ArticleJournal of medical Internet research2020
Limitations of Deep Learning Attention Mechanisms in Clinical Research: Empirical Case Study Based on the Korean Diabetic Disease Setting.
Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic study on blockchain technology for genomic data management in healthcare: approaches, challenges, and research opportunities.Medical & biological engineering & computing · 2026Pooled it
- 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 · 2025Pooled it
- Multi-dimensional attention framework for personalised Alzheimer's disease progression prediction across sporadic and genetic risk cohorts.Research square · 2026Article
- Weak supervision of H&E slides reveals systems-level biology and functional states that govern therapeutic resistance.bioRxiv : the preprint server for biology · 2026Article
- Evaluation Large Language Models' Time Dependent Consistency in Aesthetic Surgery Consultations and Comparison of Their Performance Across Different Clinical Domains.Aesthetic plastic surgery · 2026Article
- AirSeg: Learnable Interconnected Attention Framework for Robust Airway Segmentation.Journal of imaging informatics in medicine · 2026Article
- Enhancing pediatric distal radius fracture detection: optimizing YOLOv8 with advanced AI and machine learning techniques.BMC medical imaging · 2025Article
- Enhancing signal-to-noise ratio in real-time LED-based photoacoustic imaging: A comparative study of CNN-based deep learning architectures.Photoacoustics · 2025Article
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- Prototype Learning for Medical Time Series Classification via Human-Machine Collaboration.Sensors (Basel, Switzerland) · 2024Article
- Multi-modal image classification of COVID-19 cases using computed tomography and X-rays scans.Intelligent systems with applications · 2023Article
- 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 · 2023Article
- Artificial intelligence in cardiology: Hope for the future and power for the present.Frontiers in cardiovascular medicine · 2022Review
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9 authors.
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Abstract
backgroundDespite excellent prediction performance, noninterpretability has undermined the value of applying deep-learning algorithms in clinical practice. To overcome this limitation, attention mechanism has been introduced to clinical research as an explanatory modeling method. However, potential limitations of using this attractive method have not been clarified to clinical researchers. Furthermore, there has been a lack of introductory information explaining attention mechanisms to clinical researchers.
objectiveThe aim of this study was to introduce the basic concepts and design approaches of attention mechanisms. In addition, we aimed to empirically assess the potential limitations of current attention mechanisms in terms of prediction and interpretability performance.
methodsFirst, the basic concepts and several key considerations regarding attention mechanisms were identified. Second, four approaches to attention mechanisms were suggested according to a two-dimensional framework based on the degrees of freedom and uncertainty awareness. Third, the prediction performance, probability reliability, concentration of variable importance, consistency of attention results, and generalizability of attention results to conventional statistics were assessed in the diabetic classification modeling setting. Fourth, the potential limitations of attention mechanisms were considered.
resultsPrediction performance was very high for all models. Probability reliability was high in models with uncertainty awareness. Variable importance was concentrated in several variables when uncertainty awareness was not considered. The consistency of attention results was high when uncertainty awareness was considered. The generalizability of attention results to conventional statistics was poor regardless of the modeling approach.
conclusionsThe attention mechanism is an attractive technique with potential to be very promising in the future. However, it may not yet be desirable to rely on this method to assess variable importance in clinical settings. Therefore, along with theoretical studies enhancing attention mechanisms, more empirical studies investigating potential limitations should be encouraged.
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