Following the recent development of LLMs it is suggested that these can now form the basis for Agentic AI. LLM-COGNICA is an approach to Agentic AI based on the integration of the Natural Language capabilities of LLMs with Argumentation-based Reasoning that is carried out on a Controlled Natural Language. It is a hybrid neural symbolic framework for developing decision making systems specified within Natural Language and executed via Explainable Argumentation. LLM-COGNICA offers the reliability of formal problem solving by allowing the human developer and problem solver to be in control of the agent system development process. The paper presents the LLM-COGNICA environment for the development of agent decision making, together with a systematic evaluation of the central element of mapping a decision policy in Natural Language to a corresponding policy in symbolic form.

This paper shows how argumentation logic (AL) can be further extended to cover more fully paraconsistent forms of logical reasoning. The extension is based on the notion of non-acceptable, self-defeating arguments as a generalization of the Reductio ad Absurdum principle. In this case, a self-defeating argument is either in conflict with itself or with an argument that is needed for its defense against one of its counter-arguments. In the proposed extended AL, such self-defeating arguments are recognized as arguments that do not need to be explicitly defended against. Hence attacks on other arguments by self-defeating arguments can be ignored, thus extending the possibility of the attacked arguments to be acceptable.

Schwannomatosis is a rare genetic disorder characterized by the development of multiple, painful schwannomas, presenting with high variability in clinical manifestation and management complexity. To support clinical decision-making, we previously developed an Explainable Artificial Intelligence (XAI) system based on the COGNICA framework of cognitive argumentation. The system integrates the European Reference Network (ERN) GENTURIS clinical guidelines with structured, interpretable reasoning processes, enabling transparent and traceable medical recommendations. While prior work established the system’s technical feasibility, its usability and clinical acceptability among domain experts had not been systematically assessed. This study presents the results of an expert-based evaluation using a structured questionnaire that combines standardized instruments—Visual Analogue Scales (VAS), System Usability Scale (SUS), Questionnaire for User Interface Satisfaction (QUIS), User Experience Questionnaire (UEQ), and Treatment Acceptability Rating Form (TARF)—alongside open-ended feedback. Four experts from neurology, and genetics participated in the evaluation. Results demonstrate strong satisfaction and comprehension, confirming that explainable AI tools like this system can enhance medical reasoning in rare diseases. However, their effective integration into clinical workflows will require iterative refinement, clinician training, and validation in real-world settings.

The general nature of reasoning is that of resolving the conflict between a formula and opposing formulae, e.g., its negation, which together will result in an inconsistency. This resolution process becomes more challenging when the premises themselves are conflicting. Nevertheless, if we accept that reasoning is about handling conflict then reasoning with inconsistency should be a natural and primary logical concept. This paper studies Argumentation Logic (AL) as a paraconsisent logic that naturally applies to logical reasoning under any set of premises, consistent or not. It presents AL as an inherently paraconsistent logical framework where the non-paraconsistent case of reasoning, i.e., reasoning with consistent premises, appears as a special limiting case of the paraconsistent logic.

Proactively mitigating the potential polluting effects of artificial intelligence.

This study aims to address the critical issue of emergency department (ED) overcrowding, which negatively affects patient outcomes, wait times, and resource efficiency. Accurate prediction of ED length of stay (LOS) can streamline operations and improve care delivery. We utilized the MIMIC IV-ED dataset, comprising over 400,000 patient records, to classify ED LOS into short (≤4.5 hours) and long (>4.5 hours) categories. Using machine learning models, including Gradient Boosting (GB), Random Forest (RF), Logistic Regression (LR), and Multilayer Perceptron (MLP), we identified GB as the best performing model outperforming the other models with an AUC of 0.730, accuracy of 69.93%, sensitivity of 88.20%, and specificity of 40.95% on the original dataset. In the balanced dataset, GB had an AUC of 0.729, accuracy of 68.86%, sensitivity of 75.39%, and specificity of 58.59%. To enhance interpretability, a novel rule extraction method for GB model was implemented using relevant important predictors, such as triage acuity, comorbidity scores, and arrival methods. By combining predictive analytics with interpretable rule-based methods, this research provides actionable insights for optimizing patient flow and resource allocation. The findings highlight the importance of transparency in machine learning applications for healthcare, paving the way for future improvements in model performance and clinical adoption.

Although a plethora of research has been published in the literature, providing both qualitative and quantitative analyses of cardiovascular risk, there remains a need to improve interpretability, explainability, and accuracy in the assessment of cardiovascular disease risk. To achieve this, the present study proposes a methodology that extracts knowledge from data to assess cardiovascular disease risk while also offering both local and global explanations that justify the underlying theory made and why in some cases no definite decision can be taken.

Emergency departments (EDs) require effective approaches for quickly identifying patients at risk of critical outcomes (inpatient mortality or ICU admission within 12 hours). This study developed interpretable machine learning models using Gradient Boosting (GB) and Logistic Regression (LR) with 13 initial triage variables from the MIMIC-IV-ED database. GB performed slightly better than LR (Accuracy: 78.21% vs. 77.27%, AUROC: 0.887 vs. 0.863, AUPRC: 0.445 vs. 0.370). The Te2Rules method was used to extract 43 clinically interpretable rules from the GB model, with a overall fidelity of 98.90%. The use of the Rule Coverage Index (RCI) to categorize rules into high, medium, and low levels futher enhanced clinical utility. This study aims to strike a balance between predictive accuracy and interpretability, facilitating clinicians in early detection of critically ill presenting to the ED.

BACKGROUND:The addition of ultrasonic plaque texture features to clinical features in patients with asymptomatic internal carotid artery stenosis (ACS) improved the ability of a Support Vector Machine (SVM) model to identify plaques that are likely to produce stroke. However, SVM like many Artificial Intelligence (AI) black-box models lack transparency, limiting their adoption in critical settings. Explainable AI (XAI) techniques offer potential solutions by making model decision more interpretable. This study investigates whether incorporating XAI techniques can improve interpretability without significantly compromising predictive accuracy in stroke risk assessment. METHODS:We developed an Argumentation-based Explainable Machine Learning (ArgEML) methodology and framework for explainable machine learning predictions via argumentation. We used this framework to learn explainable argumentation theories from a real-life dataset of patients with asymptomatic carotid stenosis. We assessed the performance of these theories using standard machine learning (ML) metrics, while interpretability was evaluated through model transparency and quality of explanations. RESULTS:Results indicate that the ArgEML models maintain high predictive accuracy while significantly improving the interpretability of the predictions. Moreover, undecided predictions are addressed as dilemmas which still offer valuable information through the explanations of the different prediction capabilities. CONCLUSIONS:Our findings suggest that ArgEML enhances the interpretability of stroke prediction from real life medical data without sacrificing predictive performance. Moreover, explanations offer valuable insights into misclassified cases and cases where a definite prediction cannot be derived. This transparency can help refine a model, guiding clinical decisions and improving AI adoption in healthcare.

BACKGROUND:Human-centric artificial intelligence (HCAI) aims to provide support systems that can act as peer companions to an expert in a specific domain, by simulating their way of thinking and decision-making in solving real-life problems. The gynaecological artificial intelligence diagnostics (GAID) assistant is such a system. Based on artificial intelligence (AI) argumentation technology, it was developed to incorporate, as much as possible, a complete representation of the medical knowledge in gynaecology and to become a real-life tool that will practically enhance the quality of healthcare services and reduce stress for the clinician. Our study aimed to evaluate GAIDS' efficacy and accuracy in assisting the working expert gynaecologist during day-to-day clinical practice. METHODS:Knowledge-based systems utilize a knowledge base (theory) which holds evidence-based rules ("IF-THEN" statements) that are used to prove whether a conclusion (such as a disease, medication or treatment) is possible or not, given a set of input data. This approach uses argumentation frameworks, where rules act as claims that support a specific decision (arguments) and argue for its dominance over others. The result is a set of admissible arguments which support the final decision and explain its cause. RESULTS:Based on seven different subcategories of gynaecological presentations-bleeding, endocrinology, cancer, pelvic pain, urogynaecology, sexually transmitted infections and vulva pathology in fifty patients-GAID demonstrates an average overall closeness accuracy of zero point eighty-seven. Since the system provides explanations for supporting a diagnosis against other possible diseases, this evaluation process further allowed for a learning process of modular improvement in the system of the diagnostic discrepancies between the system and the specialist. CONCLUSIONS:GAID successfully demonstrates an average accuracy of zero point eighty-seven when measuring the closeness of the system's diagnosis to that of the senior consultant. The system further provides meaningful and helpful explanations for its diagnoses that can help clinicians to develop an increasing level of trust towards the system. It also provides a practical database, which can be used as a structured history-taking assistant and a friendly, patient record-keeper, while improving precision by providing a full list of differential diagnoses. Importantly, the design and implementation of the system facilitates its continuous development with a set methodology that allows minimal revision of the system in the face of new information. Further large-scale studies are required to evaluate GAID more thoroughly and to identify its limiting boundaries.

This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient Boosting exhibited marginally superior predictive performance compared to Random Forest for LOS classification. Notably, variables like triage acuity and the Elixhauser Comorbidity Index (ECI) emerged as robust predictors. The extracted rules optimize LOS stratification and resource allocation, demonstrating the critical role of data-driven methodologies in improving ED workflow efficiency and patient care delivery.

The objective of this study was to develop explainable AI modeling in the prediction of cardiovascular disease. The XGBoost algorithm was used followed by rule extraction and argumentation theory that provides interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas. Our findings are in agreement with previous research utilizing the XGBoost machine learning algorithm for prediction of cardiovascular risk, however it is supported by rule based explainability, offering significant advantages in terms of providing both global and local explainability. Further work is needed to enhance the argumentation-based rule interpretability, explainability and accuracy in scenarios with low confidence results or dilemmas.

EDITORIAL article Front. Artif. Intell., 18 March 2024Sec. Machine Learning and Artificial Intelligence Volume 7 - 2024 | https://doi.org/10.3389/frai.2024.1382426

Gorgias Cloud offers an integrated application development environment that facilitates the development of argumentation-based systems over the internet. Argumentation is offered as a service in a way that this allows application systems to remotely access the argumentation service and utilize the results of the argumentative computation. Moreover, the service results include the explanation of the decision in both human and machine-readable formats. The first is useful for allowing the application validation to be done by experts, while the second is useful for development. It appears that this is the first case where argumentation is offered to developers in such an open and distributed way.

Artificial Intelligence in Medicine has made significant progress with emerging applications in medical imaging, patient care, and other areas. While these applications have proven successful in retrospective studies, very few of them were applied in practice.The field of Medical AI faces various challenges, in terms of building user trust, complying with regulations, using data ethically.Explainable AI (XAI) aims to enable humans understand AI and trust its results. This paper presents a literature review on the recent developments of XAI solutions for medical decision support, based on a representative sample of 198 articles published in recent years. The systematic synthesis of the relevant articles resulted in several findings. (1) model-agnostic XAI techniques were mostly employed in these solutions, (2) deep learning models are utilized more than other types of machine learning models, (3) explainability was applied to promote trust, but very few works reported the physicians participation in the loop, (4) visual and interactive user interface is more useful in understanding the explanation and the recommendation of the system. More research is needed in collaboration between medical and AI experts, that could guide the development of suitable frameworks for the design, implementation, and evaluation of XAI solutions in medicine.

The objective of this study was to implement an explainable artificial intelligence (AI) model with embedded rules to assess Multiple Sclerosis (MS) disease evolution based on brain Magnetic Resonance Imaging (MRI) multi-scale lesion evaluation. Amplitude Modulation-Frequency Modulation (AM-FM) features were extracted from manually segmented brain MS lesions obtained using MRI and were labeled with the Expanded Disability Status Scale (EDSS). Machine learning models were used to classify the MS subjects with a benign course of the disease and subjects with advanced accumulating disability. Rules were extracted from the selected model with high accuracy and then were modified to perform argumentation-based reasoning. It is demonstrated that the proposed explainable AI modeling can distinguish MS subjects and give meaningful information to track the progression of the disease. Future research will examine more subjects and add new feature sets and models.

The objective of this work was to investigate the usefulness of explainable AI (XAI) functionality in the Emergency Department (ED) triage hospitalization prediction modelling based on machine learning and rule extraction. Prediction modelling was carried out using the MIMIC-IV-ED dataset that contains over 400,000 ED visits for predicting hospitalisation Yes vs No (discharged). The Gradient Boosting (GB) prediction modelling gave the best performance achieving an accuracy, sensitivity and specificity of 83%, 82% and 84% respectively. These results are comparable with previous studies published in the literature. Subsequently, interpretable rules were extracted with a high precision threshold, demonstrating the rules’ overall accuracy in replicating the model’s behaviour. Further work is needed to validate the rules extracted with the ED medical experts as well as to benchmark the findings with other models and studies.Clinical Relevance: The use of explainable AI (XAI) in predicting hospitalization outcomes in the Emergency Department (ED) using machine learning and rule extraction, has the potential to improve clinical decision-making.

Multiple Sclerosis (MS) is characterized by complex and heterogeneous nature and as a result, there’s currently no cure. Medications can help control the progression and ease the symptoms of MS. The scientific interest in the field of explainable artificial intelligence (AI) comes to the surface and aims to assist computer-aided diagnostic systems to be established in medical use by providing understandable and transparent information to the experts. The objective of this study was to present different learning methods of explainable AI models in the assessment of MS disease based on clinical data and brain magnetic resonance imaging (MRI) lesion texture features and compare them by focusing on the main findings. The learning methods used machine learning and argumentation theory to differentiate subjects with relapsing-remitting MS (RRMS) from progressive MS (PMS) subjects and provide explanations. The results showed that the different learning methods achieved a high accuracy of 99% and gave similar explanations as they extracted the same set of rules. It is hoped that the proposed methodology could lead to personalized treatment in the management of MS disease.