Frontiers in Artificial Intelligence最新文献

{"title":"Enhancing the design of voting advice applications with BERT language model.","authors":"Daniil Buryakov, Mate Kovacs, Uwe Serdült, Victor Kryssanov","doi":"10.3389/frai.2024.1343214","DOIUrl":"10.3389/frai.2024.1343214","url":null,"abstract":"<p><p>The relevance and importance of voting advice applications (VAAs) are demonstrated by their popularity among potential voters. On average, around 30% of voters take into account the recommendations of these applications during elections. The comparison between potential voters' and parties' positions is made on the basis of VAA policy statements on which users are asked to express opinions. VAA designers devote substantial time and effort to analyzing domestic and international politics to formulate policy statements and select those to be included in the application. This procedure involves manually reading and evaluating a large volume of publicly available data, primarily party manifestos. A problematic part of the work is the limited time frame. This study proposes a system to assist VAA designers in formulating, revising, and selecting policy statements. Using pre-trained language models and machine learning methods to process politics-related textual data, the system produces a set of suggestions corresponding to relevant VAA statements. Experiments were conducted using party manifestos and YouTube comments from Japan, combined with VAA policy statements from six Japanese and two European VAAs. The technical approaches used in the system are based on the BERT language model, which is known for its capability to capture the context of words in the documents. Although the output of the system does not completely eliminate the need for manual human assessment, it provides valuable suggestions for updating VAA policy statements on an objective, i.e., bias-free, basis.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333799/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009595","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":"Self-attention with temporal prior: can we learn more from the arrow of time?","authors":"Kyung Geun Kim, Byeong Tak Lee","doi":"10.3389/frai.2024.1397298","DOIUrl":"10.3389/frai.2024.1397298","url":null,"abstract":"<p><p>Many diverse phenomena in nature often inherently encode both short- and long-term temporal dependencies, which especially result from the direction of the flow of time. In this respect, we discovered experimental evidence suggesting that <i>interrelations</i> of these events are higher for closer time stamps. However, to be able for attention-based models to learn these regularities in short-term dependencies, it requires large amounts of data, which are often infeasible. This is because, while they are good at learning piece-wise temporal dependencies, attention-based models lack structures that encode biases in time series. As a resolution, we propose a simple and efficient method that enables attention layers to better encode the short-term temporal bias of these data sets by applying learnable, adaptive kernels directly to the attention matrices. We chose various prediction tasks for the experiments using Electronic Health Records (EHR) data sets since they are great examples with underlying long- and short-term temporal dependencies. Our experiments show exceptional classification results compared to best-performing models on most tasks and data sets.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11333831/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142009596","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":"A comparison of the diagnostic ability of large language models in challenging clinical cases.","authors":"Maria Palwasha Khan, Eoin Daniel O'Sullivan","doi":"10.3389/frai.2024.1379297","DOIUrl":"10.3389/frai.2024.1379297","url":null,"abstract":"<p><strong>Introduction: </strong>The rise of accessible, consumer facing large language models (LLM) provides an opportunity for immediate diagnostic support for clinicians.</p><p><strong>Objectives: </strong>To compare the different performance characteristics of common LLMS utility in solving complex clinical cases and assess the utility of a novel tool to grade LLM output.</p><p><strong>Methods: </strong>Using a newly developed rubric to assess the models' diagnostic utility, we measured to models' ability to answer cases according to accuracy, readability, clinical interpretability, and an assessment of safety. Here we present a comparative analysis of three LLM models-Bing, Chat GPT, and Gemini-across a diverse set of clinical cases as presented in the New England Journal of Medicines case series.</p><p><strong>Results: </strong>Our results suggest that models performed differently when presented with identical clinical information, with Gemini performing best. Our grading tool had low interobserver variability and proved a reliable tool to grade LLM clinical output.</p><p><strong>Conclusion: </strong>This research underscores the variation in model performance in clinical scenarios and highlights the importance of considering diagnostic model performance in diverse clinical scenarios prior to deployment. Furthermore, we provide a new tool to assess LLM output.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330891/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005445","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":"A framework for extending co-creative communication models to sustainability research.","authors":"Guanhong Li, Xiaoyun Guo","doi":"10.3389/frai.2024.1236310","DOIUrl":"10.3389/frai.2024.1236310","url":null,"abstract":"<p><p>The UN Sustainable Development Goals (SDGs) present a challenge due to their potential for conflicting objectives, which hinders their effective implementation. In order to address the complexity of sustainability issues, a framework capable of capturing the specificity of diverse sustainability issues while offering a common methodology applicable across contexts is required. Co-creative communication can be regarded as a key source of uncertainty within functional systems, as it can be instrumental in realizing and sustaining sustainability. In this regard, the studies in Constructive approaches to Co-creative Communication (CCC), particularly those employing artificial intelligence (AI) methodologies such as computational social science and innovation studies, hold significant value for both theoretical and applied sustainability research. However, existing CCC frameworks cannot be directly applied to sustainability research. This work bridges this gap by proposing a framework that outlines a general approach to establishing formalized definitions of sustainability from the lens of communication. This approach enables the direct application of CCC models to sustainability studies. The framework is based on systems theory and the methodologies of artificial intelligence, including computational/symbolic modeling and formal methods. This framework emphasizes the social function of co-creative communication and the interaction between the innovation process and the sustainability of the system. It can be concluded that the application of our framework enables the achievements of CCC to be directly applied to sustainability research. Researchers from different disciplines are therefore able to establish their own specific definitions of sustainability, which are tailored to their particular concerns. Our framework lays the groundwork for future sustainability studies that employs CCC, facilitating the integration of CCC insights into sustainability research and application. The outcomes of computational creativity research based on AI technologies, such as distributed artificial intelligence and self-organizing networks, can deepen the understanding of sustainability mechanisms and drive their practical applications. Furthermore, the functional role of co-creative communication in societal sustainability proposed in this work offers a novel perspective for future discussions on the evolutionary adaptation of co-creative communication.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330873/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005446","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":"Enhanced fingerprint classification through modified PCA with SVD and invariant moments.","authors":"Ala Balti, Abdelaziz Hamdi, Sabeur Abid, Mohamed Moncef Ben Khelifa, Mounir Sayadi","doi":"10.3389/frai.2024.1433494","DOIUrl":"10.3389/frai.2024.1433494","url":null,"abstract":"<p><p>This research introduces a novel MOMENTS-SVD vector for fingerprint identification, combining invariant moments and SVD (Singular Value Decomposition), enhanced by a modified PCA (Principal Component Analysis). Our method extracts unique fingerprint features using SVD and invariant moments, followed by classification with Euclidean distance and neural networks. The MOMENTS-SVD vector reduces computational complexity by outperforming current models. Using the Equal Error Rate (EER) and ROC curve, a comparative study across databases (CASIA V5, FVC 2002, 2004, 2006) assesses our method against ResNet, VGG19, Neuro Fuzzy, DCT Features, and Invariant Moments, proving enhanced accuracy and robustness.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11330874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142005447","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":"Deep learning for prediction of post-thrombectomy outcomes based on admission CT angiography in large vessel occlusion stroke.","authors":"Jakob Sommer, Fiona Dierksen, Tal Zeevi, Anh Tuan Tran, Emily W Avery, Adrian Mak, Ajay Malhotra, Charles C Matouk, Guido J Falcone, Victor Torres-Lopez, Sanjey Aneja, James Duncan, Lauren H Sansing, Kevin N Sheth, Seyedmehdi Payabvash","doi":"10.3389/frai.2024.1369702","DOIUrl":"10.3389/frai.2024.1369702","url":null,"abstract":"<p><strong>Purpose: </strong>Computed Tomography Angiography (CTA) is the first line of imaging in the diagnosis of Large Vessel Occlusion (LVO) strokes. We trained and independently validated end-to-end automated deep learning pipelines to predict 3-month outcomes after anterior circulation LVO thrombectomy based on admission CTAs.</p><p><strong>Methods: </strong>We split a dataset of 591 patients into training/cross-validation (n = 496) and independent test set (n = 95). We trained separate models for outcome prediction based on admission \"CTA\" images alone, \"CTA + Treatment\" (including time to thrombectomy and reperfusion success information), and \"CTA + Treatment  + Clinical\" (including admission age, sex, and NIH stroke scale). A binary (favorable) outcome was defined based on a 3-month modified Rankin Scale ≤ 2. The model was trained on our dataset based on the pre-trained ResNet-50 3D Convolutional Neural Network (\"MedicalNet\") and included CTA preprocessing steps.</p><p><strong>Results: </strong>We generated an ensemble model from the 5-fold cross-validation, and tested it in the independent test cohort, with receiver operating characteristic area under the curve (AUC, 95% confidence interval) of 70 (0.59-0.81) for \"CTA,\" 0.79 (0.70-0.89) for \"CTA + Treatment,\" and 0.86 (0.79-0.94) for \"CTA + Treatment + Clinical\" input models. A \"Treatment + Clinical\" logistic regression model achieved an AUC of 0.86 (0.79-0.93).</p><p><strong>Conclusion: </strong>Our results show the feasibility of an end-to-end automated model to predict outcomes from admission and post-thrombectomy reperfusion success. Such a model can facilitate prognostication in telehealth transfer and when a thorough neurological exam is not feasible due to language barrier or pre-existing morbidities.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324606/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989085","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":"Real-time temperature anomaly detection in vaccine refrigeration systems using deep learning on a resource-constrained microcontroller.","authors":"Mokhtar Harrabi, Abdelaziz Hamdi, Bouraoui Ouni, Jamel Bel Hadj Tahar","doi":"10.3389/frai.2024.1429602","DOIUrl":"10.3389/frai.2024.1429602","url":null,"abstract":"<p><p>Maintaining consistent and accurate temperature is critical for the safe and effective storage of vaccines. Traditional monitoring methods often lack real-time capabilities and may not be sensitive enough to detect subtle anomalies. This paper presents a novel deep learning-based system for real-time temperature fault detection in refrigeration systems used for vaccine storage. Our system utilizes a semi-supervised Convolutional Autoencoder (CAE) model deployed on a resource-constrained ESP32 microcontroller. The CAE is trained on real-world temperature sensor data to capture temporal patterns and reconstruct normal temperature profiles. Deviations from the reconstructed profiles are flagged as potential anomalies, enabling real-time fault detection. Evaluation using real-time data demonstrates an impressive 92% accuracy in identifying temperature faults. The system's low energy consumption (0.05 watts) and memory usage (1.2 MB) make it suitable for deployment in resource-constrained environments. This work paves the way for improved monitoring and fault detection in refrigeration systems, ultimately contributing to the reliable storage of life-saving vaccines.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11324578/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141989086","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":"A unified Foot and Mouth Disease dataset for Uganda: evaluating machine learning predictive performance degradation under varying distributions.","authors":"Geofrey Kapalaga, Florence N Kivunike, Susan Kerfua, Daudi Jjingo, Savino Biryomumaisho, Justus Rutaisire, Paul Ssajjakambwe, Swidiq Mugerwa, Yusuf Kiwala","doi":"10.3389/frai.2024.1446368","DOIUrl":"10.3389/frai.2024.1446368","url":null,"abstract":"<p><p>In Uganda, the absence of a unified dataset for constructing machine learning models to predict Foot and Mouth Disease outbreaks hinders preparedness. Although machine learning models exhibit excellent predictive performance for Foot and Mouth Disease outbreaks under stationary conditions, they are susceptible to performance degradation in non-stationary environments. Rainfall and temperature are key factors influencing these outbreaks, and their variability due to climate change can significantly impact predictive performance. This study created a unified Foot and Mouth Disease dataset by integrating disparate sources and pre-processing data using mean imputation, duplicate removal, visualization, and merging techniques. To evaluate performance degradation, seven machine learning models were trained and assessed using metrics including accuracy, area under the receiver operating characteristic curve, recall, precision and F1-score. The dataset showed a significant class imbalance with more non-outbreaks than outbreaks, requiring data augmentation methods. Variability in rainfall and temperature impacted predictive performance, causing notable degradation. Random Forest with borderline SMOTE was the top-performing model in a stationary environment, achieving 92% accuracy, 0.97 area under the receiver operating characteristic curve, 0.94 recall, 0.90 precision, and 0.92 F1-score. However, under varying distributions, all models exhibited significant performance degradation, with random forest accuracy dropping to 46%, area under the receiver operating characteristic curve to 0.58, recall to 0.03, precision to 0.24, and F1-score to 0.06. This study underscores the creation of a unified Foot and Mouth Disease dataset for Uganda and reveals significant performance degradation in seven machine learning models under varying distributions. These findings highlight the need for new methods to address the impact of distribution variability on predictive performance.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322090/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983459","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":"Doctor AI? A pilot study examining responses of artificial intelligence to common questions asked by geriatric patients","authors":"Ian Moore, Christopher Magnante, Ellie Embry, Jennifer Mathis, Scott Mooney, S. Haj-Hassan, Maria Cottingham, Prasad R. Padala","doi":"10.3389/frai.2024.1438012","DOIUrl":"https://doi.org/10.3389/frai.2024.1438012","url":null,"abstract":"AI technologies have the potential to transform patient care. AI has been used to aid in differential diagnosis and treatment planning for psychiatric disorders, administer therapeutic protocols, assist with interpretation of cognitive testing, and patient treatment planning. Despite advancements, AI has notable limitations and remains understudied and further research on its strengths and limitations in patient care is required. This study explored the responses of AI (Chat-GPT 3.5) and trained clinicians to commonly asked patient questions.Three clinicians and AI provided responses to five dementia/geriatric healthcare-related questions. Responses were analyzed by a fourth, blinded clinician for clarity, accuracy, relevance, depth, and ease of understanding and to determine which response was AI generated.AI responses were rated highest in ease of understanding and depth across all responses and tied for first for clarity, accuracy, and relevance. The rating for AI generated responses was 4.6/5 (SD = 0.26); the clinician s' responses were 4.3 (SD = 0.67), 4.2 (SD = 0.52), and 3.9 (SD = 0.59), respectively. The AI generated answers were identified in 4/5 instances.AI responses were rated more highly and consistently on each question individually and overall than clinician answers demonstrating that AI could produce good responses to potential patient questions. However, AI responses were easily distinguishable from those of clinicians. Although AI has the potential to positively impact healthcare, concerns are raised regarding difficulties discerning AI from human generated material, the increased potential for proliferation of misinformation, data security concerns, and more.","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141806120","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":"Multimodal data integration for oncology in the era of deep neural networks: a review.","authors":"Asim Waqas, Aakash Tripathi, Ravi P Ramachandran, Paul A Stewart, Ghulam Rasool","doi":"10.3389/frai.2024.1408843","DOIUrl":"10.3389/frai.2024.1408843","url":null,"abstract":"<p><p>Cancer research encompasses data across various scales, modalities, and resolutions, from screening and diagnostic imaging to digitized histopathology slides to various types of molecular data and clinical records. The integration of these diverse data types for personalized cancer care and predictive modeling holds the promise of enhancing the accuracy and reliability of cancer screening, diagnosis, and treatment. Traditional analytical methods, which often focus on isolated or unimodal information, fall short of capturing the complex and heterogeneous nature of cancer data. The advent of deep neural networks has spurred the development of sophisticated multimodal data fusion techniques capable of extracting and synthesizing information from disparate sources. Among these, Graph Neural Networks (GNNs) and Transformers have emerged as powerful tools for multimodal learning, demonstrating significant success. This review presents the foundational principles of multimodal learning including oncology data modalities, taxonomy of multimodal learning, and fusion strategies. We delve into the recent advancements in GNNs and Transformers for the fusion of multimodal data in oncology, spotlighting key studies and their pivotal findings. We discuss the unique challenges of multimodal learning, such as data heterogeneity and integration complexities, alongside the opportunities it presents for a more nuanced and comprehensive understanding of cancer. Finally, we present some of the latest comprehensive multimodal pan-cancer data sources. By surveying the landscape of multimodal data integration in oncology, our goal is to underline the transformative potential of multimodal GNNs and Transformers. Through technological advancements and the methodological innovations presented in this review, we aim to chart a course for future research in this promising field. This review may be the first that highlights the current state of multimodal modeling applications in cancer using GNNs and transformers, presents comprehensive multimodal oncology data sources, and sets the stage for multimodal evolution, encouraging further exploration and development in personalized cancer care.</p>","PeriodicalId":33315,"journal":{"name":"Frontiers in Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11308435/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141907822","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}