Search Results (9)

Background: Hospital appointment systems suffer from extended patient waits, manual interventions, and suboptimal resource allocation, reducing satisfaction and efficiency. Methods: This study develops IPAS using Business Process Analysis (BPA), Bizagi modeling for As-Is/To-Be workflows, SWOT analysis, TQM, and Six Sigma DMAIC. It integrates ML/NLP with BioBERT-BiLSTM triage (AUC 0.92, F1 0.87) for symptom analysis, specialist matching, and automated booking, validated via Bizagi simulations. Results: Simulations show booking time was reduced 96.3% (155 to 5.73 min) and human intervention was cut 70%, with enhanced patient satisfaction and process capability. Conclusions: IPAS demonstrates simulation-based gains in scheduling efficiency, pending real-world validation. Full article

This paper presents the design, implementation and evaluation of an agentic virtual assistant (VA) for a medical clinic, combining large language models (LLMs) with retrieval-augmented generation (RAG) technology and multi-agent artificial intelligence (AI) frameworks to enhance reliability, clinical accuracy and explainability. The assistant has multiple functionalities and is built around an orchestrator architecture in which a central agent dynamically routes user queries to specialized tools for retrieval-augmented question answering (Q&A), document interpretation and appointment scheduling. The implementation combines LangChain and LangGraph with interactive visualizations to track reasoning steps, prompts using Gemini 2.5 Flash defines tool usage and strict formatting rules, maintaining reliability and mitigating hallucinations. Prompt engineering has an important role in the implementation and thus, it is designed to assist the patient in the human–computer interaction. Evaluation through qualitative and quantitative metrics, including ROUGE, BLEU, LLM-as-a-judge and sentiment analysis, confirmed that the multi-agent architecture enhances interpretability, accuracy and context-aware performance. Evaluation shows that the multi-agent architecture improves reliability, interpretability and alignment with medical requirements, supporting diverse clinical tasks. Furthermore, the evaluation shows that Gemini 2.5 Flash combined with clinic-specific RAG significantly improves response quality, grounding and coherence compared with earlier models. SBERT analyses confirm strong semantic alignment across configurations, while LLM-as-a-judge scores highlight the superior relevance and completeness of the 2.5 RAG setup. Although some limitations remain, the updated system provides a more reliable and context-aware solution for clinical question answering. Full article

Objective: The objective of this study is to synthesize and critically appraise how artificial intelligence (AI) is being integrated into oral and maxillofacial surgery (OMFS). This review’s novel contribution is to jointly map clinical applications (diagnostics, virtual surgical planning, intraoperative guidance) and operational uses (triage, scheduling, documentation, patient communication), quantifying evidence and validation status to provide practice-oriented guidance for adoption. Study Design: A narrative review of the recent literature and expert analysis, supplemented by illustrative multicenter implementation data from OMFS practice, was carried out. Results: AI demonstrates high performance in radiographic analysis and virtual planning (up to 96% predictive accuracy and sub-millimeter soft-tissue simulation error), with clinical reports of shorter planning times and more efficient patient communication. Early deployments in OMFS clinics have increased appointment bookings, while maintaining high patient satisfaction, and reduced the administrative burden. Remaining challenges include data quality, explainability, and limited multicenter and pediatric validation, which constrain generalizability and require clinician oversight. Conclusions: AI offers substantive benefits across the OMFS care continuum—improving diagnostic accuracy, surgical planning, and patient engagement while streamlining workflows. Responsible adoption depends on transparent validation, data governance, and targeted training, with attention to cost-effectiveness. Immediate priorities include standardized reporting of quantitative outcomes (e.g., sensitivity, specificity, time saved) and prospective multicenter studies, ensuring that AI augments—rather than replaces—human-centered care. Full article

This paper aims to leverage artificial intelligence (AI) to assist physicians in utilizing advanced deep learning techniques integrated into developed models within electronic health records (EHRs) in medical information systems (MISes), which have been in use for over 15 years in health centers across the Republic of Serbia. This paper presents a human-centered AI approach that emphasizes physician decision-making supported by AI models. This study presents two developed and implemented deep neural network (DNN) models in the EHR. Both models were based on data that were collected during the COVID-19 outbreak. The models were evaluated using five-fold cross-validation. The convolutional neural network (CNN), based on the pre-trained VGG19 architecture for classifying chest X-ray images, was trained on a publicly available smaller dataset containing 196 entries, and achieved an average classification accuracy of 91.83 ± 2.82%. The DNN model for optimizing patient appointment scheduling was trained on a large dataset (341,569 entries) and a rich feature design extracted from the MIS, which is daily used in Serbia, achieving an average classification accuracy of 77.51 ± 0.70%. Both models have consistent performance and good generalization. The architecture of a realized MIS, incorporating the positioning of developed AI tools that encompass both developed models, is also presented in this study. Full article

Background: With advancements in Generative Artificial Intelligence, various industries have made substantial efforts to integrate this technology to enhance the efficiency and effectiveness of existing processes or identify potential weaknesses. Context, however, remains a crucial factor in leveraging intelligence, especially in high-stakes sectors such as healthcare, where contextual understanding can lead to life-changing outcomes. Objective: This research aims to develop a practical medical multi-agent system framework capable of automating appointment scheduling and triage classification, thus improving operational efficiency in healthcare settings. Methods: We present MedScrubCrew, a multi-agent framework integrating established technologies: Gale-Shapley stable matching algorithm for optimal patient-provider allocation, knowledge graphs for semantic compatibility profiling, and specialized large language model-based agents. The framework is designed to emulate the collaborative decision making processes typical of medical teams. Results: Our evaluation demonstrates that combining these components within a cohesive multi-agent architecture substantially enhances operational efficiency, task completeness, and contextual relevance in healthcare scheduling workflows. Conclusions:MedScrubCrew provides a practical, implementable blueprint for healthcare automation, addressing significant inefficiencies in real-world appointment scheduling and patient triage scenarios. Full article

Vehicle owners often use certified service centers throughout the warranty period, which usually extends for five years after buying. Nonetheless, after this timeframe concludes, a large number of owners turn to unapproved service providers, mainly motivated by financial factors. This change signifies a significant drop in income for automakers and their certified service networks. To tackle this issue, manufacturers utilize customer relationship management (CRM) strategies to enhance customer loyalty, usually depending on segmentation methods to pinpoint potential clients. However, conventional approaches frequently do not successfully forecast which clients are most likely to need or utilize maintenance services. This research introduces a machine learning-driven framework aimed at forecasting the probability of monthly maintenance attendance for customers by utilizing an extensive historical dataset that includes information about both customers and vehicles. Additionally, this predictive approach supports workforce planning and scheduling within after-sales service centers, aligning with AI-driven labor optimization frameworks such as those explored in the AI4LABOUR project. Four algorithms in machine learning—Decision Tree, Random Forest, LightGBM (LGBM), and Extreme Gradient Boosting (XGBoost)—were assessed for their forecasting capabilities. Of these, XGBoost showed greater accuracy and reliability in recognizing high-probability customers. In this study, we propose a machine learning framework to predict vehicle maintenance visits for after-sales services, leading to significant operational improvements. Furthermore, the integration of AI-driven workforce allocation strategies, as studied within the AI4LABOUR (reshaping labor force participation with artificial intelligence) project, has contributed to more efficient service personnel deployment, reducing idle time and improving customer experience. By implementing this approach, we achieved a 20% reduction in information delivery times during service operations. Additionally, survey completion times were reduced from 5 min to 4 min per survey, resulting in total time savings of approximately 5906 h by May 2024. The enhanced service appointment scheduling, combined with timely vehicle maintenance, also contributed to reducing potential accident risks. Moreover, the transition from a rule-based maintenance prediction system to a machine learning approach improved efficiency and accuracy. As a result of this transition, individual customer service visit rates increased by 30%, while corporate customer visits rose by 37%. This study contributes to ongoing research on AI-driven workforce planning and service optimization, particularly within the scope of the AI4LABOUR project. Full article

This paper introduces the implementation of a chatbot on the web portal of a higher education institution, aiming to enhance student interaction and provide seamless access to information and support services. With the increasing reliance on digital platforms for student engagement, a chatbot offers a user-friendly and efficient means of communication, catering to the diverse needs of students in a higher education setting. The chatbot developed utilizes natural language processing, machine learning, and artificial intelligence algorithms to engage in dynamic conversations with students. We use Large Language Models (LLMs), because these and vector databases are revolutionizing the way we handle and retrieve complex data structures. Their main objective is to provide instant responses, personalized guidance, and timely support for various aspects of student life within the institution, namely the following: Information Retrieval, where the chatbot acts as a virtual collaborator, offering quick and accurate responses to frequently asked questions regarding admissions, programs, course registration, financial aid, and campus facilities, reducing the need for manual information searches; Academic Support, where the chatbot assists students in academic matters, such as course selection, prerequisites, graduation requirements, and study resources. It can offer personalized recommendations based on a student’s academic profile and preferences; Campus Services, which provides information about campus services, extracurricular activities, events, and resources; and Appointment Scheduling, which facilitates appointment scheduling with academic advisors, and support staff, streamlining administrative processes and ensuring timely access to guidance and assistance. This development follows a user-centric approach, incorporating feedback from students, faculty, and administrators to ensure that the chatbot meets their specific needs and preferences. Rigorous testing and quality assurance measures are implemented to guarantee the accuracy, reliability, and security of the chatbot. In conclusion, we achieve a functional chatbot with a medium computational heaviness; in this way, it can be practical to use it in real time by the students on the institution’s web portal. The introduction of a chatbot on the web portal of a higher education institution represents a significant advancement in facilitating student interaction and support services. By providing instant and personalized responses, the chatbot streamlines communication, reduces response times, and empowers students to find information and resources efficiently. As chatbot technology continues to evolve, ongoing enhancements and refinements will ensure that it remains a valuable tool for enhancing student experiences, promoting engagement, and fostering a positive learning environment within the institution. Full article

No-show appointments in healthcare is a problem faced by medical centers around the world, and understanding the factors associated with no-show behavior is essential. In recent decades, artificial intelligence has taken place in the medical field and machine learning algorithms can now work as an efficient tool to understand the patients’ behavior and to achieve better medical appointment allocation in scheduling systems. In this work, we provide a systematic literature review (SLR) of machine learning techniques applied to no-show appointments aiming at establishing the current state-of-the-art. Based on an SLR following the PRISMA procedure, 24 articles were found and analyzed, in which the characteristics of the database, algorithms and performance metrics of each study were synthesized. Results regarding which factors have a higher impact on missed appointment rates were analyzed too. The results indicate that the most appropriate algorithms for building the models are decision tree algorithms. Furthermore, the most significant determinants of no-show were related to the patient’s age, whether the patient missed a previous appointment, and the distance between the appointment and the patient’s scheduling. Full article

For many years, mental health has been hidden behind a veil of shame and prejudice. In 2017, studies claimed that 10.7% of the global population suffered from mental health disorders. Recently, people started seeking relaxing treatment through technology, which enhanced and expanded mental health care, especially during the COVID-19 pandemic, where the use of mental health forums, websites, and applications has increased by 95%. However, these solutions still have many limits, as existing mental health technologies are not meant for everyone. In this work, an up-to-date literature review on state-of-the-art of mental health and healthcare solutions is provided. Then, we focus on Arab-speaking patients and propose an intelligent tool for mental health intent recognition. The proposed system uses the concepts of intent recognition to make mental health diagnoses based on a bidirectional encoder representations from transformers (BERT) model and the International Neuropsychiatric Interview (MINI). Experiments are conducted using a dataset collected at the Military Hospital of Tunis in Tunisia. Results show excellent performance of the proposed system (the accuracy is over 92%, the precision, recall, and F1 scores are over 94%) in mental health patient diagnosis for five aspects (depression, suicidality, panic disorder, social phobia, and adjustment disorder). In addition, the tool was tested and evaluated by medical staff at the Military Hospital of Tunis, who found it very interesting to help decision-making and prioritizing patient appointment scheduling, especially with a high number of treated patients every day. Full article