AI-based Decision Support Systems Research Articles

Enable Flexibilisation in FAIRWork’s Democratic AI-based Decision Support System by Applying Conceptual Models Using ADOxx

Decision-making in complex production environments is challenging as the information and knowledge requirements must be constantly observed since the ecosystems they operate in are continuously changing. Artificial intelligence (AI) can tackle complexity in decision-making by making machines more intelligent. But reacting to changing or new problems and related decision processes to facilitate the understanding of the involved humans is an equally important problem. Therefore, decision support systems are required to assist complex decisions and enable flexibility to support the decision-makers. Within this scope, we will introduce the Democratic AI-based Decision Support System (DAI-DSS), which is designed and implemented within the EU-funded FAIRWork project, considering both human and machine actors during decision-making. The FAIRWork project proposes a model-based approach to both express high-level decision scenarios and formally describe the decision processes, which are then used as input for configuring the decision support system to meet concrete decision problems.

Effects of explainable artificial intelligence in neurology decision support.

Artificial intelligence (AI)-based decision support systems (DSS) are utilized in medicine but underlying decision-making processes are usually unknown. Explainable AI (xAI) techniques provide insight into DSS, but little is known on how to design xAI for clinicians. Here we investigate the impact of various xAI techniques on a clinician's interaction with an AI-based DSS in decision-making tasks as compared to a general population. We conducted a randomized, blinded study in which members of the Child Neurology Society and American Academy of Neurology were compared to a general population. Participants received recommendations from a DSS via a random assignment of an xAI intervention (decision tree, crowd sourced agreement, case-based reasoning, probability scores, counterfactual reasoning, feature importance, templated language, and no explanations). Primary outcomes included test performance and perceived explainability, trust, and social competence of the DSS. Secondary outcomes included compliance, understandability, and agreement per question. We had 81 neurology participants with 284 in the general population. Decision trees were perceived as the more explainable by the medical versus general population (P < 0.01) and as more explainable than probability scores within the medical population (P < 0.001). Increasing neurology experience and perceived explainability degraded performance (P = 0.0214). Performance was not predicted by xAI method but by perceived explainability. xAI methods have different impacts on a medical versus general population; thus, xAI is not uniformly beneficial, and there is no one-size-fits-all approach. Further user-centered xAI research targeting clinicians and to develop personalized DSS for clinicians is needed.

Humans Supervising Artificial Intelligence – Investigation of Designs to Optimize Error Detection

ABSTRACT Artificial Intelligence (AI) fundamentally changes the way we work by introducing new capabilities. Human tasks shift towards a supervising role where the human confirms or disconfirms the presented decision. In this study, we utilise the signal detection theory to investigate and explain how the performance of human error detection is influenced by specific information design. We conducted two online experiments in the context of AI-supported information extraction and measured the ability of participants to validate the extracted information. In the first experiment, we investigated the mechanism of information provided prior to conducting the error detection task. In the second experiment, we manipulated the design of the presented information during the task and investigated its effect. Both manipulations significantly impacted the error detection performance of humans. Hence our study provides important insights for developing AI-based decision support systems and contributes to the theoretical understanding of human-AI collaboration.

Improving Trust in AI with Mitigating Confirmation Bias: Effects of Explanation Type and Debiasing Strategy for Decision-Making with Explainable AI

With advancements in artificial intelligence (AI), explainable AI (XAI) has emerged as a promising tool for enhancing the explainability of complex machine learning models. However, the explanations generated by an XAI may lead to cognitive biases among human users. To address this problem, this study aims to investigate how to mitigate users’ cognitive biases based on their individual characteristics. In the literature review, we found two factors that can be helpful in remedying biases: 1) debiasing strategies that have been reported to potentially reduce biases in users’ decision-making via additional information or change in information delivery, and 2) explanation modality types. To examine these factors’ effects, we conducted an experiment with a 4 (debiasing strategy) × 3 (explanation type) between-subject design. In the experiment, participants were exposed to an explainable interface that provides an AI’s outcomes with explanatory information, and their behavioral and attitudinal responses were collected. Specifically, we statistically examined the effects of textual and visual explanations on users’ trust and confirmation bias toward AI systems, considering the moderating effects of debiasing methods and watching time. The results demonstrated that textual explanations lead to higher trust in XAI systems compared to visual explanations. Moreover, we found that textual explanations are particularly beneficial for quick decision-makers to evaluate the outputs of AI systems. Next, the results indicated that the cognitive bias can be effectively mitigated by providing users with a priori information. These findings have theoretical and practical implications for designing AI-based decision support systems that can generate more trustworthy and equitable explanations.

A global cyber-threat intelligence system with artificial intelligence and convolutional neural network

Global cyber-attacks significantly impact the economy, society, organizations, and individuals. Existing research on cyber-attacks lacks in demonstrating Artificial Intelligence (AI) based analytical solutions for providing country-wide cyber threat intelligence. Cyber strategists at a national level require AI-based decision support systems for deciding a country’s cyber posture or preparedness. This paper proposes an AI-based solution that autonomously collects multidimensional cyber-attack data on social media posts on cyber-related outcry. The proposed system provides critical analytical capability in the cyber-threat spectrum and uses sophisticated AI-based algorithms for anomaly detection, prediction, sentiment analysis, location detection, translation, etc. The proposed system was deployed from 11 October 2022 to 31 October 2022. During these 21 days, the system autonomously collected 30,203 records on cyber threats covering multiple dimensions of cyber-threat. These dimensions included country-wide daily cyber-attack records by ransomware, exploits, web threats, spam, malicious mail, network attacks, local infections, and on-demand scan. Moreover, the system performed AI-based acquisition and analysis of 3789 cyber-related tweets from 3402 tweet users in 37 different languages. The system also autonomously translated 893 non-English tweets. The proposed system is the first solution that uses Convolutional Neural Network (CNN) based anomaly detection to detect abnormalities in cyber-threat spectrum worldwide and predict cyber-attacks automatically. The proposed system was demonstrated to provide evidence-based decisions on global cyber threats in multiple platforms, including iOS, Android, and Windows.

Multi-class classification of thyroid nodules from automatic segmented ultrasound images: Hybrid ResNet based UNet convolutional neural network approach

Background and objectivesEarly detection and diagnosis of thyroid nodule types are important because they can be treated more effectively in their early stages. The types of thyroid nodules are generally stated as atypia of undetermined significance/follicular lesion of undetermined significance (AUS/FLUS), benign follicular, and papillary follicular. The risk of malignancy for AUS/FLUS is typically stated to be between 5 and 15 %, while some studies indicate a risk as high as 25 %. Without complete histology, it is difficult to classify nodules and these diagnostic operations are pricey and risky. To minimize laborious workload and misdiagnosis, recently various AI-based decision support systems have been developed. MethodsIn this study, a novel AI-based decision support system has been developed for the automated segmentation and classification of the types of thyroid nodules. This system is based on a hybrid deep-learning procedure that makes both an automatic thyroid nodule segmentation and classification tasks, respectively. In this framework, the segmentation is executed with some U-Net architectures such as ResUNet and ResUNet++ integrating with the feature extraction and upsampling with dropout operations to prevent overfitting. The nodule classification task is achieved by various deep nets architecture such as VGG-16, DenseNet121, ResNet-50, and Inception ResNet-v2 considering some accurate classification criteria such as Intersection over Union (IOU), Dice coefficient, accuracy, precision, and recall. ResultsIn analysis, a total of 880 patients with ages ranging from 10 to 90 years were included by taking the ultrasound images and demographics. The experimental evaluations showed that ResUNet++ demonstrated excellent segmentation outcomes, attaining remarkable evaluation scores including a dice coefficient of 92.4 % and a mean IOU of 89.7 %. ResNet-50 and Inception ResNet-v2 trained over the images segmented with UNets have shown better performance in terms of achieving high evaluation scores for the classification accuracy such as 96.6 % and 95.0 %, respectively. In addition, ResNet-50 and Inception ResNet-v2 classified AUS/FLUS from the images segmented with UNets with AUC=97.0 % and 96.0 %, respectively. ConclusionsThe proposed AI-based decision support system improves the automatic segmentation performance of AUS/FLUS and it has shown better performance than available approaches in the literature with respect to ACC, Jaccard and DICE losses. This system has great potential for clinical use by both radiologists and surgeons as well.

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

This study investigates the transformative impact of AI-based Decision Support Systems (DSS) and Big Data Analytics (BDA) on student innovation and employability skills in an era of rapid technological advancement, with a focus on the mediating role of technological acceptance and the moderating role of resource availability. This study, which draws on a wide range of educational contexts and data sources, gives complete knowledge of the complex links between technology adoption, student results, and contextual factors. The results of this study show how AI-based DSS and BDA have a significant impact on musical education. These technological advancements enable tailored instruction and foster students' creative thinking. In order to prepare students for a work market that is rapidly changing, they act as a catalyst for improving employability skills. The study, however, emphasizes the complicated dynamics at work. Technological Acceptance emerges as a major mediating component, underlining the significance of students and instructors freely and effectively accepting technological tools. Furthermore, as a moderating factor, Resource Availability takes center stage, emphasizing the need for equitable access to educational resources to ensure that technology-driven advantages are accessible to all. The results of this study have broad repercussions. The adoption of AI and BDA by educational institutions is encouraged as transformative technologies for enhancing the learning process. Policymakers must create regulations that support equal access to technology and promote an innovative culture in the classroom. This study highlights for students how important it is to adopt new technologies, realizing how important they are in determining both their academic and career paths.

Psychological assessment of AI-based decision support systems: tool development and expected benefits.

This study aimed to develop an evaluation tool that assesses the use of AI-based decision support systems (DSSs) in professional practice from a human-centered perspective. Following the International Organization for Standardization, this perspective aims to ensure that the use of interactive technologies improves users' psychological load experience and behavior, e.g., in the form of reduced stress experience or increased performance. Concomitantly, this perspective attempts to proactively prevent or detect and correct the potential negative effects of these technologies on user load, such as impaired satisfaction and engagement, as early as possible. Based on this perspective, we developed and validated a questionnaire instrument, the Psychological Assessment of AI-based DSSs (PAAI), for the user-centered evaluation of the use of AI-based DSSs in practice. In particular, the instrument considers central design characteristics of AI-based DSSs and the corresponding work situation, which have a significant impact on users' psychological load. The instrument was tested in two independent studies. In Study 1, N = 223 individuals were recruited. Based on the results of item and scale analyses and an exploratory factor analysis, the newly developed instrument was refined, and the final version was tested using a confirmatory factor analysis. Findings showed acceptable-to-good fit indices, confirming the factorial validity of the PAAI. This was confirmed in a second study, which had N = 471 participants. Again, the CFA yielded acceptable-to-good fit indices. The validity was further confirmed using convergent and criterion validity analyses.

AI-based decision support system for public procurement

Tenders are powerful means of investment of public funds and represent a strategic development resource. Thus, improving the efficiency of procuring entities and developing evaluation models turn out to be essential to facilitate e-procurement procedures. With this contribution, we introduce our research to create a supporting system for the decision-making and monitoring process during the entire course of investments and contracts. This system employs artificial intelligence techniques based on natural language processing, focused on providing instruments for extracting useful information from both structured and unstructured (i.e., text) data. Therefore, we developed a framework based on a web app that provides integrated tools such as a semantic search engine, a summariser, an open information extraction engine in the form of triples (subject–predicate–object) for tender documents, and dashboards for analysing tender data.

Barriers and Enablers for Implementation of an Artificial Intelligence-Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews.

Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.

Factors influencing the implementation of AI-based decision support systems for antibiotic prescription in hospitals: a qualitative analysis from the perspective of health professionals

Decision support systems based on artificial intelligence might optimize antibiotic prescribing in hospitals and prevent the development of antimicrobial resistance. The aim of this study was to identify impeding and facilitating factors for successful implementation from the perspective of health professionals. Problem-centered individual interviews were conducted with health professionals working in hospitals. Data evaluation was based on the structured qualitative content analysis according Kuckartz. Attitudes of health professionals were presented along the Human-Organization -Technology-fit model. Technological and organizational themes were the most important factors for system implementation. Especially, compatibility with existing systems and user-friendliness were seen to play a major role in successful implementation. Additionally, the training of potential users and the technical equipment of the organization were considered essential. Finally, the importance of promoting technical skills of potential users in the long term and creating trust in the benefits of the system were highlighted. The identified factors provide a basis for prioritizing and quantifying needs and attitudes in a next step. It becomes clear that, beside technological factors, attention to context-specific and user-related conditions are of fundamental importance to ensure successful implementation and system trust in the long term.

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.

Check the box! How to deal with automation bias in AI-based personnel selection.

Artificial Intelligence (AI) as decision support for personnel preselection, e.g., in the form of a dashboard, promises a more effective and fairer selection process. However, AI-based decision support systems might prompt decision makers to thoughtlessly accept the system's recommendation. As this so-called automation bias contradicts ethical and legal requirements of human oversight for the use of AI-based recommendations in personnel preselection, the present study investigates strategies to reduce automation bias and increase decision quality. Based on the Elaboration Likelihood Model, we assume that instructing decision makers about the possibility of system errors and their responsibility for the decision, as well as providing an appropriate level of data aggregation should encourage decision makers to process information systematically instead of heuristically. We conducted a 3 (general information, information about system errors, information about responsibility) x 2 (low vs. high aggregated data) experiment to investigate which strategy can reduce automation bias and enhance decision quality. We found that less automation bias in terms of higher scores on verification intensity indicators correlated with higher objective decision quality, i.e., more suitable applicants selected. Decision makers who received information about system errors scored higher on verification intensity indicators and rated subjective decision quality higher, but decision makers who were informed about their responsibility, unexpectedly, did not. Regarding aggregation level of data, decision makers of the highly aggregated data group spent less time on the level of the dashboard where highly aggregated data were presented. Our results show that it is important to inform decision makers who interact with AI-based decision-support systems about potential system errors and provide them with less aggregated data to reduce automation bias and enhance decision quality.

Artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health: An integrative review.

An integrative review investigating the incorporation of artificial intelligence (AI) and machine learning (ML) based decision support systems in mental health care settings was undertaken of published literature between 2016 and 2021 across six databases. Four studies met the research question and the inclusion criteria. The primary theme identified was trust and confidence. To date, there is limited research regarding the use of AI-based decision support systems in mental health. Our review found that significant barriers exist regarding its incorporation into practice primarily arising from uncertainty related to clinician's trust and confidence, end-user acceptance and system transparency. More research is needed to understand the role of AI in assisting treatment and identifying missed care. Researchers and developers must focus on establishing trust and confidence with clinical staff before true clinical impact can be determined. Finally, further research is required to understand the attitudes and beliefs surrounding the use of AI and related impacts for the wellbeing of the end-users of care. This review highlights the necessity of involving clinicians in all stages of research, development and implementation of artificial intelligence in care delivery. Earning the trust and confidence of clinicians should be foremost in consideration in implementation of any AI-based decision support system. Clinicians should be motivated to actively embrace the opportunity to contribute to the development and implementation of new health technologies and digital tools that assist all health care professionals to identify missed care, before it occurs as a matter of importance for public safety and ethical implementation. AI-basesd decision support tools in mental health settings show most promise as trust and confidence of clinicians is achieved.

THREAT MODEL OF DECISION SUPPORT SYSTEMS OPERATING ON THE BASIS OF ELEMENTS OF ARTIFICIAL INTELLIGENCE

The modern development of artificial intelligence (AI) affects many aspects of society's life and activities, including decision support systems. AI is already successfully implemented in various fields such as business, medicine, automated data processing and many others. However, this development also brings with it various threats and challenges that require careful study and security measures. This article is devoted to the development and analysis of a threat model for decision support systems that function on the basis of elements of artificial intelligence (AI). The relevance of the research lies in the rapid development of AI technologies and their increasingly widespread use in various fields, including management, medicine, finance and many others. The article describes the structure and main component models of threats that may arise in AI-based decision support systems. An overview of existing attacks on artificial intelligence (AI) is given, which cover the stages of training, the use of machine learning algorithms, as well as the information infrastructure of the system. Classification of attack data was carried out, based on the analysis of the AI functioning process, and attack categories most relevant and dangerous for AI systems were identified. A threat model for a decision support system based on artificial intelligence technologies is proposed, the feature of which is the consideration of threats to the confidentiality of data of AI systems, threats to the functioning of AI systems, and threats to information systems. This model is of practical importance for developers of AI systems and for specialists in the field of cyber security, as it contributes to increasing the level of security and reliability of decision support systems based on artificial intelligence elements in the modern digital environment.

Determining the reliability of diagnosis and treatment using artificial intelligence software with panoramic radiographs.

The objective of this study was to evaluate the accuracy and effectiveness of an artificial intelligence (AI) program in identifying dental conditions using panoramic radiographs (PRs), as well as to assess the appropriateness of its treatment recommendations. PRs from 100 patients (representing 4497 teeth) with known clinical examination findings were randomly selected from a university database. Three dentomaxillofacial radiologists and the Diagnocat AI software evaluated these PRs. The evaluations were focused on various dental conditions and treatments, including canal filling, caries, cast post and core, dental calculus, fillings, furcation lesions, implants, lack of interproximal tooth contact, open margins, overhangs, periapical lesions, periodontal bone loss, short fillings, voids in root fillings, overfillings, pontics, root fragments, impacted teeth, artificial crowns, missing teeth, and healthy teeth. The AI demonstrated almost perfect agreement (exceeding 0.81) in most of the assessments when compared to the ground truth. The sensitivity was very high (above 0.8) for the evaluation of healthy teeth, artificial crowns, dental calculus, missing teeth, fillings, lack of interproximal contact, periodontal bone loss, and implants. However, the sensitivity was low for the assessment of caries, periapical lesions, pontic voids in the root canal, and overhangs. Despite the limitations of this study, the synthesized data suggest that AI-based decision support systems can serve as a valuable tool in detecting dental conditions, when used with PR for clinical dental applications.

Preparing Psychologists and Social Workers for the Daily Use of AI

A daily use of Artificial Intelligence (AI) is becoming a fact in many fields today, and two of them are psychology and social work. At the same time as AI systems are used for predicting psychological treatments and for decisions in social welfare, higher education has few AI courses for these professions. Moreover, there are several examples in these fields where AI can make unethical decisions that need to be corrected by humans. To better understand the possibilities and challenges of AI in psychology and social work, professional users of AI services need a tailored education on how the underlying technology works. The aim of this paper is to present a project concept for the design and evaluation of a novel course in AI for professional development in psychology and social work. For the design and development of the course the guiding research question should be: What are the strengths and challenges with contemporary AI techniques regarding prediction, adaptivity and decision systems? The suggested AI course should be given as a technology enhanced online training to enable the idea of anytime and anywhere for full-time working participants. Course content and activities are divided into the four separate sections of: 1) The history of AI structured around the 'Three waves of AI', with o focus on the current third wave. 2) A section with a focus on AI techniques for prediction and adaptivity. Underlying techniques such as machine learning, neural networks, and deep learning will be conceptually described and discussed, but not on a detailed level. 3) An elaborated discussion on the relevance, usefulness and trust, and the at the difference between AI-based decision systems and AI-based decision support systems. 4) Finally, the fourth section should comprise the ethical aspects of AI, and discuss transparency and Explainable AI. An innovative approach of the project is to use a neuroscientific assessment of the education to understand how the education changes brain function relevant to evaluate AI based decision. This should be complemented with a qualitative evaluation based on semi-structured interviews.

AI and housing discrimination: the case of mortgage applications

Issues surrounding bias and discrimination in housing markets have been acknowledged and discussed both in the literature and in practice. In this study, we investigate this issue specifically in the context of mortgage applications through the lens of an AI-based decision support system. Using the data provided as a part of the Home Mortgage Disclosure Act (HMDA), we first show that ethnicity bias does indeed exist in historical mortgage application approvals, where black applicants are more likely to be declined a mortgage compared with white applicants whose circumstances are otherwise similar. More interestingly, this bias is amplified when an off-the-shelf machine-learning model is used to recommend an approval/denial decision. Finally, when fair machine-learning algorithms are adopted to alleviate such biases, we find that the “fairness” actually leaves all stakeholders—black applicants, white applicants, and mortgage lenders—worse off. Our findings caution against the use of machine-learning models without human involvement when the decision has significant implications for the prediction subjects.

Clinicians' Perceptions of an Artificial Intelligence-Based Blood Utilization Calculator: Qualitative Exploratory Study.

According to the US Food and Drug Administration Center for Biologics Evaluation and Research, health care systems have been experiencing blood transfusion overuse. To minimize the overuse of blood product transfusions, a proprietary artificial intelligence (AI)-based blood utilization calculator (BUC) was developed and integrated into a US hospital's electronic health record. Despite the promising performance of the BUC, this technology remains underused in the clinical setting. This study aims to explore how clinicians perceived this AI-based decision support system and, consequently, understand the factors hindering BUC use. We interviewed 10 clinicians (BUC users) until the data saturation point was reached. The interviews were conducted over a web-based platform and were recorded. The audiovisual recordings were then anonymously transcribed verbatim. We used an inductive-deductive thematic analysis to analyze the transcripts, which involved applying predetermined themes to the data (deductive) and consecutively identifying new themes as they emerged in the data (inductive). We identified the following two themes: (1) workload and usability and (2) clinical decision-making. Clinicians acknowledged the ease of use and usefulness of the BUC for the general inpatient population. The clinicians also found the BUC to be useful in making decisions related to blood transfusion. However, some clinicians found the technology to be confusing due to inconsistent automation across different blood work processes. This study highlights that analytical efficacy alone does not ensure technology use or acceptance. The overall system's design, user perception, and users' knowledge of the technology are equally important and necessary (limitations, functionality, purpose, and scope). Therefore, the effective integration of AI-based decision support systems, such as the BUC, mandates multidisciplinary engagement, ensuring the adequate initial and recurrent training of AI users while maintaining high analytical efficacy and validity. As a final takeaway, the design of AI systems that are made to perform specific tasks must be self-explanatory, so that the users can easily understand how and when to use the technology. Using any technology on a population for whom it was not initially designed will hinder user perception and the technology's use.

Impact of cognitive workload and situation awareness on clinicians’ willingness to use an artificial intelligence system in clinical practice

Determinants of technology acceptance are multifaceted, particularly for artificial intelligence (AI) in healthcare. Using AI might impact users’ cognitive workload and situation awareness. This study explores the moderating effect of clinicians’ situation awareness and workload on the interaction between trust, risk, and intent to use an AI-based decision support system known as the blood utilization calculator (BUC). The study took place at an academic hospital in Wisconsin, US. A purposeful sampling strategy was utilized to recruit 119 BUC users. The data was collected via an online validated survey. The study leveraged Hayes PROCESS to capture the moderation effect of situation awareness and cognitive workload on the relationship between perceived risk and trust. The study also reports the significant impact of situation awareness (positively) and cognitive workload (negatively) on intent to use BUC. Adding to the body of knowledge, our study advocates for minimal cognitive workload and optimal situation awareness in healthcare.