ArticlePeerJ. Computer science2023
Understanding the black-box: towards interpretable and reliable deep learning models.
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 30 papers, 1 of them a synthesis that pooled it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Multichannel deep learning-based MRI model for predicting breast cancer axillary lymph node invasion: a comparative study to Node-RADS.Quantitative imaging in medicine and surgery · 2026Article
- Physiological Data Integration and Predictive Modeling in Intensive Care.Life (Basel, Switzerland) · 2026Review
- Feature Selection and Machine Learning Strategies for CT Radiomics-Based Survival Prediction in Non-Small Cell Lung Cancer: A Comparative Study.Diagnostics (Basel, Switzerland) · 2026Article
- An interpretable machine learning model predicts frailty risk in middle-aged and older adults with gastrointestinal disease: a longitudinal study.Scientific reports · 2026Article
- AI-derived constrained conditional model for screening marker genes through integrated high-throughput transcriptome big data.BMC medical research methodology · 2026Article
- Evaluating network-based algorithms for protein function prediction using plant protein-protein interaction networks.BMC plant biology · 2026Article
- Article
- NetG2P: Network-based genotype-to-phenotype transformation identifies key signaling crosstalk for prognosis in pan-cancer study.BMC biology · 2026Article
- Review
- Diagnostic performance of multimodal ultrasound-based deep learning models in differentiating benign and malignant thyroid nodules.Frontiers in oncology · 2026Article
- Opening the black box: insights into ubiquitin-mediated control of innate antiviral immunity and AI-enhanced therapeutics.Frontiers in immunology · 2026Review
- Artificial Intelligence for Antimicrobial Resistance Detection and Prediction inInfection and drug resistance · 2026Review
- Machine learning-based diagnostic models for prevalent metabolic syndrome among patients with disuse muscle atrophy: a comparative study of seven algorithms.Frontiers in endocrinology · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Artificial intelligence analysis of OCT biomarkers to predict visual outcomes following vitrectomy for epiretinal membrane.International journal of retina and vitreous · 2025Article
- New insights into translational research in Alzheimer's disease guided by artificial intelligence, computational and systems biology.Acta pharmaceutica Sinica. B · 2025Review
- Gaining insights into epigenetic memories through artificial intelligence and omics science in plants.Journal of integrative plant biology · 2025Review
- Spectroscopic and Imaging Technologies Combined with Machine Learning for Intelligent Perception of Pesticide Residues in Fruits and Vegetables.Foods (Basel, Switzerland) · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Deep learning (DL) has revolutionized the field of artificial intelligence by providing sophisticated models across a diverse range of applications, from image and speech recognition to natural language processing and autonomous driving. However, deep learning models are typically black-box models where the reason for predictions is unknown. Consequently, the reliability of the model becomes questionable in many circumstances. Explainable AI (XAI) plays an important role in improving the transparency and interpretability of the model thereby making it more reliable for real-time deployment. To investigate the reliability and truthfulness of DL models, this research develops image classification models using transfer learning mechanism and validates the results using XAI technique. Thus, the contribution of this research is twofold, we employ three pre-trained models VGG16, MobileNetV2 and ResNet50 using multiple transfer learning techniques for a fruit classification task consisting of 131 classes. Next, we inspect the reliability of models, based on these pre-trained networks, by utilizing Local Interpretable Model-Agnostic Explanations, the LIME, a popular XAI technique that generates explanations for the predictions. Experimental results reveal that transfer learning provides optimized results of around 98% accuracy. The classification of the models is validated on different instances using LIME and it was observed that each model predictions are interpretable and understandable as they are based on pertinent image features that are relevant to particular classes. We believe that this research gives an insight for determining how an interpretation can be drawn from a complex AI model such that its accountability and trustworthiness can be increased.
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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.