Intelligence-based medicine最新文献

{"title":"Design and implementation of a low-cost malaria diagnostic system based on convolutional neural network","authors":"Ekobo Akoa Brice ,&nbsp;Ndoumbe Jean ,&nbsp;Mohamadou Madina","doi":"10.1016/j.ibmed.2025.100272","DOIUrl":"10.1016/j.ibmed.2025.100272","url":null,"abstract":"<div><div>This work focuses on the design and implementation of an intelligent system that can diagnose malaria from blood smear images. This system takes data in the image format and provides an instant and automated diagnosis to output the result of the patient’s condition on a screen. The methodology for achieving the system is based on the CNN (convolutional neural network). The latter has the specificity to function as a feature extractor and image classifier. The software part thus obtained is implemented in an electronic device that serves as a kit mounted with our care. The establishment of such a system has innumerable assets, such as rapidity during diagnosis by a laboratory technician or not; its portability that will facilitate its use wherever needed. From an ergonomic and functional point of view, the system has a real impact in the diagnosis of a large-scale malaria endemic. The CNN was trained on a large dataset of blood smears and was able to accurately classify infected and uninfected samples with high sensitivity and specificity. Insofar as the system carried out after testing on several samples reaches an average sensitivity of 89.50% and an average precision of 89%, this improves decision-making on the diagnosis of malaria. The system thus created allows malaria to be diagnosed at low cost from blood smear images. The use of CNNs in this project has the advantage of automatically extracting features from blood smear images and classifying them efficiently. The major advantage of the proposed system is its portability and lower cost. The performance of the proposed algorithm was evaluated on a publicly available malaria data set.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100272"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144596406","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Arkangel AI: A conversational agent for real-time, evidence-based medical question-answering","authors":"Maria Camila Villa,&nbsp;Natalia Castano-Villegas,&nbsp;Isabella Llano,&nbsp;Julian Martinez,&nbsp;Maria Fernanda Guevara,&nbsp;Jose Zea,&nbsp;Laura Velásquez","doi":"10.1016/j.ibmed.2025.100274","DOIUrl":"10.1016/j.ibmed.2025.100274","url":null,"abstract":"<div><h3>Introduction</h3><div>Large Language Models (LLMs) have been trained and tested on several medical question-answering (QA) datasets built from medical licensing exams and natural interactions between doctors and patients to fine-tune them for specific health-related tasks.</div></div><div><h3>Objective</h3><div>We aimed to develop LLM-powered Conversational Agents (CAs) equipped to produce fast, accurate, and real-time responses to medical queries in different clinical and scientific scenarios. This paper presents Arkangel AI, our first conversational agent and research assistant.</div></div><div><h3>Methods</h3><div>The model is based on a system containing five LLMs; each is classified within a specific workflow with pre-defined instructions to produce the best search strategy and provide evidence-based answers. We assessed accuracy, intra/inter-class variability, and Cohen's Kappa using the question-answer (QA) dataset MedQA. Additionally, we used the PubMedQA dataset and assessed both databases using the RAGAS framework, including Context, Response Relevance, and Faithfulness. Traditional statistical analysis was performed with hypothesis tests and 95 % IC.</div></div><div><h3>Results</h3><div>Accuracy for MedQA (n: 1273) was 90.26 % and Cohen's kappa was 87 %, surpassing current SoTAs for other LLMs (GPT-4o, MedPaLM2). The model retrieved 80 % of the expected articles and provided relevant answers in 82 % of PubMedQA.</div></div><div><h3>Conclusion</h3><div>Arkangel AI showed proficient retrieval and reasoning abilities and unbiased responses. Evenly distributed medical QA datasets to train improved LLMs and external validation for the model with real-world physicians in clinical scenarios are needed. Clinical decision-making remains in the hands of trained healthcare professionals.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100274"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829353","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach in predicting early antenatal care initiation at first trimester among reproductive women in Somalia: an analysis with SHAP explanations","authors":"Jamilu Sani ,&nbsp;Mohamed Mustaf Ahmed","doi":"10.1016/j.ibmed.2025.100252","DOIUrl":"10.1016/j.ibmed.2025.100252","url":null,"abstract":"<div><h3>Introduction</h3><div>Timely antenatal care (ANC) initiation is essential for maternal and neonatal health, enabling the early detection of risks and ensuring optimal care. In Somalia, delayed initiation of ANC poses a significant health risk. This study applied machine learning (ML) models to predict early ANC initiation among Somali women and identify key predictors using SHapley Additive exPlanations (SHAP).</div></div><div><h3>Methods</h3><div>Data from the 2020 Somali Health and Demographic Survey were analyzed, focusing on ANC timing in 3138 women aged 15–49. Six ML models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and XGBoost) were assessed for accuracy, precision, recall, F1-score, and AUROC. Feature importance was evaluated using SHAP to interpret the influence of each predictor.</div></div><div><h3>Results</h3><div>Random Forest achieved the highest performance, with an accuracy of 70 %, precision of 0.69, recall of 0.71, and AUROC of 0.74, closely followed by XGBoost, which achieved an accuracy of 69 % and AUROC of 0.72. SHAP analysis identified the place of delivery, residence, and age group as the most influential predictors of early ANC initiation, with the number of births in the past five years showing a significant negative impact.</div></div><div><h3>Conclusion</h3><div>Machine learning models, particularly Random Forest and XGBoost, effectively predicted early ANC initiation, highlighting significant demographic and healthcare access-related predictors. These findings suggest targeted interventions focusing on delivery location preferences, residential factors, and age-specific approaches to improve early ANC attendance in Somalia.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100252"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877377","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fully automatic content-aware tiling pipeline for pathology whole slide images","authors":"Falah Jabar ,&nbsp;Lill-Tove Rasmussen Busund ,&nbsp;Biagio Ricciuti ,&nbsp;Masoud Tafavvoghi ,&nbsp;Thomas K. Kilvaer ,&nbsp;David J. Pinato ,&nbsp;Mette Pøhl ,&nbsp;Sigve Andersen ,&nbsp;Tom Donnem ,&nbsp;David J. Kwiatkowski ,&nbsp;Mehrdad Rakaee","doi":"10.1016/j.ibmed.2025.100318","DOIUrl":"10.1016/j.ibmed.2025.100318","url":null,"abstract":"<div><div>Tiling (or patching) histology Whole Slide Images (WSIs) is a required initial step in the development of deep learning (DL) models. Gigapixel-scale WSIs must be divided into smaller, manageable image tiles. Standard WSI tiling techniques often exclude diagnostically important tissue regions or include regions with artifacts such as folds, blurs, and pen-markings, which can significantly degrade DL model performance and analysis. This paper introduces WSI-SmartTiling, a fully automated, deep learning-based, content-aware WSI tiling pipeline designed to include maximal information content from WSI. A supervised DL model for artifact detection was developed using pixel-based semantic segmentation at high magnification (20× and 40x) to classify WSI regions as either artifacts or qualified tissue. The model was trained on a diverse dataset and validated using both internal and external datasets. Quantitative and qualitative evaluations demonstrated its superiority, outperforming state-of-the-art methods with accuracy, precision, recall, and F1 scores exceeding 95 % across all artifact types, along with Dice scores above 94 %. In addition, WSI-SmartTiling integrates a generative adversarial network model to reconstruct tissue regions obscured by pen-markings in various colors, ensuring relevant valuable areas are preserved. Lastly, while excluding artifacts, the pipeline efficiently tiles qualified tissue regions with minimum tissue loss.</div><div>In conclusion, this high-resolution preprocessing pipeline can significantly improve pathology WSI-based feature extraction and DL-based classification by minimizing tissue loss and providing high-quality – artifact-free – tissue tiles. The WSI-SmartTiling pipeline is publicly available on <span><span>GitHub</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100318"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145683783","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI speechbots and 3D segmentations in virtual reality improve radiology on-call training in resource-limited settings","authors":"Yusuf Alibrahim ,&nbsp;Muhieldean Ibrahim ,&nbsp;Devindra Gurdayal ,&nbsp;Muhammad Munshi","doi":"10.1016/j.ibmed.2025.100245","DOIUrl":"10.1016/j.ibmed.2025.100245","url":null,"abstract":"<div><h3>Objective</h3><div>Evaluate the use of large-language model (LLM) speechbot tools and deep learning-assisted generation of 3D reconstructions when integrated in a virtual reality (VR) setting to teach radiology on-call topics to radiology residents.</div></div><div><h3>Methods</h3><div>Three first year radiology residents in Guyana were enrolled in an 8-week radiology course that focused on preparation for on-call duties. The course, delivered via VR headsets with custom software integrating LLM-powered speechbots trained on imaging reports and 3D reconstructions segmented with the help of a deep learning model. Each session focused on a specific radiology area, employing a didactic and case-based learning approach, enhanced with 3D reconstructions and an LLM-powered speechbot. Post-session, residents reassessed their knowledge and provided feedback on their VR and LLM-powered speechbot experiences.</div></div><div><h3>Results/discussion</h3><div>Residents found that the 3D reconstructions segmented semi-automatically by deep learning algorithms and AI-driven self-learning via speechbot was highly valuable. The 3D reconstructions, especially in the interventional radiology session, were helpful and the benefit is augmented by VR where navigating the models is seamless and perception of depth is pronounced. Residents also found conversing with the AI-speechbot seamless and was valuable in their post session self-learning. The major drawback of VR was motion sickness, which was mild and improved over time.</div></div><div><h3>Conclusion</h3><div>AI-assisted VR radiology education could be used to develop new and accessible ways of teaching a variety of radiology topics in a seamless and cost-effective way. This could be especially useful in supporting radiology education remotely in regions which lack local radiology expertise.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100245"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BreastCare application: Moroccan Breast cancer diagnosis through deep learning-based image segmentation and classification","authors":"Nouhaila Erragzi ,&nbsp;Nabila Zrira ,&nbsp;Safae Lanjeri ,&nbsp;Youssef Omor ,&nbsp;Anwar Jimi ,&nbsp;Ibtissam Benmiloud ,&nbsp;Rajaa Sebihi ,&nbsp;Rachida Latib ,&nbsp;Nabil Ngote ,&nbsp;Haris Ahmad Khan ,&nbsp;Shah Nawaz","doi":"10.1016/j.ibmed.2025.100254","DOIUrl":"10.1016/j.ibmed.2025.100254","url":null,"abstract":"<div><div>Breast cancer remains a critical health problem worldwide. Increasing survival rates requires early detection. Accurate classification and segmentation are crucial for effective diagnosis and treatment. Although breast imaging modalities offer many advantages for the diagnosis of breast cancer, the interpretation of breast ultrasound images has always been a vital issue for physicians and radiologists due to misdiagnosis. Moreover, detecting cancer at an early stage increases the chances of survival. This article presents two approaches: Attention-DenseUNet for the segmentation task and EfficientNetB7 for the classification task using public datasets: BUSI, UDIAT, BUSC, BUSIS, and STUHospital. These models are proposed in the context of Computer-Aided Diagnosis (CAD) for breast cancer detection. In the first study, we obtained an impressive Dice coefficient for all datasets, with scores of 88.93%, 95.35%, 92.79%, 93.29%, and 94.24%, respectively. In the classification task, we achieved a high accuracy using only four public datasets that include the two classes benign and malignant: BUSI, UDIAT, BUSC, and BUSIS, with an accuracy of 97%, 100%, 99%, and 94%, respectively. Generally, the results show that our proposed methods are considerably better than other state-of-the-art methods, which will undoubtedly help improve cancer diagnosis and reduce the number of false positives. Finally, we used the suggested approaches to create “Moroccan BreastCare”, an advanced breast cancer segmentation and classification software that automatically processes, segments, and classifies breast ultrasound images.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100254"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143943162","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Skin disease classification using transfer learning model and fusion strategy","authors":"YA-Ching Yang ,&nbsp;Wu-Chun Chung ,&nbsp;Chun-Ying Wu ,&nbsp;Che-Lun Hung ,&nbsp;Yi-Ju Chen","doi":"10.1016/j.ibmed.2025.100271","DOIUrl":"10.1016/j.ibmed.2025.100271","url":null,"abstract":"<div><div>Inflammatory skin diseases often display overlapping visual features, making accurate diagnosis challenging. This study proposes a deep learning framework combining transfer learning, feature fusion, and adaptive ensemble strategies to improve dermatological image classification. Using MobileNetV3-Large as the backbone, expert-defined anatomical metadata and model-derived probabilities were fused to enrich diagnostic features. A fuzzy rank-based ensemble aggregated predictions across multiple regions of interest (ROIs), prioritizing classifier confidence dynamically. The approach achieved consistent performance across ROI settings, with F1-scores reaching 0.8. These findings demonstrate that integrating anatomical context with deep learning enhances the interpretability and diagnostic utility of automated dermatological systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100271"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144563717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging Conv-XGBoost algorithm for perceived mental stress detection using Photoplethysmography","authors":"Geethu S. Kumar,&nbsp;B. Ankayarkanni","doi":"10.1016/j.ibmed.2025.100209","DOIUrl":"10.1016/j.ibmed.2025.100209","url":null,"abstract":"<div><div>Stress detection is crucial for monitoring mental health and preventing stress-related disorders. Real-time stress detection shows promise with photoplethysmography (PPG), a non-invasive optical technology that analyzes blood volume changes in the microvascular bed of tissue. This study introduces a novel hybrid model, Conv-XGBoost, which combines Convolutional Neural Networks (CNN) and eXtreme Gradient Boosting (XGBoost) to improve the accuracy and robustness of stress detection from PPG signals. The Conv-XGBoost model utilizes the feature extraction capabilities of CNNs to process PPG signals, converting them into spectrograms that capture the time–frequency characteristics of data. The XGBoost component is essential for handling the complex, high-dimensional feature sets provided by the CNN, enhancing prediction capabilities through gradient boosting. This customized approach addresses the limitations of traditional machine learning algorithms in dealing with hand-crafted features. The Pulse Rate Variability-based Photoplethysmography dataset was chosen for training and validation. The outcomes of the experiments revealed that the proposed Conv-XGBoost model outperformed more conventional machine learning techniques with a training accuracy of 98.87%, validation accuracy of 93.28% and an F1-score of 97.25%. Additionally, the model demonstrated superior resilience to noise and variability in PPG signals, common in real-world scenarios. This study underscores how hybrid models can improve stress detection and sets the stage for future research integrating physiological signals with advanced deep learning techniques.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100209"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377342","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Feature selection using hybridized Genghis Khan Shark with snow ablation optimization technique for multi-disease prognosis","authors":"Ruqsar Zaitoon ,&nbsp;Shaik Salma Asiya Begum ,&nbsp;Sachi Nandan Mohanty ,&nbsp;Deepa Jose","doi":"10.1016/j.ibmed.2025.100249","DOIUrl":"10.1016/j.ibmed.2025.100249","url":null,"abstract":"<div><div>The exponential growth in medical data and feature dimensionality presents significant challenges in building accurate and efficient diagnostic models. High-dimensional datasets often contain redundant or irrelevant features that degrade classification performance and increase computational burden. Feature selection (FS) is therefore a critical step in medical data analysis to enhance model accuracy and interpretability. While many recent FS techniques rely on optimization algorithms, tuning their parameters and avoiding early convergence remain major challenges. This study introduces a novel hybrid optimization technique—Hybridized Genghis Khan Shark with Snow Ablation Optimization (HyGKS-SAO)—to identify the most informative features for multi-disease classification. The raw medical datasets are first pre-processed using a Tanh-based normalization method. The HyGKS-SAO algorithm then selects optimal features, balancing exploration and exploitation effectively. Finally, a multi-kernel support vector machine (SVM) is employed to classify diseases based on the selected features. The proposed framework is evaluated on six publicly available medical datasets, including breast cancer, diabetes, heart disease, stroke, lung cancer, and chronic kidney disease. Experimental results demonstrate the effectiveness of the proposed method, achieving 98 % accuracy, 97.99 % MCC, 96.31 % PPV, 97.35 % G-mean, 98.03 % Kappa Coefficient, and a low computation time of 50 s, outperforming several state-of-the-art approaches.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"11 ","pages":"Article 100249"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143848482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementation of artificial intelligence in detection, classification, and prognostication of osteosarcoma utilizing different assessment techniques: a systematic review","authors":"Zhina Mohamadi ,&nbsp;Paniz Partovifar ,&nbsp;Helia Ahmadzadeh ,&nbsp;Elmira Ali Ahmadi ,&nbsp;Ali Ghanbari ,&nbsp;Sina Feyzipour ,&nbsp;Fatemeh Atefat ,&nbsp;Nazanin Jahanpeyma ,&nbsp;Fatemeh Haghighi asl ,&nbsp;Armin Zarinkhat ,&nbsp;Narges Sharbatdaran ,&nbsp;Narges Hosseinzadeh taher ,&nbsp;Mobina Sedighi ,&nbsp;Fatemeh Aghajafari","doi":"10.1016/j.ibmed.2025.100250","DOIUrl":"10.1016/j.ibmed.2025.100250","url":null,"abstract":"<div><h3>Introduction</h3><div>Osteosarcoma (OS) is the most common primary bone cancer particularly in individuals aged 0–19, classified into different stages. Early diagnosis improves survival, Determination of prognosis and treatment based on it, and enables limb-sparing surgery. AI, in particular machine learning (ML) and deep learning (DL), helps analyze large datasets, identify biomarkers, predict prognosis, and personalize treatments by assessing the aforementioned features. AI has the potential to improve evaluation procedures, such as imaging and pathology approaches used in OS diagnosis, prognosis, and treatment. This study systematically examines AI's synergistic role with conventional evaluating techniques in OS treatment, improving prognostication, predicting therapy responses, and developing personalized treatment strategies.</div></div><div><h3>Method</h3><div>We performed an extensive search via several databases until April 23, 2024. Machine learning (ML), deep learning (DL) as the main branches of AI are often utilized in the medical sciences were searched for detection classification, and prognostication of osteosarcoma. RAYYAN.ai was used to screen the articles through the titles and abstracts. We conducted data extraction on the included articles and employed Cochrane and QUIPS tools to assess potential bias in the included non-prognosis and prognosis studies to evaluate their quality, respectively.</div></div><div><h3>Results</h3><div>There were 8129 articles obtained from the four databases following a thorough search. Of them 8050 ones were excluded and the remaining 78 articles published from 2013 to 2024 were reviewed. A large number of the articles indicated moderate and low risk of bias as a result of the risk of bias assessment. The majority of the articles that were reviewed (n = 48) concerned the clinical aspects of osteosarcoma; of these, 23 and 25 studies assessed diagnosis and prognoses, respectively. Furthermore, 20 articles examined image analysis specifically, 4 examined image segmentation methods, and 16 introduced classifiers to identify osteosarcoma from other diseases.</div></div><div><h3>Conclusion</h3><div>AI improves biomarker identification, diagnostics, and prognosis of osteosarcoma through medical imaging and data integration. Models like ResNet50 and CNN show high performance but face real-world limitations due to data heterogeneity and overfitting. This study explores AI's role in osteosarcoma diagnosis, emphasizing interdisciplinary collaboration, external validation, and real-world application challenges.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"12 ","pages":"Article 100250"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144167924","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}