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OpenAlex Citation Counts

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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:

Language, Speech, and Facial Expression Features for Artificial Intelligence–Based Detection of Cancer Survivors’ Depression: Scoping Meta-Review
Urška Smrke, Izidor Mlakar, Simon Lin, et al.
JMIR Mental Health (2021) Vol. 8, Iss. 12, pp. e30439-e30439
Open Access | Times Cited: 18

Showing 18 citing articles:

Modern views of machine learning for precision psychiatry
Zhe Chen, Prathamesh Kulkarni, Isaac R. Galatzer‐Levy, et al.
Patterns (2022) Vol. 3, Iss. 11, pp. 100602-100602
Open Access | Times Cited: 90
Machine learning based approaches for clinical and non-clinical depression recognition and depression relapse prediction using audiovisual and EEG modalities: A comprehensive review
Sana Yasin, Alice Othmani, Imran Raza, et al.
Computers in Biology and Medicine (2023) Vol. 159, pp. 106741-106741
Closed Access | Times Cited: 33
Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy
Moustaq Karim Khan Rony, Dipak Chandra Das, Most. Tahmina Khatun, et al.
Digital Health (2025) Vol. 11
Open Access | Times Cited: 1
Intelligent oncology: The convergence of artificial intelligence and oncology
Bo Lin, Zhibo Tan, Yaqi Mo, et al.
Journal of the National Cancer Center (2022) Vol. 3, Iss. 1, pp. 83-91
Open Access | Times Cited: 27
Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians
Katherine C. Kellogg, Shiri Sadeh‐Sharvit
Frontiers in Psychiatry (2022) Vol. 13
Open Access | Times Cited: 25
Artificial Intelligence in Diagnosing Depression Through Behavioural Cues: A Diagnostic Accuracy Systematic Review and Meta‐Analysis
Yong Shian Goh, Qi Rui See, Nopporn Vongsirimas, et al.
Journal of Clinical Nursing (2025)
Closed Access
Exploring the Intersection of Artificial Intelligence and Psychosocial Oncology: Enhancing Care in the Digital Age
Jennifer Bires, Elizabeth Franklin, Krista Nelson, et al.
AI in Precision Oncology (2025)
Closed Access
An End-to-End framework for extracting observable cues of depression from diary recordings
Izidor Mlakar, Umut Ariöz, Urška Smrke, et al.
Expert Systems with Applications (2024) Vol. 257, pp. 125025-125025
Open Access | Times Cited: 3
Scoping Review on the Multimodal Classification of Depression and Experimental Study on Existing Multimodal Models
Umut Ariöz, Urška Smrke, Nejc Plohl, et al.
Diagnostics (2022) Vol. 12, Iss. 11, pp. 2683-2683
Open Access | Times Cited: 14
Research progress on the psychological burden and intervention measures in cancer patients
Hanqi Wang, Hao Lin, Bing Liu
Frontiers in Psychiatry (2024) Vol. 15
Open Access | Times Cited: 2
A review on the efficacy of artificial intelligence for managing anxiety disorders
Kheenpal Das, P. Gavade
Frontiers in Artificial Intelligence (2024) Vol. 7
Open Access | Times Cited: 2
Clinical cases on functional neurosurgery and motor speech disorders
Kostas Konstantopoulos, Dimitrios Giakoumettis
Elsevier eBooks (2023), pp. 357-418
Closed Access | Times Cited: 1
Linguistic Indicators of Depressive Symptoms in Conversations with Virtual Humans
Lucía Gómez-Zaragozá, Maria Eleonora Minissi, José Llanes-Jurado, et al.
IFIP advances in information and communication technology (2023), pp. 521-534
Closed Access | Times Cited: 1
Artificial intelligence-based detection of cancer survivors’ depression cues: A narrative review
Urška Smrke, Izidor Mlakar, Umut Ariöz, et al.
AIP conference proceedings (2024) Vol. 3030, pp. 030003-030003
Closed Access
Decoding anxiety: A scoping review of observable cues
Urška Smrke, Izidor Mlakar, Ana Rehberger, et al.
Digital Health (2024) Vol. 10
Open Access
Improving Psychiatry Services with Artificial Intelligence: Opportunities and Challenges
Hale Yapıcı Eser, M. Ballı, Ayşegül Albayrak Doğan
Turkish Journal of Psychiatry (2024)
Open Access
Artificial Intelligence in cancer survivorship care plans: what lies beyond diagnostics?
Soumya Jindal, Meemansa Jindal, Pooja Bhati
(2022), pp. 1-7
Closed Access | Times Cited: 1
An End-to-End Framework for Extracting Observable Cues of Depression from Diary Recordings (Preprint)
Izidor Mlakar, Umut Ariöz, Urška Smrke, et al.
(2023)
Closed Access
Modern Views of Machine Learning for Precision Psychiatry
Zhe Chen, Prathamesh Kulkarni, Isaac R. Galatzer‐Levy, et al.
(2022)
Open Access
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