ArticlePeerJ. Computer science2023
Explainability of deep learning models in medical video analysis: a survey.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- Enhancing Explainability in AI-Based Interstitial Lung Disease Detection via Multi-Class Learning Based on Lesion Areas.Journal of imaging informatics in medicine · 2026Article
- Echocardiographic measures read by artificial intelligence enable accurate and rapid prediction of the worsening of heart failure.European heart journal. Digital health · 2025Article
- Analysis of Inertial Measurement Unit Data for an AI-Based Physical Function Assessment System Using In-Clinic-like Movements.Bioengineering (Basel, Switzerland) · 2024Article
- An explainable long short-term memory network for surgical site infection identification.Surgery · 2024Article
- Explainable AI: Machine Learning Interpretation in Blackcurrant Powders.Sensors (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning methods have proven to be effective for multiple diagnostic tasks in medicine and have been performing significantly better in comparison to other traditional machine learning methods. However, the black-box nature of deep neural networks has restricted their use in real-world applications, especially in healthcare. Therefore, explainability of the machine learning models, which focuses on providing of the comprehensible explanations of model outputs, may affect the possibility of adoption of such models in clinical use. There are various studies reviewing approaches to explainability in multiple domains. This article provides a review of the current approaches and applications of explainable deep learning for a specific area of medical data analysis-medical video processing tasks. The article introduces the field of explainable AI and summarizes the most important requirements for explainability in medical applications. Subsequently, we provide an overview of existing methods, evaluation metrics and focus more on those that can be applied to analytical tasks involving the processing of video data in the medical domain. Finally we identify some of the open research issues in the analysed area.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.