ArticleIEEE transactions on radiation and plasma medical sciences2021
On Interpretability of Artificial Neural Networks: A Survey.
Article in IEEE transactions on radiation and plasma medical sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06878469 (SIMPLIF-AI), which is not on this map. Cited by 72 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
SIMPLIF-AI: SIMPLIFication and Standardization of Care Pathways for Patients With Rare Brain Tumors Through Artificial Intelligence
Who cites it
72 citing papers in PubMed.
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- Technical Review of Magnetic Resonance Fingerprinting Applications in Cerebral Physiology.Magnetic resonance in medicine · 2026Review
- Robust and interpretable unit level causal inference in neural networks for pediatric myopia.NPJ digital medicine · 2026Article
- Healthcare applications of 0-1 neural networks in prescriptive problems with observational data.Health care management science · 2026Article
- Compact and Interpretable Neural Networks Using Lehmer Activation Units.Entropy (Basel, Switzerland) · 2026Article
- Machine Learning-Driven Risk Prediction Models for Posthepatectomy Liver Failure: A Narrative Review.Medicina (Kaunas, Lithuania) · 2026Review
- A reinforcement learning-guided interpretable method for postoperative sepsis prediction with Hilbert-Schmidt Independence Criterion.Frontiers in big data · 2026Article
- Interpretable crop pest and disease identification based on comparative concept tree.PloS one · 2026Article
- Evaluating machine learning models for clothing size prediction using anthropometric measurements from 3D body scanning.Scientific reports · 2025Article
- [Research progress on deep learning-based computer-aided diagnosis of thyroid nodules using ultrasound imaging].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Review
- Prediction of Standard Combustion Enthalpy of Organic Compounds Combining Machine Learning and Chemical Graph Theory: A Strategy.ACS omega · 2025Article
- Machine learning-assisted finite element modeling of additively manufactured meta-materials.3D printing in medicine · 2025Article
- Machine learning models for predicting multimorbidity trajectories in middle-aged and elderly adults.Scientific reports · 2025Article
- Building a neural network model to define DNA sequence specificity in V(D)J recombination.Nucleic acids research · 2025Article
- Universal multilayer network embedding reveals a causal link between GABA neurotransmitter and cancer.BMC bioinformatics · 2025Article
- Beyond human perception: challenges in AI interpretability of orangutan artwork.Primates; journal of primatology · 2025Article
- Balancing Performance and Interpretability in Medical Image Analysis: Case study of Osteopenia.Journal of imaging informatics in medicine · 2025Article
- Exploring the Unseen: A Survey of Multi-Sensor Fusion and the Role of Explainable AI (XAI) in Autonomous Vehicles.Sensors (Basel, Switzerland) · 2025Review
12 more citing papers are in PubMed but not listed here.
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
Abstract
Deep learning as represented by the artificial deep neural networks (DNNs) has achieved great success recently in many important areas that deal with text, images, videos, graphs, and so on. However, the black-box nature of DNNs has become one of the primary obstacles for their wide adoption in mission-critical applications such as medical diagnosis and therapy. Because of the huge potentials of deep learning, increasing the interpretability of deep neural networks has recently attracted much research attention. In this paper, we propose a simple but comprehensive taxonomy for interpretability, systematically review recent studies in improving interpretability of neural networks, describe applications of interpretability in medicine, and discuss possible future research directions of interpretability, such as in relation to fuzzy logic and brain science.
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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.