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:

Physician understanding, explainability, and trust in a hypothetical machine learning risk calculator
William K. Diprose, Nicholas Buist, Ning Hua, et al.
Journal of the American Medical Informatics Association (2019) Vol. 27, Iss. 4, pp. 592-600
Open Access | Times Cited: 144

Showing 1-25 of 144 citing articles:

Explainable artificial intelligence models using real-world electronic health record data: a systematic scoping review
Seyedeh Neelufar Payrovnaziri, Zhaoyi Chen, Pablo Rengifo‐Moreno, et al.
Journal of the American Medical Informatics Association (2020) Vol. 27, Iss. 7, pp. 1173-1185
Open Access | Times Cited: 250
Challenges to implementing artificial intelligence in healthcare: a qualitative interview study with healthcare leaders in Sweden
Lena Petersson, Ingrid Larsson, Jens M. Nygren, et al.
BMC Health Services Research (2022) Vol. 22, Iss. 1
Open Access | Times Cited: 213
Ethical, legal, and social considerations of AI-based medical decision-support tools: A scoping review
Anto Čartolovni, Ana Tomičić, Elvira Lazić Mosler
International Journal of Medical Informatics (2022) Vol. 161, pp. 104738-104738
Closed Access | Times Cited: 142
Implementation Frameworks for Artificial Intelligence Translation Into Health Care Practice: Scoping Review
Fábio Gama, Daniel Tyskbo, Jens M. Nygren, et al.
Journal of Medical Internet Research (2021) Vol. 24, Iss. 1, pp. e32215-e32215
Open Access | Times Cited: 124
Explainable AI for clinical and remote health applications: a survey on tabular and time series data
Flavio Di Martino, Franca Delmastro
Artificial Intelligence Review (2022) Vol. 56, Iss. 6, pp. 5261-5315
Open Access | Times Cited: 85
Machine learning in precision diabetes care and cardiovascular risk prediction
Evangelos K. Oikonomou, Rohan Khera
Cardiovascular Diabetology (2023) Vol. 22, Iss. 1
Open Access | Times Cited: 47
Guiding principles for the responsible development of artificial intelligence tools for healthcare
Kimberly Badal, Carmen M. Lee, Laura J. Esserman
Communications Medicine (2023) Vol. 3, Iss. 1
Open Access | Times Cited: 43
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: 22
Resistance to artificial intelligence in health care: Literature review, conceptual framework, and research agenda
Yikai Yang, Eric W.T. Ngai, Lei Wang
Information & Management (2024) Vol. 61, Iss. 4, pp. 103961-103961
Closed Access | Times Cited: 17
Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study
Daria Shevtsova, Anam Ahmed, Iris W A Boot, et al.
JMIR Human Factors (2024) Vol. 11, pp. e47031-e47031
Open Access | Times Cited: 16
Health Professionals' Perspectives on the Use of Artificial Intelligence in Healthcare: A Systematic Review
Rakesh Kumar Sahoo, Krushna Chandra Sahoo, Sapna Negi, et al.
Patient Education and Counseling (2025), pp. 108680-108680
Closed Access | Times Cited: 1
Systematic review of current natural language processing methods and applications in cardiology
Meghan Reading Turchioe, Alexander Volodarskiy, Jyotishman Pathak, et al.
Heart (2021) Vol. 108, Iss. 12, pp. 909-916
Open Access | Times Cited: 77
Machine learning in medicine: should the pursuit of enhanced interpretability be abandoned?
Chang Ho Yoon, Robert Torrance, Naomi Scheinerman
Journal of Medical Ethics (2021) Vol. 48, Iss. 9, pp. 581-585
Open Access | Times Cited: 75
Re-focusing explainability in medicine
Laura Arbelaez Ossa, Georg Starke, Giorgia Lorenzini, et al.
Digital Health (2022) Vol. 8, pp. 205520762210744-205520762210744
Open Access | Times Cited: 66
Application of Artificial Intelligence in Shared Decision Making: Scoping Review
Samira Abbasgholizadeh Rahimi, Michelle Cwintal, Yuhui Huang, et al.
JMIR Medical Informatics (2022) Vol. 10, Iss. 8, pp. e36199-e36199
Open Access | Times Cited: 60
Exploring perceptions of healthcare technologies enabled by artificial intelligence: an online, scenario-based survey
Alison L. Antes, Sara Burrous, Bryan A. Sisk, et al.
BMC Medical Informatics and Decision Making (2021) Vol. 21, Iss. 1
Open Access | Times Cited: 59
Trading off accuracy and explainability in AI decision-making: findings from 2 citizens’ juries
Sabine N van der Veer, Lisa Riste, Sudeh Cheraghi‐Sohi, et al.
Journal of the American Medical Informatics Association (2021) Vol. 28, Iss. 10, pp. 2128-2138
Open Access | Times Cited: 55
It’s Just Not That Simple: An Empirical Study of the Accuracy-Explainability Trade-off in Machine Learning for Public Policy
Andrew Bell, Ian René Solano-Kamaiko, Oded Nov, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 248-266
Open Access | Times Cited: 45
Utilization of model-agnostic explainable artificial intelligence frameworks in oncology: a narrative review
Colton Ladbury, Reza Zarinshenas, Hemal Semwal, et al.
Translational Cancer Research (2022) Vol. 11, Iss. 10, pp. 3853-3868
Open Access | Times Cited: 43
Who Goes First? Influences of Human-AI Workflow on Decision Making in Clinical Imaging
Riccardo Fogliato, Shreya Chappidi, Matthew P. Lungren, et al.
2022 ACM Conference on Fairness, Accountability, and Transparency (2022), pp. 1362-1374
Open Access | Times Cited: 42
Relationship between prediction accuracy and feature importance reliability: An empirical and theoretical study
Jianzhong Chen, Leon Qi Rong Ooi, Trevor Wei Kiat Tan, et al.
NeuroImage (2023) Vol. 274, pp. 120115-120115
Open Access | Times Cited: 32
Multi-stakeholder preferences for the use of artificial intelligence in healthcare: A systematic review and thematic analysis
Vinh Vo, Gang Chen, Yves Saint James Aquino, et al.
Social Science & Medicine (2023) Vol. 338, pp. 116357-116357
Open Access | Times Cited: 28
Trustworthy artificial intelligence in Alzheimer’s disease: state of the art, opportunities, and challenges
Shaker El–Sappagh, José M. Alonso, Tamer Abuhmed, et al.
Artificial Intelligence Review (2023) Vol. 56, Iss. 10, pp. 11149-11296
Closed Access | Times Cited: 25
Explainable machine learning framework to predict personalized physiological aging
David Bernard, Emmanuel Doumard, Isabelle Ader, et al.
Aging Cell (2023) Vol. 22, Iss. 8
Open Access | Times Cited: 23
Deep Learning for Cardiovascular Imaging
Ramsey M. Wehbe, Aggelos K. Katsaggelos, Kristian J. Hammond, et al.
JAMA Cardiology (2023) Vol. 8, Iss. 11, pp. 1089-1089
Closed Access | Times Cited: 23
Page 1 - Next Page

Scroll to top