Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model decisions are often made without reason or explanation. To reduce this gap, explainable artificial intelligence (XAI) offers a huge opportunity to receive informed decision support from deep learning models and opens the black box of the method. We conducted an explainable deep learning method based on ResNet152 combined with Grad–CAM for endoscopy image classification. We used an open-source KVASIR dataset that consisted of a total of 8000 wireless capsule images. The heat map of the classification results and an efficient augmentation method achieved a high positive result with 98.28% training and 93.46% validation accuracy in terms of medical image classification.

Automatic detection of Wireless Endoscopic Images can avoid dangerous possible diseases such as cancers. Therefore, a number of articles have been published on different methods to enhance the speed of detection and accuracy. We also present a custom version of the YOLOv4-tiny for Wireless Endoscopic Image detection and localization that uses a You Only Look Once (YOLO) version to enhance the model accuracy. We modified the YOLOv4-tiny model by replacing the CSPDarknet-53-tiny backbone structure with the Inception-ResNet-A block to enhance the accuracy of the original YOLOv4-tiny. In addition, we implemented a new custom data augmentation method to enhance the data quality, even for small datasets. We focused on maintaining the color of medical images because the sensitivity of medical images can affect the efficiency of the model. Experimental results showed that our proposed method obtains 99.4% training accuracy; compared with the previous models, this is more than a 1.2% increase. An original model used for both detection and the segmentation of medical images may cause a high error rate. In contrast, our proposed model could eliminate the error rate of the detection and localization of disease areas from wireless endoscopic images.

The importance of early detection in the management of skin lesions, such as skin cancer, cannot be overstated due to its critical role in enhancing treatment outcomes. This study presents an innovative multi-input model that fuses image and tabular data to improve the accuracy of diagnoses. The model incorporates a dual-input architecture, combining a ResNet-152 for image processing with a multilayer perceptron (MLP) for tabular data analysis. To optimize the handling of tabular data, Principal Component Analysis (PCA) is employed to reduce dimensionality, facilitating more focused and efficient model training. The model’s effectiveness is confirmed through rigorous testing, yielding impressive metrics with an F1 score of 98.91%, a recall of 99.19%, and a precision of 98.76%. These results underscore the potential of combining multiple data inputs to provide a nuanced analysis that outperforms single-modality approaches in skin lesion diagnostics.

Vision-based fall detection for smart-city CCTV must be fast, interpretable, and robust to nuisance alarms. In real surveillance scenes, false alarms are often caused by sitting, crouching, duplicate detections, short-lived pose noise, and brief posture changes that do not correspond to actual falls. This paper presents a lightweight CCTV fall-detection framework evaluated on URFD, Le2i, and UP-Fall. The proposed method combines teacher-guided distillation from ViTPose to a YOLO26s-pose student, custom person detection, full-body ROI extraction, nine interpretable posture features, Random Forest classification, tracking-based duplicate suppression, and post-event false-positive rejection. The distillation stage improves pose mAP50-95 from 66.8% to 70.9% and pose mAP50 from 88.9% to 91.2%. In the final stand/fall setting, Random Forest with false-positive rejection achieves 98.46% accuracy, 98.61% precision, 98.43% recall, and 98.52% F1-score. The main contribution of this work is a practical and interpretable surveillance framework that integrates distilled lightweight pose estimation, posture-based fall representation, and tracking-aware false-positive suppression for robust deployment-oriented fall detection.

Artificial intelligence (AI) increasingly informs smart-city services, yet residents can rarely see why an automated decision was made or correct it when it is wrong, so their participation remains passive. This study proposes a human-in-the-loop explainable AI (HITL-XAI) framework in which residents act as sensors, annotators, evaluators, and stewards of urban trees. Residents scan the QR marker panels already mounted on municipal trees to view each tree’s measured size and AI health grade with a plain-language explanation, report damage, adopt a tree, vote on priorities, and agree or disagree with the grade. Arborists verify every report and disagreement; verified errors are diagnosed by capture condition and translated into targeted training images, citizen-informed augmentation, capture metadata, and revised vision-language prompts. In a pilot in Pyeongtaek, Republic of Korea, 50 participants contributed 200 photographs and 374 participatory actions, including 41 damage reports and 23 tree adoptions; 84% rated the explanations understandable, and the System Usability Scale score was 78.4. Verified feedback exposed systematic seasonal, illumination, and weather errors, and the resulting updates raised health-grading accuracy on the citizen photographs, in an exploratory feedback-informed evaluation, from 78.5% to 89.0%, reduced referrals to inspectors from 21.0% to 9.5%, and increased citizen agreement with the grades from 81.5% to 91.0%. Explanations thus turn citizen disagreement into a verifiable signal that improves the service itself, moving participation from consultation towards verified co-evaluation of the AI service. Although limited to a single-city volunteer pilot, the approach offers cities that already tag their trees a low-cost, transferable route to AI services that residents can scrutinise and help improve.