Artificial Intelligence-based Decision Support System Research Articles

Artificial Intelligence-Based Decision Support System for Sustainable Urban Mobility

As urban populations rise globally, cities face increasing challenges in managing urban mobility. This paper addresses the question of identifying which modifications to introduce regarding city mobility by evaluating potential solutions using city-specific, subjective multi-objective criteria. The innovative AI-based recommendation engine assists city planners and policymakers in prioritizing key urban mobility aspects for effective policy proposals. By leveraging multi-criteria decision analysis (MCDA) and ±1/2 analysis, this engine provides a structured approach to systematically and simultaneously navigate the complexities of urban mobility planning. The proposed approach aims to provide an open-source interoperable prototype for all smart cities to utilize such recommendation systems routinely, fostering efficient, sustainable, and forward-thinking urban mobility strategies. Case studies from four European cities—Helsinki (tunnel traffic), Amsterdam (bicycle traffic for a new city quarter), Messina (adding another bus line), and Bilbao (optimal timing for closing the city center)—highlight the engine’s transformative potential in shaping urban mobility policies. Ultimately, this contributes to more livable and resilient urban environments, based on advanced urban mobility management.

Explainable Artificial Intelligence-Based Decision Support Systems: A Recent Review

This survey article provides a comprehensive overview of the evolving landscape of Explainable Artificial Intelligence (XAI) in Decision Support Systems (DSSs). As Artificial Intelligence (AI) continues to play a crucial role in decision-making processes across various domains, the need for transparency, interpretability, and trust becomes paramount. This survey examines the methodologies, applications, challenges, and future research directions in the integration of explainability within AI-based Decision Support Systems. Through an in-depth analysis of current research and practical implementations, this article aims to guide researchers, practitioners, and decision-makers in navigating the intricate landscape of XAI-based DSSs. These systems assist end-users in their decision-making, providing a full picture of how a decision was made and boosting trust. Furthermore, a methodical taxonomy of the current methodologies is proposed and representative works are presented and discussed. The analysis of recent studies reveals that there is a growing interest in applying XDSSs in fields such as medical diagnosis, manufacturing, and education, to name a few, since they smooth down the trade-off between accuracy and explainability, boost confidence, and also validate decisions.

Developing Robust AI Applications for Clinical Use: The Special Case of Pathology

Robustness is a key requirement for any method in medicine, especially when the method in question is being used as part of a diagnostic process. This is particularly true for artificial intelligence-based decision support systems, which, although being used as a supportive tool, will ultimately influence diagnostic assessments. In pathology, attaining clinical robustness in AI methods poses a particularly challenging task, primarily due to the extensive diversity of digital images, which humans can adapt to far more easily. This paper presents factors that contribute to this challenge, but also identifies and evaluates common solutions to counteract domain shift, which is known to deteriorate the performance of artificial intelligence models.

An artificial intelligence-based decision support system for classifying patients with adrenal tumors

An artificial intelligence-based decision support system for classifying patients with adrenal tumors

Decision support systems for antibiotic prescription in hospitals: a survey with hospital managers on factors for implementation.

Inappropriate antimicrobial use, such as antibiotic intake in viral infections, incorrect dosing and incorrect dosing cycles, has been shown to be an important determinant of the emergence of antimicrobial resistance. Artificial intelligence-based decision support systems represent a potential solution for improving antimicrobial prescribing and containing antimicrobial resistance by supporting clinical decision-making thus optimizing antibiotic use and improving patient outcomes. The aim of this research was to examine implementation factors of artificial intelligence-based decision support systems for antibiotic prescription in hospitals from the perspective of the hospital managers, who have decision-making authority for the organization. An online survey was conducted between December 2022 and May 2023 with managers of German hospitals on factors for decision support system implementation. Survey responses were analyzed from 118 respondents through descriptive statistics. Survey participants reported openness towards the use of artificial intelligence-based decision support systems for antibiotic prescription in hospitals but little self-perceived knowledge in this field. Artificial intelligence-based decision support systems appear to be a promising opportunity to improve quality of care and increase treatment safety. Along with the Human-Organization-Technology-fit model attitudes were presented. In particular, user-friendliness of the system and compatibility with existing technical structures are considered to be important for implementation. The uptake of decision support systems also depends on the ability of an organization to create a facilitating environment that helps to address the lack of user knowledge as well as trust in and skepticism towards these systems. This includes the training of user groups and support of the management level. Besides, it has been assessed to be important that potential users are open towards change and perceive an added value of the use of artificial intelligence-based decision support systems. The survey has revealed the perspective of hospital managers on different factors that may help to address implementation challenges for artificial intelligence-based decision support systems in antibiotic prescribing. By combining factors of user perceptions about the systems´ perceived benefits with external factors of system design requirements and contextual conditions, the findings highlight the need for a holistic implementation framework of artificial intelligence-based decision support systems.

Clinical Evaluation of an Artificial Intelligence-Based Decision Support System for the Diagnosis and American College of Radiology Thyroid Imaging Reporting and Data System Classification of Thyroid Nodules.

Background: This study aimed to evaluate the clinical impact of an artificial intelligence (AI)-based decision support system (DSS), Koios DS, on the analysis of ultrasound imaging and suspicious characteristics for thyroid nodule risk stratification. Methods: A retrospective ultrasound study was conducted on all thyroid nodules with histological findings from June 2021 to December 2022 in a thyroid nodule clinic. The diagnostic performance of ultrasound imaging was evaluated by six readers on the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) before and after the use of the AI-based DSS and by AI itself. Results: A total of 172 patients (83.1% women) with a mean age of 52.3 ± 15.3 years were evaluated. The mean maximum nodular diameter was 2.9 ± 1.2 cm, with 11.0% being differentiated thyroid carcinomas. Among the nodules initially classified as ACR TI-RADS 3 and 4, AI reclassified 81.4% and 24.5% into lower risk categories, respectively. Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic performance of the readers and the AI-based DSS versus histological diagnosis. There was an increase in the area under the ROC curve (AUROC) after the use of AI (0.776 vs. 0.817, p < 0.001). The AI-based DSS improved the mean sensitivity (Sens) (82.3% vs. 86.5%) and specificity (Spe) (38.3% vs. 54.8%), produced a high negative predictive value (94.5% vs. 96.4%), and increased the positive predictive value (PPV) (14.0% vs. 16.1%) and diagnostic precision (43.0% vs. 49.3%). Based on the ACR TI-RADS score, there was significant improvement in interobserver agreement after the use of AI (r = 0.741 for ultrasound imaging alone vs. 0.981 for ultrasound imaging and the AI-based DSS, p < 0.001). Conclusions: The use of an AI-based DSS was associated with overall improvement in the diagnostic efficacy of ultrasound imaging, based on the AUROC, as well as an increase in Sens, Spe, negative and PPVs, and diagnostic accuracy. There was also a reduction in interobserver variability and an increase in the degree of concordance with the use of AI. AI reclassified more than half of the nodules with intermediate ACR TI-RADS scores into lower risk categories.

Closed-Loop Control, Artificial Intelligence-Based Decision-Support Systems, and Data Science.

Closed-Loop Control, Artificial Intelligence-Based Decision-Support Systems, and Data Science.

An artificial intelligence-based decision support system for early and accurate diagnosis of Parkinson’s Disease

People with Parkinson’s Disease (PD) might struggle with sadness, restlessness, or difficulty speaking, chewing, or swallowing. A diagnosis can be challenging because there is no specific PD test. It is diagnosed by doctors using a neurological exam and a medical history. This study proposes several Machine Learning (ML) algorithms to predict PD. These ML algorithms include K-Nearest Neighbor (KNN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting algorithms (XGBoost), and their ensemble methods using publicly available PD dataset with 195 instances. The ML algorithms are used to predict and classify PD using homogeneous XGBoost ensemble techniques with reduced amount of entropy. Synthetic Minority Oversampling Technique (SMOTE) is utilized to handle imbalanced data, and 10-fold cross-validation is employed for evaluation. The results show that the homogeneous XGBoost-Random Forest outperforms other ML methods with 98% accuracy and Matthew’s correlation coefficient value 0.93.

Improving the performance of EM and K-means algorithms for breast lesion segmentation

Aims: Breast cancer is the most common type of cancer in women and accounts for a large portion of cancer-related deaths. As in the other types of cancer, the prevention and early diagnosis of breast cancer gain importance day after day. For this purpose, the artificial intelligence-based decision support systems become popular in recent years. In this study, an automatic breast lesion segmentation process is proposed to detect breast lesions in the images taken with magnetic resonance imaging (MRI) protocol. &#x0D; Methods: Two most popular segmentation methods: expectation maximization (EM) and K-means algorithms are used to determine the region of breast lesions. Furthermore, superpixel based fuzzy C-means (SPFCM) algorithm is applied after EM and K-means methods to improve the lesion segmentation performance.&#x0D; Results: The proposed methods are evaluated on the private database constructed by the authors with ethical permission. The performances of the utilized methods are analyzed by comparing the lesion areas determined by a radiologist (ground-truth) and areas that are achieved by automatic segmentation algorithms. &#x0D; Conclusion: Dice coefficient, Jaccard index (JI), and area under curve (AUC) metrics are calculated for performance comparison. According to the simulation results, EM, K-means, EM+SPFCM, and K-means+SPFCM methods provides good segmentation performance on breast MRI database. The best segmentation results are obtained by using EM+SPFCM hybrid method. The results of the EM+SPFCM method are 0,8711, 0,8979, and 0,9981 for JI, Dice, and AUC, respectively.

Assessment of an artificial intelligence-based decision support system in the thyroid nodule evaluation in clinical practice

Assessment of an artificial intelligence-based decision support system in the thyroid nodule evaluation in clinical practice

The need to strengthen the evaluation of the impact of Artificial Intelligence-based decision support systems on healthcare provision

Despite the renewed interest in Artificial Intelligence-based clinical decision support systems (AI-CDS), there is still a lack of empirical evidence supporting their effectiveness. This underscores the need for rigorous and continuous evaluation and monitoring of processes and outcomes associated with the introduction of health information technology.We illustrate how the emergence of AI-CDS has helped to bring to the fore the critical importance of evaluation principles and action regarding all health information technology applications, as these hitherto have received limited attention. Key aspects include assessment of design, implementation and adoption contexts; ensuring systems support and optimise human performance (which in turn requires understanding clinical and system logics); and ensuring that design of systems prioritises ethics, equity, effectiveness, and outcomes.Going forward, information technology strategy, implementation and assessment need to actively incorporate these dimensions. International policy makers, regulators and strategic decision makers in implementing organisations therefore need to be cognisant of these aspects and incorporate them in decision-making and in prioritising investment. In particular, the emphasis needs to be on stronger and more evidence-based evaluation surrounding system limitations and risks as well as optimisation of outcomes, whilst ensuring learning and contextual review. Otherwise, there is a risk that applications will be sub-optimally embodied in health systems with unintended consequences and without yielding intended benefits.

Toward Precision Medicine: Development and Validation of A Machine Learning Based Decision Support System for Optimal Sequencing in Castration-Resistant Prostate Cancer.

Selecting a patient-specific sequencing strategy to maximize survival outcomes is a clinically unmet need for patients with castration-resistant prostate cancer (CRPC). We developed and validated an artificial intelligence-based decision support system (DSS) to guide optimal sequencing strategy selection. Clinicopathological data of 46 covariates were retrospectively collected from 801 patients diagnosed with CRPC at 2 high-volume institutions between February 2004 and March 2021. Cox-proportional hazards regression survival (Cox) modeling in extreme gradient boosting (XGB) was used to perform survival analysis for cancer-specific mortality (CSM) and overall mortality (OM) according to the use of abiraterone acetate, cabazitaxel, docetaxel, and enzalutamide. The models were further stratified into first-, second-, and third-line models that each provided CSM and OM estimates for each line of treatment. The performances of the XGB models were compared with those of the Cox models and random survival forest (RSF) models in terms of Harrell's C-index. The XGB models showed greater predictive performance for CSM and OM compared to the RSF and Cox models. C-indices of 0.827, 0.807, and 0.748 were achieved for CSM in the first-, second-, and third-lines of treatment, respectively, while C-indices of 0.822, 0.813, and 0.729 were achieved for OM regarding each line of treatment, respectively. An online DSS was developed to provide visualization of individualized survival outcomes according to each line of sequencing strategy. Our DSS can be used in clinical practice by physicians and patients as a visualized tool to guide the sequencing strategy of CRPC agents.

Development of Artificial Intelligence in the Dispute Resolution of Religious Courts

The number of disputes in religious courts continues to increase every year, giving great potential to develop the use of artificial intelligence to make it more efficient and effective by paying attention to the basic principles of Islamic law. This paper discusses the potential and development of the use of artificial intelligence technology in dispute cases in religious courts based on Maslahah Mursalah. The research method is juridical-normative, with a conceptual and philosophical approach. The research shows that the use of artificial intelligence in dispute resolution at religious courts is important for the reason that it can minimize the risk of errors and help judges determine legal considerations in the dispute resolution of Islamic law so that they are more accurate, effective, and efficient. The potential for developing the use of artificial intelligence technology in religious courts must consider several aspects, including the integration of artificial intelligence technology in the dispute resolution process in religious courts, the development of artificial intelligence-based decision support systems, and legal-ethical aspects of the use of artificial intelligence in religious courts. The formulation of the concept for developing the use of artificial intelligence technology in dispute resolution in the religious courts must be guided by the Maslahah Mursalah principle as a moral-ethical foundation within the legal system framework, which includes legal structure, legal substance, and legal culture so that the development of the use of artificial intelligence technology in the disputes resolution of Islamic law in religious courts be effective and justice for all.

AI-Based Decision Support System for Traumatic Brain Injury: A Survey.

Traumatic brain injury (TBI) is one of the major causes of disability and mortality worldwide. Rapid and precise clinical assessment and decision-making are essential to improve the outcome and the resulting complications. Due to the size and complexity of the data analyzed in TBI cases, computer-aided data processing, analysis, and decision support systems could play an important role. However, developing such systems is challenging due to the heterogeneity of symptoms, varying data quality caused by different spatio-temporal resolutions, and the inherent noise associated with image and signal acquisition. The purpose of this article is to review current advances in developing artificial intelligence-based decision support systems for the diagnosis, severity assessment, and long-term prognosis of TBI complications.

Assesmement of An Artificial Intelligence-Based Decision Support System in The Thyroid Nodule Evaluation in Clinical Practice

Searchable abstracts of presentations at key conferences in endocrinology ISSN 1470-3947 (print) | ISSN 1479-6848 (online)

An artificial intelligence-based decision support system for early diagnosis of polycystic ovaries syndrome

Polycystic ovary syndrome (PCOS) can affect a female’s reproductive system and comes with associated complications to the endocrine system of the affected individual. The diagnosis success of the condition varies depending on the stage of the disease. Thus, there is a need for investment in additional technologies that can help bolster the overall diagnosis success of the condition and to cue in prompt care strategies. A substantial amount of work has been done on this, where artificial intelligence technology has been investigated around the exploitation of patient medical health records towards predicting whether a patient is carrying the PCOS condition. The shortcomings associated with this related literature are based on the use of an unbalanced dataset towards the training of the candidate models, which can induce a form of model bias and approach the problem as a binary-based prediction exercise. This study aims at providing a solution to this apparent gap in knowledge by designing a prediction model using the Kaggle PCOS dataset, which is initially balanced using a synthetic sample generation algorithm. Next, a probability-based inference system is designed to estimate and stage the degree of the PCOS condition in the patient.

Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered "gold standard". Matthew's correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to "gold standard" (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Fully automated bladder tumor segmentation from T2 MRI images using 3D U-Net algorithm.

Bladder magnetic resonance imaging (MRI) has been recently integrated in the diagnosis pathway of bladder cancer. However, automatic recognition of suspicious lesions is still challenging. Thus, development of a solution for proper delimitation of the tumor and its separation from the healthy tissue is of primordial importance. As a solution to this unmet medical need, we aimed to develop an artificial intelligence-based decision support system, which automatically segments the bladder wall and the tumor as well as any suspect area from the 3D MRI images. We retrospectively assessed all patients diagnosed with bladder cancer, who underwent MRI at our department (n=33). All examinations were performed using a 1.5 Tesla MRI scanner. All images were reviewed by two radiologists, who performed manual segmentation of the bladder wall and all lesions. First, the performance of our fully automated end-to-end segmentation model based on a 3D U-Net architecture (by considering various depths of 4, 5 or 6 blocks) trained in two data augmentation scenarios (on 5 and 10 augmentation datasets per original data, respectively) was tested. Second, two learning setups were analyzed by training the segmentation algorithm with 7 and 14 MRI original volumes, respectively. We obtained a Dice-based performance over 0.878 for automatic segmentation of bladder wall and tumors, as compared to manual segmentation. A larger training dataset using 10 augmentations for 7 patients could further improve the results of the U-Net-5 model (0.902 Dice coefficient at image level). This model performed best in terms of automated segmentation of bladder, as compared to U-Net-4 and U-Net-6. However, in this case increased time for learning was needed as compared to U-Net-4. We observed that an extended dataset for training led to significantly improved segmentation of the bladder wall, but not of the tumor. We developed an intelligent system for bladder tumors automated diagnostic, that uses a deep learning model to segment both the bladder wall and the tumor. As a conclusion, low complexity networks, with less than five-layers U-Net architecture are feasible and show good performance for automatic 3D MRI image segmentation in patients with bladder tumors.

Closed-Loop and Artificial Intelligence-Based Decision Support Systems.

Closed-Loop and Artificial Intelligence-Based Decision Support Systems.

Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

As the world population is expected to touch 9.73 billion by 2050, according to the Food and Agriculture Organization (FAO), the demand for agricultural needs is increasing proportionately. Smart Agriculture is replacing conventional farming systems, employing advanced technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Machine Learning (ML) to ensure higher productivity and precise agriculture management to overcome food demand. In recent years, there has been an increased interest in researchers within Smart Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, there is a lack of research in Operations Research (OR) insights into Smart Agriculture. This paper conducts a Bibliometric Analysis of past research work in OR knowledge which has been done over the last two decades in Agriculture 4.0, to understand the trends and the gaps. Biblioshiny, an advanced data mining tool, was used in conducting bibliometric analysis on a total number of 1,305 articles collected from the Scopus database between the years 2000–2022. Researchers and decision makers will be able to visualize how newer advanced OR theories are being applied and how they can contribute toward some research gaps highlighted in this review paper. While governments and policymakers will benefit through understanding how Unmanned Aerial Vehicles (UAV) and robotic units are being used in farms to optimize resource allocation. Nations that have arid climate conditions would be informed how satellite imagery and mapping can assist them in detecting newer irrigation lands to assist their scarce agriculture resources.