Ming-Ying Lu, Jacky Chung-Hao Wu, Henry Horng-Shing Lu
et al.
The global burden of hepatocellular carcinoma (HCC) has shifted from viral to nonviral etiologies. However, successful antiviral therapy does not fully eliminate the risk of HCC, underscoring the demand for more effective surveillance strategies. Current screening methods, such as semiannual ultrasonography and the measurement of α-fetoprotein levels, offer suboptimal sensitivity for early detection. A cost-effective, reliable surveillance approach remains an unmet need. The Barcelona Clinic Liver Cancer staging system provides a framework to guide HCC therapy; yet, some gray zone exists, particularly for patients with intermediate-stage disease. Although tyrosine kinase inhibitors and immunotherapies have transformed the therapeutic landscape, their efficacies vary among patients, highlighting the necessity for personalized treatment strategies. In response to these challenges, artificial intelligence (AI) approaches have emerged as transformative tools in healthcare. By processing complex, nonlinear relationships and uncovering hidden patterns in clinical data, AI methods offer capabilities beyond those of traditional statistical methods. Furthermore, AI-driven multi-omics analysis holds promise for identifying novel biomarkers, thereby advancing precision medicine for HCC patients. This review introduces the potential of AI applications in enhancing the diagnosis, treatment, and prognosis of HCC.
Diseases of the digestive system. Gastroenterology
Jaromir Hunia, Jaromir Hunia, Jaromir Tomasik
et al.
The recent advancement of mRNA technology has opened new therapeutic avenues for treating hematologic malignancies, offering innovative approaches to enhance existing immunotherapies. This review examines the expanding role of in vitro transcribed (IVT)-mRNA-based platforms in hemato-oncology, focusing on key areas: monoclonal antibody production, bispecific antibody development, and CAR-T cell engineering. Unlike conventional biologics, mRNA allows for in vivo expression of therapeutic proteins, reducing manufacturing complexity and expanding access through scalable, cell-free synthesis. IVT-mRNA-encoded monoclonal and bispecific antibodies can overcome limitations such as short half-life and the need for continuous infusion, while enabling innovations like Fc silencing, protease-activated masking, and combinatorial immunotherapies. In CAR-T cell therapy, IVT-mRNA provides transient, safer alternatives to viral vector-based approaches and facilitates emerging strategies such as in vivo CAR programming and IVT-mRNA vaccine-like boosters. Despite these advantages, challenges remain, including delivery precision, durability of therapeutic effects, and limited clinical trial success. Beyond therapeutic mechanisms, the integration of bioinformatics and AI in IVT-mRNA design is accelerating the development of personalized and efficient cancer treatments. Overall, mRNA technology is redefining immunotherapy in hematology and holds the potential to broaden access to advanced treatments globally.
Ghenwa Chamouni, Filippo Lococo, Carolina Sassorossi
et al.
IntroductionArtificial intelligence (AI) is increasingly integrating into the healthcare field, particularly in lung cancer care, including screening, diagnosis, treatment, and prognosis. While these applications offer promising advancements, they also raise complex challenges that must be addressed to ensure responsible implementation in clinical practice. This scoping review explores the ethical and legal aspects of AI applications in lung cancer.MethodsA search was conducted across PubMed, Scopus, Web of Science, Cochrane Library, PROSPERO, OAIster, and CABI. A total of 581 records were initially retrieved, of which 20 met the eligibility criteria and were included in the review. The PRISMA guidelines were followed.ResultsThe most frequently reported ethical concern was data privacy. Other recurrent issues included informed consent, no harm to patients, algorithmic bias and fairness, transparency, equity in AI access and use, and trust. The most frequently raised legal concerns were data protection and privacy, although issues relating to cybersecurity, liability, safety and effectiveness, the lack of appropriate regulation, and intellectual property law were also noted. Solutions proposed ranged from technical approaches to calls for regulatory and policy development. However, many studies lacked comprehensive legal analysis, and most included papers originated from high-income countries. This highlights the need for a broader global perspective.DiscussionThis review found that data privacy and protection are the most prominent ethical and legal concerns in AI applications for lung cancer care. Deep Learning (DL) applications, especially in diagnostic imaging, are closely tied to data privacy, lack of transparency, and algorithmic bias. Hybrid and multimodal AI systems raise additional concerns regarding informed consent and the lack of proper regulations. Ethical issues were more frequently addressed than legal ones, with limited consideration for global applicability, particularly in low- and lower middle-income countries. Although technical and policy solutions have been proposed, these remain largely unvalidated and fragmented, with limited real-world feasibility or scalability.
R. S. Basavanna, Ishaan Adhaulia, N. M. Dhanyakumar
et al.
Background:
The integration of artificial intelligence in dentistry has seen remarkable advancements, especially in diagnostic imaging. This study evaluates and compares the accuracy of deep learning models with that of dental postgraduate students in determining working length on intraoral periapical radiographs.
Materials and Methods:
One hundred anonymized radiographs of single-rooted teeth with files at working length were obtained. The images were preprocessed and used to train a deep learning model. Five dental postgraduates visually estimated the working length after receiving training. Pixel counting in image processing software provided the gold standard measurement. Accuracy comparisons were performed using a t-test.
Results:
The deep learning model demonstrated significantly higher accuracy (85%) compared to human estimations (mean accuracy 75.4%). The t-test yielded P = 0.0374 (P < 0.05), rejecting the null hypothesis.
Conclusion:
Deep learning models show great potential in enhancing precision and reliability for working length determination in endodontics. With further refinement, these models can effectively complement human expertise in dental radiographic interpretation.
The automotive industry increasingly relies on 3D modeling technologies to design and manufacture vehicle components with high precision. One critical challenge is optimizing the placement of latches that secure the dashboard side paneling, as these placements vary between models and must maintain minimal tolerance for movement to ensure durability. While generative artificial intelligence (AI) has advanced rapidly in generating text, images, and video, its application to creating accurate 3D CAD models remains limited. This paper proposes a novel framework that integrates a PointNet deep learning model with Python-based CAD automation to predict optimal clip placements and surface thickness for dashboard side panels. Unlike prior studies that focus on general-purpose CAD generation, this work specifically targets automotive interior components and demonstrates a practical method for automating part design. The approach involves generating placement data—potentially via generative AI—and importing it into the CAD environment to produce fully parameterized 3D models. Experimental results show that the prototype achieved a 75% success rate across six of eight test surfaces, indicating strong potential despite the limited sample size. This research highlights a clear pathway for applying generative AI to part design automation in the automotive sector and offers a foundation for scaling to broader design applications.
With the rapid development of generative artificial intelligence technology, the traditional cloud-based centralized model training and inference face significant limitations due to high transmission latency and costs, which restrict user-side in-situ Artificial Intelligence Generated Content (AIGC) service requests. To this end, we propose the Edge Artificial Intelligence Generated Content (EdgeAIGC) framework, which can effectively address the challenges of cloud computing by implementing in-situ processing of services close to the data source through edge computing. However, AIGC models usually have a large parameter scale and complex computing requirements, which poses a huge challenge to the storage and computing resources of edge devices. This paper focuses on the edge intelligence model caching and resource allocation problems in the EdgeAIGC framework, aiming to improve the cache hit rate and resource utilization of edge devices for models by optimizing the model caching strategy and resource allocation scheme, and realize in-situ AIGC service processing. With the optimization objectives of minimizing service request response time and execution cost in resource-constrained environments, we employ the Twin Delayed Deep Deterministic Policy Gradient algorithm for optimization. Experimental results show that, compared with other methods, our model caching and resource allocation strategies can effectively improve the cache hit rate by at least 41.06% and reduce the response cost as well.
An enhanced snow geese algorithm (ESGA) is proposed to address the problems of the weakened population diversity and unbalanced search tendencies encountered by the snow geese algorithm (SGA) in the search process. First, an adaptive switching strategy is used to dynamically select the search strategy to balance the exploitation and exploration capabilities. Second, a dominant group guidance strategy is introduced to improve the population quality. Finally, a dominant stochastic difference search strategy is designed to enrich the population diversity and help it escape from the local optimum by co-directing effects in multiple directions. Ablation experiments were performed on the CEC2017 test set to illustrate the improvement mechanism and the degree of compatibility of their improved strategies. The proposed ESGA with a highly cited algorithm and the powerful improved algorithm are compared on the CEC2022 test suite, and the experimental results confirm that the ESGA outperforms the compared algorithms. Finally, the ability of the ESGA to solve complex problems is further highlighted by solving the robot path planning problem.
Artificial Intelligence (AI) is rapidly transforming engineering fields, from robotics to aerospace, with applications in control systems for UAVs and satellites. This work builds on a previously developed AI attitude controller for the InnoCube 3U nanosatellite. Deploying complex Neural Networks (NNs) on resource-limited microcontrollers presents a significant challenge. To overcome this, we propose distilling a Multi-Layer Perceptron (MLP) trained with Deep Reinforcement Learning (DRL) for attitude control into a Kolmogorov–Arnold Network (KAN). We convert this numeric KAN into a symbolic KAN, where each edge represents a learnable mathematical function, and finally extract a concise symbolic formula. This symbolic representation dramatically reduces memory usage and computational complexity, making it ideal for pico- and nanosatellites. We evaluate and demonstrate the feasibility of this approach for inertial pointing with reaction wheels in simulation using a realistic model of the InnoCube satellite. Our results show that the highly compressed KANs successfully solve the attitude control problem, while reducing the required memory footprint and inference time on the InnoCube ADCS hardware by over an order of magnitude. Beyond attitude control, we believe symbolic KANs hold great potential in aerospace for neural network compression and interpretable, data-driven modeling and system identification in future space missions.
Abstract Background Niacin Skin-Flushing Response (NSR) has emerged as a promising objective biomarker for the precise diagnosis of mental disorders. However, its diagnostic potential has been constrained by the limitations of traditional statistical approaches. The advent of Artificial Intelligence (AI) offers a transformative opportunity to overcome these challenges. This study presents a novel contribution to the field by establishing an open-access dataset and developing advanced AI-driven tools to enhance the diagnostic accuracy of psychiatric disorders through NSR analysis. Methods This study introduces the world’s first open dataset specifically developed for AI studies of Niacin Skin-Flushing Response (NSR), a physiological biomarker associated with mental illnesses including depression, bipolar disorder, and schizophrenia. Leveraging this dataset, we developed an advanced Machine Learning (ML) approach designed for the broad diagnosis of mental disorders. Distinct from prior studies which are often limited to First Episode Schizophrenia and depend on specific devices, our approach champions device independence. The core of our methodology involves a novel algorithm featuring an Efficient-Unet based Deep Learning model for the precise segmentation of NSR areas. This segmentation is significantly enhanced by runtime data augmentation and trained on a robust train/validation/test dataset split. Subsequently, a Support Vector Machine (SVM) method is employed for psychiatric disorder classification utilizing feature vectors extracted from the segmentation of NSR areas with a 3-scale quantization. The SVM training incorporates 5-fold cross-validation, Synthetic Minority Over-sampling Technique (SMOTE) for managing class imbalance, and hyperparameter tuning to optimize balanced accuracy. Results The established dataset comprises 600 high-quality NSR images from 120 individuals, encompassing a diverse cohort of healthy controls and patients with various mental illnesses. The developed AI tools offer an objective, swift, and highly accurate approach that is demonstrably independent of the diagnosed condition or the specific device used for image acquisition. Comparative results demonstrate that the ML-based diagnostic approach achieves a sensitivity ranging from 60.0 to 65.0% and a specificity from 75.0 to 88.3% across various types of illnesses, further underscoring its broad applicability and device independence. Conclusions This research conclusively demonstrates the significant potential of advanced AI tools in achieving precise diagnosis of psychiatric disorders, potentially surpassing human capabilities in both speed and accuracy. With the provision of the proposed open dataset and the introduction of novel methodologies, this study marks substantial progress in developing an objective and accurate NSR-based screening process for a wide spectrum of psychiatric disorders. Its enhanced applicability and independence from specific devices hold profound potential to substantially advance mental health diagnostics and contribute to improved patient outcomes globally.
<p>Air pollution adversely affects health, ecosystems, and infrastructure. In the <i>Western Balkans</i> (Albania, Bosnia and Herzegovina, Kosovo<span class="note-anchor" id="fna_Ch1.Footn1"><a href="#fn_Ch1.Footn1"><sup>1</sup></a></span>, Montenegro, the Republic of North Macedonia, and Serbia), the air pollution situation is more adverse than in the European Union in general. Understanding the air quality situation requires high-quality emission data with a high-resolution spatial distribution, especially for enabling remediation efforts, which is lacking in the Western Balkan region.</p>
<p>In this work, we have calculated air pollution emissions from the heating of individual housing units in the Western Balkan region. The basis for the dataset is a geographical dataset of buildings detected from satellite imagery by artificial intelligence (AI) methods. The building data have been combined with geospatial land-use datasets and statistical data for heating needs for residential buildings in the countries included and finally with emission factors to calculate the heating emissions.</p>
<p>Using this novel approach, the resulting datasets provide high-resolution heating emission data for common pollutants and are published as open data (<a href="https://doi.org/10.5281/zenodo.13906810">https://doi.org/10.5281/zenodo.13906810</a>, <span class="cit" id="xref_altparen.1"><a href="#bib1.bibx2">Asker</a>, <a href="#bib1.bibx2">2024</a></span>). When comparing national totals for emissions, the datasets in this work are comparable to other, spatially coarser datasets, though the agreement strongly depends on the fuel usage data for each country/region.</p>
The aim of this exploratory quantitative study was to examine young people’s perceptions of the role of artificial intelligence in spiritual and moral development. The research included 121 students from the Technical Faculty “Mihajlo Pupin” in Zrenjanin and The Serbian Orthodox Church Academy for Arts and Conservation in Belgrade, thus the study relied on a convenience sample. The study analyzed how students assess the role of AI in their spiritual and moral development. Data were collected through an online Likert-type questionnaire encompassing eight domains: AI use and perception, digital literacy, emotional attitudes toward AI, spirituality, values, empathic concern, and moral attitudes. The instrument demonstrated high reliability (Cronbach’s α = .950). Descriptive, correlational, and regression methods and techniques, as well as exploratory factor analysis, were used in the analysis. The results indicate that respondents perceive AI as only slightly to moderately stimulating for their spiritual and moral development. It may be concluded that AI plays a limited and secondary role in the spiritual and moral development of young people, while this process is more strongly shaped by religious practice, family influence, and traditional sources of spirituality. Limitations relate to the sample size and structure, as well as the use of self-report measures, while future research may include broader samples and combined methodological approaches.
This study addresses patient unpunctuality, a major concern affecting patient waiting time, resource utilization, and quality of care. We develop and compare four machine learning models, including multinomial logistic regression, decision tree, random forest, and artificial neural network, to accurately predict patient arrival patterns and aid efficient scheduling. These models are analyzed using the explainable artificial intelligence approach and the Shapley additive explanations model, promoting comprehension and trust in our algorithmic results. Using three years of appointment data from a psychiatric clinic, we identify the travel distance, appointment lead time, patient’s age, Body Mass Index (BMI), and certain mental diagnoses as significant factors affecting the patient’s unpunctuality. Despite the good predictive potential of machine learning algorithms, no single model excels in all performance metrics. The study proposes implementing these machine learning techniques and the explainable artificial intelligence tool into the clinic’s appointment system as a decision support system to minimize patient unpunctuality.
Computer applications to medicine. Medical informatics
Nasser M. Assery, Carlos A. Jurado, Mansour K. Assery
et al.
Peri-implantitis is an inflammatory condition induced by bacterial biofilm that affects the soft and hard tissues surrounding dental implants, compromising the success of implant therapy. Recent studies have highlighted the potential links between peri-implant health and systemic inflammation, including uncontrolled diabetes mellitus, psychological stress, cardiovascular disease, obesity, and infectious diseases such as COVID-19. As an inflammatory disease, peri-implantitis may trigger systemic inflammation by elevating circulating levels of pro-inflammatory cytokines, which could have unknown impacts on overall health. While the relationship between periodontal health and systemic conditions is better understood, the association between peri-implant disease and systemic inflammation remains unclear. Therefore, this comprehensive review aims to summarize the most recent evidence on the relationship between peri-implantitis and systemic inflammation, focusing on biological complications, microbiology, and biomarkers. This review aims to enhance our understanding of the links between peri-implantitis and systemic inflammation and promote further research in this field by discussing the latest insights and clinical implications.
Ali Raza, Mohammad Rustom Al Nasar, Essam Said Hanandeh
et al.
Kinematic motion detection aims to determine a person’s actions based on activity data. Human kinematic motion detection has many valuable applications in health care, such as health monitoring, preventing obesity, virtual reality, daily life monitoring, assisting workers during industry manufacturing, caring for the elderly. Computer vision-based activity recognition is challenging due to problems such as partial occlusion, background clutter, appearance, lighting, viewpoint, and changes in scale. Our research aims to detect human kinematic motions such as walking or running using smartphones’ sensor data within a high-performance framework. An existing dataset based on smartphones’ gyroscope and accelerometer sensor values is utilized for the experiments in our study. Sensor exploratory data analysis was conducted in order to identify valuable patterns and insights from sensor values. The six hyperparameters, tunned artificial indigence-based machine learning, and deep learning techniques were applied for comparison. Extensive experimentation showed that the ensemble learning-based novel ERD (ensemble random forest decision tree) method outperformed other state-of-the-art studies with high-performance accuracy scores. The proposed ERD method combines the random forest and decision tree models, which achieved a 99% classification accuracy score. The proposed method was successfully validated with the k-fold cross-validation approach.