This paper is in the following e-collection/theme issue:

Artificial Intelligence (2129) Clinical Informatics (1325) Machine Learning (2076)

Published on in Vol 22, No 12 (2020): December

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/18418, first published .
Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting

Authors of this article:

Junetae Kim1, 2, 3 Author Orcid Image ;   Sangwon Lee2 Author Orcid Image ;   Eugene Hwang4 Author Orcid Image ;   Kwang Sun Ryu2 Author Orcid Image ;   Hanseok Jeong1 Author Orcid Image ;   Jae Wook Lee5 Author Orcid Image ;   Yul Hwangbo3, 6 Author Orcid Image ;   Kui Son Choi1, 2 Author Orcid Image ;   Hyo Soung Cha1, 2 Author Orcid Image
Article Authors Cited by (27) Tweetations (6) Metrics

Journals

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  2. Tran T, Lee J, Gunathilake M, Kim J, Kim S, Cho H, Kim J. A comparison of machine learning models and Cox proportional hazards models regarding their ability to predict the risk of gastrointestinal cancer based on metabolic syndrome and its components. Frontiers in Oncology 2023;13 View
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  4. Karatzia L, Aung N, Aksentijevic D. Artificial intelligence in cardiology: Hope for the future and power for the present. Frontiers in Cardiovascular Medicine 2022;9 View
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  6. Hwang E, Park H, Kim H, Kim J, Jeong H, Kim J, Kim S. Development of a Bispectral index score prediction model based on an interpretable deep learning algorithm. Artificial Intelligence in Medicine 2023;143:102569 View
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  10. Manh V, Jia X, Xue W, Xu W, Mei Z, Dong Y, Zhou J, Huang R, Ni D. An efficient framework for lesion segmentation in ultrasound images using global adversarial learning and region-invariant loss. Computers in Biology and Medicine 2024;171:108137 View
  11. Xie J, Wang Z, Yu Z, Ding Y, Guo B. Prototype Learning for Medical Time Series Classification via Human–Machine Collaboration. Sensors 2024;24(8):2655 View
  12. Gong K, Xue Y, Kong L, Xie X. Cost prediction for ischemic heart disease hospitalization: Interpretable feature extraction using network analysis. Journal of Biomedical Informatics 2024;154:104652 View
  13. ŞAHiN E, Arslan N, Özdemir D. Unlocking the black box: an in-depth review on interpretability, explainability, and reliability in deep learning. Neural Computing and Applications 2025;37(2):859 View
  14. Paul A, Mallidi S. Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures. Photoacoustics 2025;41:100674 View
  15. Yu B, Zhang H, Zhang M, Rathnayake U. Deep learning-based differential gut flora for prediction of Parkinson’s. PLOS ONE 2025;20(1):e0310005 View
  16. Ajith S, Vijayakumar S, Elakkiya N. Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorithms. Discover Food 2025;5(1) View
  17. Rahman A, Kundu D, Debnath T, Rahman M, Das U, Miah A, Muhammad G. From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook. Expert Systems 2025;42(6) View
  18. Naveen K, Anoop B, Siju K, Kar M, Venugopal V. EffNet-SVM: A Hybrid Model for Diabetic Retinopathy Classification Using Retinal Fundus Images. IEEE Access 2025;13:79793 View
  19. Krishnan C, Hussain S, Stanford D, Sthanam V, Bodduluri S, Raju S, Rowe S, Kim H. AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation. Journal of Imaging Informatics in Medicine 2025 View
  20. Baaklini M, Valdés Hernández M. Deep learning for MRI-based acute and subacute ischaemic stroke lesion segmentation—a systematic review, meta-analysis, and pilot evaluation of key results. Frontiers in Medical Technology 2025;7 View
  21. Wang C, Yang J, He Q. Event extraction for visual: De-biasing with causality-guided attention mechanism. Neurocomputing 2025;649:130783 View
  22. Amirouche F, Prosper A, Mzeihem M. Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques. BMC Medical Imaging 2025;25(1) View
  23. Tang Z, Yang D, Tan L, Zeng L. Deep learning-based time series forecasting for bearing remaining useful life: Recent advances, hybrid architectures, and targeted enhancements. Engineering Applications of Artificial Intelligence 2025;162:112457 View

Books/Policy Documents

  1. Zheng J, Zhuang Z, Liao T, Chen L. Proceedings of the 12th International Conference on Computer Engineering and Networks. View
  2. Tiwari M, Waoo A. Revolutionizing Healthcare: AI Integration with IoT for Enhanced Patient Outcomes. View
  3. Suveena S, Rekha A, Rani J, V. Oommen O, Ramakrishnan R. Revolutionizing Drug Discovery:Cutting-Edge Computational Techniques. View

Conference Proceedings

  1. Nivetha B, Gomathi P, Abirami T, Nanjundan M, Raja G, Gowsika B. 2025 6th International Conference on Inventive Research in Computing Applications (ICIRCA). Transformer-Based Multi-Modal Deep Learning Framework for Early Disease Prognosis Using EHR and Medical Imaging Data View