Jul 2026· Clinical Cancer Research· Vol 32, pp. A036-A036· 0 citations
TL;DR
An AI-based platform that combines multiple screening modalities with AI-driven digital analysis for identification of high-risk individuals to improve cancer screening, support clinical decision-making, reduce cancer risk, and optimize healthcare resources is developed.
Abstract
Cancer is the second leading cause of death worldwide and causes nearly 9.6 million deaths each year according to the WHO. Although medical technology and screening programs continue to improve, delayed diagnosis remains a major challenge. Artificial intelligence (AI) offers a promising approach for cancer detection and risk prediction. This study developed an AI-based platform that integrates gene panel data, traditional risk factors, and healthcare professional assessments to identify individuals at high cancer risk. The system applied machine learning models including random forest, support vector machines, convolutional neural networks (CNN), deep learning algorithms, and Natural Language Processing to analyze complex associations between genetic variants and clinical risk factors. The platform used Python programming with Django, MySQL, ChartJS, JSCharting, HTML, CSS, and JavaScript frameworks. Developers created independent front-end and back-end modules and managed version control with Git. Researchers evaluated the platform in 514,506 cases across 39 multicenter units. The deep learning engine analyzed medical data and risk factors using CNN, support vector machines, and random forest classifiers. The platform also integrated ECG and mammography analysis with general health screening to provide comprehensive risk assessment. Two CNN models supported colon cancer analysis with different fully connected layers for classification of colon adenocarcinoma and benign colonic tissue (https://smartcancer.ir/). Random Forest-based models estimated short-term 5-year risk and long-term lifetime risk. The platform integrated Electronic Health Records (EHR) with blockchain technology to improve security, privacy, and data integrity. The system also generated family trees to support genetic counselling. The platform recommended the next clinical step based on low-risk or high-risk status and stored previous records for specialist follow-up. This study presents a novel framework that combines multiple screening modalities with AI-driven digital analysis for identification of high-risk individuals. Integration of federated learning, blockchain security, and AI-based diagnostics can improve cancer screening, support clinical decision-making, reduce cancer risk, and optimize healthcare resources.
Aida Yavari Kondori, Ahmadreza Tavasouli, Mona Maftouh, Ghazaleh Pourali, Zahra Yousefli, Soodabeh Shahidsales, Ali Alamdaran, Masoud Pezeshkirad, Marjaneh Farazestanian, Elisa Giovannetti, Amir Avan, Hamid Naderi. Smart Health Screening in Identification of Individuals at High Cancer Risk Using Artificial Intelligence [abstract]. In: Proceedings of AACR Drug Discovery and Development (AACR D3) Conference; 2026 Jul 21-24; Boston, MA. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(14_Suppl):Abstract nr A036.
The paper presents the framework that mediates between optimization methods and predictive modeling to provide valuable information on the next generation of data-driven cardiovascular diagnostics to provide valuable information on the next generation of data-driven cardiovascular diagnostics.
Shamal Salunkhe, Smita Bharne, P. Patil et al.· International Research Journ...· 0 citations
Prediabetes is a highly prevalent intermediate metabolic state and a major public health target for preventing type 2 diabetes mellitus (T2DM). However, current approaches to prediabetes screening, risk stratification, and lifestyle management remain limited by inconsistent diagnostic definitions, incomplete case detection, heterogeneous progression risk, and the resource-intensive nature of conventional face-to-face prevention programmes. Artificial intelligence (AI), including machine learning, deep learning, explainable AI, and algorithm-driven digital interventions, is increasingly being explored as a tool to address these gaps. This narrative review used a structured search of PubMed, Web of Science Core Collection, and Embase for studies published from January 2010 to April 2026, selecting articles that evaluated AI-assisted or algorithm-driven approaches for prediabetes screening, progression risk prediction, or lifestyle intervention and reported relevant model performance, validation, or intervention outcomes. Current evidence indicates that AI-based models can improve discrimination beyond traditional risk scores by integrating routine clinical data, longitudinal electronic health records, continuous glucose monitoring profiles, wearable-derived behavioural signals, and emerging molecular biomarkers. Some externally validated models have shown clinically relevant performance for identifying individuals at high risk of progression and for guiding more targeted preventive strategies. In parallel, fully or semi-automated digital programmes delivered through mobile applications, web platforms, connected scales, and sensor-based feedback systems have demonstrated potential to support lifestyle change, improve engagement, and reduce reliance on labour-intensive counselling. Nevertheless, translation into routine care remains constrained by heterogeneity in prediabetes definitions, limited external validation across diverse populations, uncertain long-term effectiveness, geographical imbalance in evidence, privacy concerns, and the need for stronger governance frameworks. Overall, AI should be viewed as an assistive technology that may support earlier detection, more precise risk stratification, and scalable lifestyle management in prediabetes. Further multi-centre, prospective, and implementation-focused studies are needed to establish clinical utility, equity, safety, and cost-effectiveness.
Run-Xuan Chen, Min Song, Tong Xiang· Frontiers in Endocrinology· 0 citations
The reviewed studies demonstrated that Deep learning approaches, particularly convolutional neural networks, transformer-based models, and multimodal frameworks, showed improved predictive accuracy when large and diverse datasets were available.
Tehreem Khan, Tayyaba Usman, Ifrah Khalid et al.· Discover Artificial Intellig...· 0 citations
This study emphasizes the potential of computational techniques in cancer risk assessment, highlighting opportunities for specific and data-driven healthcare solutions. It examines the use of artificial intelligence (AI), machine learning (ML), and deep learning (DL) approaches to improve cancer risk assessment using a Kaggle dataset. The study uses Java-based ML software to create and evaluate multiple predictive models, taking advantage of its powerful libraries and frameworks for processing and analyzing cancer risk indicators. This work analyzes model performance using 10-fold cross-validation, resulting in reliable generalization and accuracy estimates. Several classification techniques, such as Random Forest (RF), logistic regression (LR), decision trees (DT), Naive Bayes (NB), and Multi-layer perceptron (MLP), are used to assess their efficacy in predicting risk levels for various cancer types. To measure classification effectiveness, key performance metrics such as accuracy, precision, recall, and F1 score are produced, in addition to multi-class confusion matrices. The results show that the RF model is the best classifier for classification, with accuracy of 99.85%, F-measure of 99.80%, precision of 99.80%, and sensitivity of 99.90%. These findings demonstrate the model’s ability to effectively estimate cancer risk levels among individuals, allowing for earlier discovery and more effective medical care.
Areen Arabiat, H. Owida, Suhaila Abuowaida et al.· bioRxiv· 0 citations
Cancer is one of the major causes of death worldwide, mostly owing to late discovery and hence restricted treatment choices. Existing screening approaches are primarily invasive and often associated with complicated, long and expensive procedures. In biomedicine and bioinformatics, several research groups have examined the use of machine learning methods to solve the important challenge of categorizing cancer patients into high- and low-risk categories. These methodologies have thus been used to mimic the onset and treatment of cancer. The ability of ML algorithms to detect important characteristics in complex datasets further highlights their importance. Many of these approaches like as Decision Trees, Logistic Regression (LR), Support Vector Machines and K-Nearest Neighbours have been widely employed in cancer research to generate prediction models that aid decision makers to make better and more trustworthy decisions. ML methods are indeed able to improve our understanding of cancer formation, but need adequate validation to be regarded for application in ordinary clinical practice. Hence, an ML approach was utilized to simulate the progression of cancer. The prediction models shown here are based on several ML approaches and a broad variety of input features and Data Samples. The proposed framework incorporates data preprocessing, feature selection, and advanced classification algorithms to enhance diagnostic accuracy and facilitate timely clinical decision-making. The study emphasizes the potential of artificial intelligence in advancing precision oncology and improving healthcare outcomes.
Sakshi Singh, Saurav Kumar, Yusuf Perwej et al.· International Journal of Lat...· 0 citations
Cardiovascular disease is a main cause of mortality worldwide that need the development of reliable and data-driven prediction models for timely diagnosis and intervention. Conventional risk assessment methods depend on statistical scoring and handcrafted clinical features fail to capture complex nonlinear features in several cardiovascular existing data. This systematic literature review investigates recent advances in artificial intelligence-based CVD prediction that include machine learning, deep learning, hybrid, and ensemble learning models. The PRISMA-2020 strategy and Kitchenham guidelines, 110 studies published between 2013 and 2025 were systematically analyzed across diverse data modalities such as electronic health records, electrocardiograms, medical images, wearable sensors, and multimodal datasets. The findings indicate that tree-based ML models such as Random Forest, XGBoost, and Gradient Boosting shows the strong performance on structured clinical datasets. The DL-based models such as convolutional neural networks, long short-term memory networks, and transformer-based models effectively capture the high-dimensional physiological and imaging features. Hybrid model combined deep representations with clinical parameters to achieve predictive performance up to 98.00%, and ensemble learning models shows the superior performance and generalization capabilities across several distinct datasets. The challenges in cardiovascular disease detection such as limited multimodal datasets, very few external validation, insufficient explainability, and the absence of standardized benchmark evaluation parameters continue to show down the clinical translation. This review synthesizes current algorithmic developments, identifies critical research gaps, and highlights the potential of multimodal learning, explainable AI, federated learning, and clinically validated model for advancing next-generation cardiovascular risk prediction systems.
A. Gaikwad, N. Kulkarnir· Journal of Intelligent Decis...· 0 citations
