Frontiers in Artificial Intelligence最新文献

The Clever Hans (CH) effect is a historical analogy of a horse solving mathematical problems based on some cues, representing a critical failure in artificial intelligence (AI) systems, where models achieve higher performance by utilizing spurious correlations and artifacts presented in the datasets rather than relying on causal relationships or task-related features. This effect or phenomenon is prevalent across multiple domains of AI such as computer vision, natural language processing, medical imaging, and reinforcement learning. This review examines the Clever Hans effect, the conceptual foundation of spurious correlations, and current evaluation methods that obscure such behavior. We further survey state-of-the-art detection and mitigation strategies, focusing on both model-centric and data-centric techniques. Building on these insights, we propose a roadmap for robust AI development, which includes standard benchmarking, causal integration, human-in-the-loop auditing, and transparent policy frameworks. This study underscores that addressing the Clever Hans effect is not only necessary for technical robustness but also for the ethical and responsible deployment of AI systems in real-world, high-stakes environments.

Introduction: The growing prevalence of fraud and malware, fueled by increased online activity and digital transactions, has exposed the shortcomings of conventional detection systems, particularly in handling novel or obfuscated threats, class imbalance, and high-dimensional data with many irrelevant features. This underscores the need for robust and adaptive detection methodologies.

Methods: This study proposes an advanced Fraud Detection (FD) methodology, BKOA-GOBL, that enhances the Binary Kepler Optimization Algorithm (BKOA) by integrating Ghost Opposition-Based Learning (GOBL) to improve Feature Selection (FS). The BKOA dynamically models gravitational attraction, planetary motion mechanics, and cyclic control to maintain a balance between exploration and exploitation. At the same time, the GOBL enhances broader search diversification and prevents early convergence, allowing the local optimum to be avoided. The Random Under-Sampling (RUS) technique is utilized to mitigate the class imbalance in fraud benchmarks.

Results and discussion: Experimental validation is conducted on five real-world benchmarks, including the Australian, European, CIC-MalMem-2022, Synthetic Financial Transaction Log, and Real vs Fake Job Postings datasets, using k-Nearest Neighbors (K-NN) and XGBoost (Xgb-tree) classifiers. The BKOA-GOBL achieves outstanding performance, reaching classification accuracies up to 99.96% in some benchmarks and corresponding feature reduction rates up to 81.82%. Precision, recall, ROC_AUC, and F1-scores were consistently high across most benchmarks, demonstrating reliable and balanced detection. However, some challenging benchmarks-such as the Real vs Fake Job Postings dataset using k-NN classifier-returned lower scores (Precision = 76.14%, Recall = 66.55%, F1-score = 71.00%, and ROC_AUC = 74.15%), reflecting the difficulty of the problem. Comparative analyses against 12 recent Metaheuristic Algorithms (MHAs) and Machine Learning (ML) classifiers confirmed BKOA-GOBL's dominance in terms of accuracy and computational efficiency. Its statistical superiority is confirmed by the Wilcoxon rank-sum test, underscoring its robustness, adaptability, and effectiveness in high-dimensional fraud and malware detection tasks and real-world fraud and malware detection scenarios.

Artificial intelligence (AI)-based models and algorithms may aid in achieving overall more efficient and accurate diagnostics in various medical specialties. Such AI-based tools could be integrated and potentially offer advantages over currently used diagnostic and monitoring algorithms, enabling the pursue of more individualized treatment options with potentially improved patient outcomes in the future. However, very few studies exploring the potential of AI-based tools have been reported in veterinary medicine. Diagnosis and subclassification of chronic inflammatory enteropathy (CIE) and protein-losing enteropathy (PLE), requiring an integrated approach including several diagnostic modalities, remains a challenge in clinical canine gastroenterology and might benefit from AI-based tools. Thus, we aimed to use AI-based deep learning to develop a model that can differentiate clinical cases of protein-losing PLE from non-PLE CIE using ultrasonographic (B-mode) images. This pilot study included anonymized data extracted from the electronic medical records and diagnostic images from routine diagnostic evaluations of 59 dogs. Following several optimization steps, the final model had a high accuracy (91.57%), precision (0.9286), recall (0.9070), F1 score (0.9176), and AUC-ROC (0.9529). This model was highly sensitive and specific for the detection of ultrasonographic features associated with clinicopathologic and/or histological lesions consistent with a PLE diagnosis. Combining sonographic diagnostics with machine learning yielded a high degree of accuracy in PLE differentiation. The results of this study underscore the potential of integrating an AI-based model into CIE diagnostics and PLE differentiation in clinical canine gastroenterology.

Objective: This study aimed to develop and compare machine learning (ML) models for predicting depressive symptoms in adolescents, based on teacher-reported textual descriptions of student behaviors.

Methods: Participants were 441 adolescents from Tianjin, China. Their teachers provided written reports on behavioral or emotional concerns, while the students completed the Patient Health Questionnaire-9 (PHQ-9). Text data from reports were processed using Term Frequency-Inverse Document Frequency (TF-IDF). Four ML models-Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and Least Absolute Shrinkage and Selection Operator (LASSO)-were trained and evaluated using a 80/20 data split and 5-fold cross-validation.

Results: PHQ-9 screening identified 71.7% (n = 316) of adolescents with clinically significant depressive symptoms (score ≥10). The Random Forest (RF) model demonstrated superior performance, achieving a recall of 0.97, accuracy of 0.91, precision of 0.92, and F1-score of 0.92. SVM and XGBoost also showed good performance, while LASSO was the weakest. The analysis demonstrated that teacher reports could identify depressive symptoms with up to 97% recall.

Conclusion: Machine learning, particularly Random Forest, can effectively predict adolescent depressive symptoms from teacher-reported text. This approach offers a practical and efficient tool for early identification in school settings, facilitating timely intervention.

Introduction: Brain tumors are among the most aggressive forms of cancer, requiring precise diagnosis and treatment planning to improve patient outcomes. This study aims to develop an efficient deep learning-based framework for the classification of brain tumors using MRI data.

Methods: The methodology employs Convolutional Neural Networks (CNNs) to accurately classify tumors into four categories: normal, glioma, pituitary, and meningioma. Key preprocessing techniques, including noise reduction,resizing, and data augmentation, were applied to enhance the robustness of the model. Advanced architectures such as DenseNet50, VGG19, and other transfer learning models, along with CNN variants, were trained and evaluated for their performance. Explainable AI (XAI) techniques, including Grad-CAM, LIME, and feature map visualizations, played a crucial role in providing better visualizations of the model's decision-making process and identifying areas of improvement during model training and to establish a better model.

Results: The best-performing model, a 4-conv-1-dense-1-dropout CNN, achieved a classification accuracy of 95.86%, outperforming deeper architectures and transfer learning approaches. The findings underscore the potential of deep learning models for reliable and efficient brain tumor classification. This work concludes with recommendations for real-time deployment in clinical settings and explores future integration with Large Language Models (LLMs) to generate detailed diagnostic reports.

[This corrects the article DOI: 10.3389/frai.2025.1689182.].

Precision healthcare is increasingly oriented toward the development of therapeutic strategies that are as individualized as the patients receiving them. Central to this paradigm shift is artificial intelligence (AI)-enabled multi-modal data integration, which consolidates heterogeneous data streams-including genomic, transcriptomic, proteomic, imaging, environmental, and electronic health record (EHR) data into a unified analytical framework. This integrative approach enhances early disease detection, facilitates the discovery of clinically actionable biomarkers, and accelerates rational drug development, with particularly significant implications for oncology, neurology, and cardiovascular medicine. Advanced machine learning (ML) and deep learning (DL) algorithms are capable of extracting complex, non-linear associations across data modalities, thereby improving diagnostic precision, enabling robust risk stratification, and informing patient-specific therapeutic interventions. Furthermore, AI-driven applications in digital health, such as wearable biosensors and real-time physiological monitoring, allow for continuous, dynamic refinement of treatment plans. This review examines the transformative potential of multi-modal AI in precision medicine, with emphasis on its role in multi-omics data integration, predictive modeling, and clinical decision support. In parallel, it critically evaluates prevailing challenges, including data interoperability, algorithmic bias, and ethical considerations surrounding patient privacy. The synergistic convergence of AI and multi-modal data represents not merely a technological innovation but a fundamental redefinition of individualized healthcare delivery.

Introduction: Patients with severe COVID-19 may require MV or ECMO. Predicting who will require interventions and the duration of those interventions are challenging due to the diverse responses among patients and the dynamic nature of the disease. As such, there is a need for better prediction of the duration and outcomes of MV use in patients, to improve patient care and aid with MV and ECMO allocation. Here we develop and examine the performance of ML models to predict MV duration, ECMO, and mortality for patients with COVID-19.

Methods: In this retrospective prognostic study, hierarchical machine-learning models were developed to predict MV duration and outcome prediction from demographic data and time-series data consisting of vital signs and laboratory results. We train our models on 10,378 patients with positive severe acute respiratory syndrome-related coronavirus (SARS-CoV-2) virus testing from Emory's COVID CRADLE Dataset who sought treatment at Emory University Hospital between February 28, 2020, to January 24, 2022. Analysis was conducted between January 10, 2022, and April 5, 2024. The main outcomes and measures were the AUROC, AUPRC and the F-score for MV duration, need for ECMO, and mortality prediction.

Results: Data from 10,378 patients with COVID-19 (median [IQR] age, 60 [48-72] years; 5,281 [50.89%] women) were included. Overall MV class distributions for 0 days, 1-4 days, 5-9 days, 10-14 days, 15-19 days, 20-24 days, 25-29 days, and ≥30 days of MV were 8,141 (78.44%), 812 (7.82%), 325 (3.13%), 241 (2.32%), 153 (1.47%), 97 (0.93%), 87 (0.84%), and 522 (5.03%), respectively. Overall ECMO use and mortality rates were 15 (0.14%) and 1,114 (10.73%), respectively. On MV duration, ECMO use, and mortality outcomes, the highest-performing model reached weighted average AUROC scores of 0.873, 0.902, and 0.774, and the highest-performing model reached weighted average AUPRC scores of 0.790, 0.999, and 0.893.

Conclusions and relevance: Hierarchical ML models trained on vital signs, laboratory results, and demographic data show promise for the prediction of MV duration, ECMO use, and mortality in COVID-19 patients.