The review, led by Yosra Didi together with Ahlem Walha and Ali Wali, does not simply catalog architectures and accuracy figures. Instead, the authors organize the literature through a structured five-dimensional qualitative analytical framework covering methodological rigor, clinical relevance, technical validity, reproducibility evidence and imaging-specific considerations. Crucially, the team is transparent that this framework is a lens for structured qualitative comparison rather than a quantitative scoring system, a deliberate choice that reflects how difficult it is to compare studies whose experimental designs differ so widely. By attending explicitly to methodological context, the review aims to explain why results that look comparable on paper may not be comparable in practice.

Technically, the field the authors survey is dominated by convolutional neural networks, the deep learning family that has become the workhorse of medical image analysis. The reviewed studies trace a clear evolutionary arc: early work relied on classic backbones such as VGG, ResNet, Inception and Xception, typically fine-tuned on ImageNet pre-trained weights through transfer learning, while more recent efforts embrace efficient architectures like MobileNet and EfficientNet for deployment-friendly inference, capsule networks for preserving spatial hierarchies, and vision transformers such as the Swin transformer that replace convolutional inductive biases with attention-based global context. Ensemble methods, which combine predictions from multiple networks to squeeze out additional accuracy, appear repeatedly, as do hybrid pipelines that pair deep feature extractors with classical classifiers such as support vector machines, k-nearest neighbors and random forests.

Alongside pure classification, the review documents a substantial strand of work built on segmentation networks, including U-Net and V-Net, that first delineate lungs, heart borders or lesion regions before classification proceeds. Studies of cardiomegaly detection, tuberculosis localization and COVID-19 severity assessment illustrate why this matters: segmenting the anatomical structure of interest can focus a model’s attention on clinically relevant tissue and reduce the risk that a network learns shortcuts from irrelevant image regions. Attention mechanisms and explainable AI overlays, such as probability maps and saliency-based visualizations, are increasingly bolted onto these pipelines in an effort to make black-box predictions legible to clinicians, though the review notes that interpretability claims are rarely tested against actual radiologist reasoning.

The data underpinning all of this is a familiar roster of public benchmark datasets: ChestX-ray8 and its successors, CheXpert, PadChest, MIMIC-CXR, VinDr-CXR, the Montgomery and Shenzhen tuberculosis collections, the Kaggle pneumonia archives, and a wave of COVID-19 repositories assembled during the pandemic, including the COVID-19 Radiography Database, BIMCV-COVID-19+, and the RSNA Ricord releases. The authors find that studies evaluated on a single dataset report systematically more optimistic performance than those subjected to multi-dataset evaluation regimes, a pattern consistent with models overfitting to the imaging characteristics, label conventions and patient populations of one institution. When the same architecture is tested across hospitals, scanners and countries, performance differences between datasets emerge that single-cohort experiments never reveal.

Underlying dataset limitations compound the problem. Many widely used chest X-ray corpora carry labels derived automatically from radiology reports rather than from independent expert adjudication, introducing label noise that caps achievable accuracy in ways rarely quantified. Class imbalance, uncertain and missing labels, and demographic and geographic skew all recur across the reviewed corpus, and the reviewers highlight that such weaknesses directly affect diagnostic reliability. Data augmentation, including generative approaches based on convolutional GANs and techniques such as CLAHE contrast enhancement, is frequently deployed to compensate, but the review observes that augmentation choices are rarely ablated systematically, leaving it unclear how much of a reported gain comes from the model and how much from the preprocessing recipe.

Perhaps the most damning findings concern experimental hygiene. Across a substantial share of the 81 studies, the reviewers document inconsistent experimental design choices, non-standardized preprocessing pipelines and limited ablation reporting, making it genuinely difficult to determine which technical innovations actually drive performance. Preprocessing steps ranging from resizing and normalization to lung cropping and denoising vary from paper to paper, sometimes within evaluations of the same architecture, and hyperparameter details are often underreported. The reproducibility picture is similarly uneven: code and trained weights are infrequently released, and the review points to established reporting standards, such as the CLAIM checklist for artificial intelligence in medical imaging, as tools that remain underused despite being designed precisely to address these gaps.

The scarcity of external clinical validation emerges as the review’s central concern. Very few of the surveyed systems have been tested on data from institutions absent from training, and fewer still have been evaluated in genuine clinical workflows with radiologists in the loop. The authors draw an implicit contrast with landmark analyses, such as the widely cited Nature Machine Intelligence study cataloging common pitfalls in COVID-19 machine learning research, which documented how data leakage and flawed study design produced models that collapsed outside their original datasets. Against that backdrop, the pattern the Tunisian team identifies, apparent performance differences between single-dataset and multi-dataset regimes paired with limited external validation, reads as a warning that laboratory excellence has not yet translated into deployable trust.

Still, the review is constructive rather than dismissive. Based on architectural cross-comparisons and a qualitative audit of reproducibility practices, the authors consolidate recommendations for the field: adopt multi-dataset and external validation as the default rather than the exception, standardize preprocessing and reporting so that results can be meaningfully compared, report ablations that isolate the contribution of each design choice, and design studies around clinical relevance from the outset, including the disease prevalence, co-morbidities and imaging conditions of the intended deployment setting. They also flag the open challenges that will define the next phase of the field, chief among them generalization across populations and scanners, evaluation standardization, and the practical integration of these models into radiology workflows.

The stakes could hardly be higher. Chest radiography remains one of the most performed imaging examinations in the world, and in many health systems, particularly in low- and middle-income countries where the review’s tuberculosis-focused literature is concentrated, a reliable AI triage tool could extend diagnostic capacity where radiologists are scarce. The review’s authors, who report no funding and no conflicts of interest, analyzed only publicly available published studies and generated no new datasets of their own. Their message to the community is ultimately one of disciplined optimism: the technical machinery for AI-assisted chest X-ray interpretation is mature and improving rapidly, but the evidence base must be rebuilt on foundations of external validation, transparent reporting and standardized evaluation before these systems can responsibly share the reading room with human experts.

Subject of Research: AI-driven thoracic disease classification in chest radiography

Article Title: AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis

Article References: Didi, Y., Walha, A., & Wali, A. (2026). AI-driven techniques for thoracic disease classification in chest radiography: A comprehensive review and critical analysis. Neural Computing and Applications, 38(17), Article 737. https://doi.org/10.1007/s00521-026-12457-6

Image Credits: AI Generated

DOI: 10.1007/s00521-026-12457-6

Keywords: artificial intelligence, deep learning, chest X-ray, thoracic disease classification, convolutional neural networks, medical imaging, pneumonia detection, tuberculosis, COVID-19, transfer learning, clinical validation, reproducibility