markus Herrmann, Eva C. Winkler
Journal of Medical Ethics (2024), pp. jme-110046
Closed Access
Enhancing Predictive Accuracy for Urinary Tract Infections Post-Pediatric Pyeloplasty with Explainable AI: An Ensemble TabNet Approach
Hongyang Wang, Junpeng Ding, Shuochen Wang, et al.
(2024)
Closed Access
User and developer views on using Artificial Intelligence technologies to facilitate the early diagnosis of skin cancers in primary care settings: A qualitative study (Preprint)
O. T. G. Jones, Natália Calanzani, Suzanne E. Scott, et al.
JMIR Cancer (2024)
Open Access
Page 1

Scroll to top