Trust in Artificial Intelligence: Clinicians Are Essential

The use of Artificial Intelligence (AI) in healthcare is growing quickly and offers big improvements in medical diagnostics, treatment planning, and patient care. However, people often don't trust AI systems, which prevents them from being widely used. This article looks at both the philosophical and practical issues of trust in healthcare AI systems. First, we provide an overview of the current state of AI in healthcare. Then, we review existing research on trust in technology. Based on our findings, we identify three main factors that affect trust in AI: Technology-Related Factors (transparency, reliability, safety), Healthcare Context Factors (how well AI fits into healthcare settings, proper training for professionals), and Individual User Factors (user experience and attitudes toward AI). Our results show that continuous human oversight, strong regulations, and ethical considerations are essential. Addressing these areas is key to making sure AI systems in healthcare are reliable, transparent, and trusted by both healthcare professionals and patients.

As artificial intelligence (AI) becomes increasingly integral to our lives, ensuring these systems are trustworthy and transparent is paramount. The concept of explainability has emerged as a crucial element in fostering trust within AI systems. Nevertheless, the dynamics between explainability and trust in AI are intricate and not fully comprehended. This paper delves into the nexus between explainability and trust in AI, offering perspectives on crafting AI systems that users can rely on. Through an examination of existing literature, we investigate how transparency, accountability, and human oversight influence trust in AI systems and assess how various explainability approaches contribute to trust enhancement. Utilizing a set of experiments, our research examines how different explanatory models impact users' trust in AI systems, revealing that the nature and quality of explanations have a significant influence on trust levels. Additionally, we scrutinize the balance between explainability and accuracy in AI systems, discussing its implications for the development of reliable AI. This study underscores the critical role of explainability in engendering trust in AI systems, providing guidance on the development of AI systems that are both transparent and trustworthy, thereby fostering confidence among users.

The rapid growth in adoption of artificial intelligence (AI) for decision-making processes, administrative functions and academic services has created an environment of rapid change throughout the university setting. As technology advances, concerns surrounding transparency, fairness and accountability of algorithms also arise; these issues affect how employees of universities perceive and trust AI systems. Research on AI governance and trust in organisations will be utilised in this study to explore how transparency and communication among leaders impact employee trust in AI as a governance tool within universities. Data from academic and administrative staff from universities that are currently undergoing transformation through digitalisation influenced by AI will be analysed using a structural equation modelling (SEM) method. The study will propose that algorithm transparency has both a direct and indirect positive correlation to employee trust through perceived organisational fairness. Additionally, the relationship between algorithm transparency and employee trust will be moderated by leader communication through reducing uncertainty and creating understanding of AI as a decision tool. Overall, this research is intended to advance the body of literature related to human-centred governance of AI by identifying socio-organisational mechanisms through which employee trust in AI systems develops. Furthermore, it will provide university leaders and policymakers with actionable recommendations for creating responsible governance structures for AI that prioritise transparency, communication and employee participation. By evaluating individual level trust related to the ethical implementation of AI at universities, our research will assist universities in understanding how they can balance their drive towards innovation through technology and caring for people.

Artificial intelligence (AI) systems for decision-support are being introduced across multiple domains. One precondition for successful implementation is that those affected by the decisions trust the AI system. In the literature, many drivers of trust in AI have been proposed. In this study, we investigate the relative importance of six potential drivers of trust across three different decision-making domains, i.e., finance, legal, and healthcare in the Danish population. Based on an initial literature review and focus groups we designed a discrete choice study using conjoint analysis. Respondents were presented with a decision involving an AI system and asked to choose which of three possible AI systems they would trust the most, each system being characterized by a combination of levels of six attributes. All participants were presented with six choice situations. The decisions presented in the three domains were chosen to have approximately equal importance based on the focus group discussions. The three decisions were decision-making about a loan for a house purchase, decision-making about guilt in a case of assault, and diagnosis of skin cancer in primary care. Three samples of approximately 300 respondents each, representative of the Danish adult population were recruited through the panel provider Wilke. The main findings are that the most important drivers of trust differ between domains, with professional responsibility for the final decision being most important in finance and legal contexts, and precision being most important in the healthcare domain. Across all three domains, the absence of a particular trust-making feature has a strong negative effect, stronger than the presence of the same feature.

This systematic review examines how traditional Technology Acceptance Models (TAMs) can be adapted to better understand user acceptance of AI technologies. The study analyzed 80 peer-reviewed articles published between 2010 and 2025, identified through a comprehensive search of EBSCOhost, Embase, Inspec, Scopus, and Web of Science databases using keywords related to AI acceptance factors and technology acceptance models. Our analysis employed descriptive statistics, thematic analysis, and narrative synthesis to identify key factors influencing AI acceptance, including perceived usefulness, perceived ease of use, trust, transparency, explainability, and ethical considerations. We found that trust in AI systems' reliability, fairness, and privacy protection plays a crucial role in user acceptance, with low trust leading to resistance. Transparency and explainability of AI decision-making processes were identified as critical for building user trust. The review also highlights limitations of current research, including inconsistent definitions of AI technologies, inadequate exploration of cultural and contextual differences in AI acceptance, and the predominance of cross-sectional research designs. By addressing these areas, future research can provide a more comprehensive theoretical foundation for understanding user acceptance of AI technologies, guiding the development and ethical application of AI systems. Keywords: Artificial Intelligence (AI), Trust and Explainability in AI, Technology Acceptance Models (TAM).

BackgroundThe integration of artificial intelligence (AI) holds substantial potential to alter diagnostics and treatment in health care settings. However, public attitudes toward AI, including trust and risk perception, are key to its ethical and effective adoption. Despite growing interest, empirical research on the factors shaping public support for AI in health care (particularly in large-scale, representative contexts) remains limited.ObjectiveThis study aimed to investigate public attitudes toward AI in patient health care, focusing on how AI attributes (autonomy, costs, reliability, and transparency) shape perceptions of support, risk, and personalized care. In addition, it examines the moderating role of sociodemographic characteristics (gender, age, educational level, migration background, and subjective health status) in these evaluations. Our study offers novel insights into the relative importance of AI system characteristics for public attitudes and acceptance.MethodsWe conducted a factorial vignette experiment with a probability-based survey of 3030 participants from Germany’s general population. Respondents were presented with hypothetical scenarios involving AI applications in diagnosis and treatment in a hospital setting. Linear regression models assessed the relative influence of AI attributes on the dependent variables (support, risk perception, and personalized care), with additional subgroup analyses to explore heterogeneity by sociodemographic characteristics.ResultsMean values between 4.2 and 4.4 on a 1-7 scale indicate a generally neutral to slightly negative stance toward AI integration in terms of general support, risk perception, and personalized care expectations, with responses spanning the full scale from strong support to strong opposition. Among the 4 dimensions, reliability emerges as the most influential factor (percentage of explained variance [EV] of up to 10.5%). Respondents expect AI to not only prevent errors but also exceed current reliability standards while strongly disapproving of nontraceable systems (transparency is another important factor, percentage of EV of up to 4%). Costs and autonomy play a comparatively minor role (percentage of EVs of up to 1.5% and 1.3%), with preferences favoring collaborative AI systems over autonomous ones, and higher costs generally leading to rejection. Heterogeneity analysis reveals limited sociodemographic differences, with education and migration background influencing attitudes toward transparency and autonomy, and gender differences primarily affecting cost-related perceptions. Overall, attitudes do not substantially differ between AI applications in diagnosis versus treatment.ConclusionsOur study fills a critical research gap by identifying the key factors that shape public trust and acceptance of AI in health care, particularly reliability, transparency, and patient-centered approaches. Our findings provide evidence-based recommendations for policy makers, health care providers, and AI developers to enhance trust and accountability, key concerns often overlooked in system development and real-world applications. The study highlights the need for targeted policy and educational initiatives to support the responsible integration of AI in patient care.

Artificial intelligence (AI) is rapidly expanding in myriad industries and systems. This study sought to investigate public trust in using AI in the criminal court process. While previous research has identified factors that influence trust in AI, such as perceived accuracy and transparency of algorithms, less is known about the role of influential leaders—such as judges—in shaping public trust in new technology. This study examined the relationship between locus of control, anthropomorphism, cultural values, and perceived trust in AI. Participants completed a survey assessing their perceptions of trust in AI in determining bail eligibility, bail fines and fees, sentencing length, sentencing fines and fees, and writing legal documents (e.g., findings and disposition). Participants were more likely to trust AI performing financial calculations rather than determining bail eligibility, sentence length, or drafting legal documents. Participants’ comfort with AI in decision-making also depended on their perceptions of judges’ trust in AI, and they expressed concerns about AI perpetuating bias and the need for extensive testing to ensure accuracy. Interestingly, no significant association was found with other participant characteristics (e.g., locus of control, anthropomorphism, or cultural values). This study contributes to the literature by highlighting the role of judges as influential leaders in shaping public trust in AI and examining the influence of individual differences on trust in AI. The findings also help inform the development of recommended practices and ethical guidelines for the responsible use of AI in the courts.

As artificial intelligence (AI) becomes ubiquitous across various fields, understanding people’s acceptance and trust in AI systems becomes essential. This review aims to identify quantitative measures used to measure trust in AI and the associated studied elements. Following the PRISMA guidelines, three databases were consulted, selecting articles published before December 2023. Ultimately, 45 articles out of 1283 were selected. Articles were included if they were peer-reviewed journal publications in English reporting empirical studies measuring trust in AI systems with multi-item questionnaires. Studies were analyzed through the lenses of cognitive and affective trust. We investigated trust definitions, questionnaires employed, types of AI systems, and trust-related constructs. Results reveal diverse trust conceptualizations and measurements. In addition, the studies covered a wide range of AI system types, including virtual assistants, content detection tools, chatbots, medical AI, robots, and educational AI. Overall, the studies show compatibility of cognitive or affective trust focus between theorization, items, experimental stimuli, and level of anthropomorphism of the systems. The review underlines the need to adapt measurement of trust in the specific characteristics of human–AI interaction, accounting for both the cognitive and affective sides. Trust definitions and measurement could be chosen depending also on the level of anthropomorphism of the systems and the context of application.

Trust in artificial intelligence (AI) has emerged as a critical factor influencing students' interactions with AI-supported learning environments. However, validated instruments for measuring student trust in AI within educational contexts remain limited. This study developed and validated a multidimensional scale to assess student trust in AI systems in online distance education. Scale development followed a sequential multi-stage design, including item generation based on literature review, expert evaluation for content validity, and psychometric validation through exploratory and confirmatory factor analyses. A total of 837 distance learning students participated in the study (EFA = 412; CFA = 307; validation sample = 118). Exploratory factor analysis supported a five-factor structure explaining 63.20% of the total variance. Confirmatory factor analysis with an independent sample indicated good model fit ([Formula: see text]/df = 2.17; CFI = .95; TLI = .94; RMSEA = .06). The scale demonstrated high internal consistency (α = .94; ω = .94). Multi-group confirmatory factor analysis supported metric invariance across gender, indicating comparable measurement across groups. No significant gender differences were observed, and trust in AI was not significantly associated with grade point average or system engagement frequency. These results suggest that trust represents a distinct perception that may influence how students engage with AI-supported learning environments. The resulting 21-item scale provides a reliable instrument for investigating student trust in AI and offers practical implications for the design and implementation of AI-supported learning in higher education.

Integrating Artificial Intelligence Techniques in Medical Bacteriology is a scientific research paper that explores the transformative role of artificial intelligence (AI) in enhancing diagnostic and therapeutic practices within the field of bacteriology. As AI technologies increasingly permeate healthcare, this study provides a comprehensive analysis of how machine learning (ML) and deep learning algorithms can significantly improve the accuracy and timeliness of bacterial pathogen detection and antibiotic resistance management, marking a notable advancement in laboratory medicine.[1-][2] The research emphasizes the potential of AI to streamline workflows and enhance operational efficiency in medical bacteriology. By automating processes such as error detection, result interpretation, and image analysis, AI systems can significantly reduce the turnaround time for diagnostic results, ultimately leading to improved patient outcomes.[3][4] The paper also highlights the importance of data quality management in the development of AI models, advocating for adherence to established standards throughout the dataset lifecycle to ensure the reliability of AI applications in clinical settings.[5]Despite the promising advancements, the integration of AI in healthcare is not without its challenges. The study discusses the current limitations in the clinical efficacy and cost-effectiveness of AI applications, revealing a gap between research outcomes and real-world implementation.[4] Additionally, ethical considerations surrounding data privacy and algorithmic transparency are addressed, emphasizing the need for regulatory frameworks that promote safe and equitable AI use in medical practice.[6]Overall, this research provides a critical examination of the trends and innovations in AI applications in medical bacteriology, employing statistical analysis and bibliometric techniques to identify research hotspots and emerging patterns in the field from 2010 to 2024.[7][6] By integrating AI methodologies, the study aims to lay the groundwork for future research directions and improve quality assurance standards

Artificial intelligence (AI) is rapidly integrating into infrastructure planning, design, construction, management, and operation. This encompasses the use of AI-powered, intelligent systems to process vast amounts of data and support human decision-making concerning urban development, architecture, transportation systems, housing, energy efficiency, and sustainability of the built environment. AI integration into the built environment has also introduced new challenges with respect to transparency, accountability, fairness, and reliability in machine decision-making, and led to concerns about algorithmic bias, privacy, and ethical deployment of technology. A key barrier to formalizing trust in AI lies in the absence of consistent definitions of the main constructs of trust, and their interplay in trust formation and calibration. Trust formation involves the initial establishment of confidence in AI systems, while calibration refers to the ongoing alignment of trust with system performance and user experiences. Understanding how these processes interact within a decision context is crucial for developing robust frameworks that enhance trust in AI. In the built environment domain, in addition to technical capabilities of the AI systems, it is also essential to consider social, ethical, and legal dimensions, to ensure that the outcomes serve the best interests of humanity. This paper provides an overview of trust in AI systems, followed by describing findings from the literature through the lens of several built environment decision-making scenarios with the goal of creating a common understanding to support future research on human trust in intelligent systems, and how it may influence the quality and timeliness of resulting decision outcomes.

This all-inclusive research paper plunges into the transformative nature of artificial intelligence (AI) in healthcare, emphasizing its ability to change patient care and administrative activities. The investigation examines systematically how AI technologies are creating major improvements towards patients’ outcomes and enhanced operational efficiency within the healthcare systems. From diagnostics to personalized treatment plans and administrative tasks, AI is being integrated using sophisticated algorithms in numerous aspects of healthcare delivery. One major focus is improving diagnostic accuracy. AI-backed diagnostic tools such as image recognition software as well as predictive models have outperformed traditional methods in medical condition identification and diagnosis. For instance, artificial intelligence algorithms show exceptional skills in analysing medical images resulting to earlier detection as well as accurate diagnoses of diseases like cancer, cardiovascular conditions among others neurological disorders. These enhancements not only allow for timely intervention but they significantly reduce instances where diagnosis goes wrong hence enhancing patient prognosis while reducing health costs drastically. The ability to customize personalized therapy is yet another essential aspect of AI in healthcare. With such information as genetic data, medical records and lifestyle factors, and other patient information, AI systems can develop individualized treatment plans. It is for this reason that personalization of treatments enhances their efficiency while reducing the risk of any adverse effects as well as ensuring patients’ adherence to prescribed therapies. Besides, it is important to note that AI-driven predictive models are helpful in identifying individuals with a higher probability of developing specific conditions thereby allowing interventions aimed at preventing diseases and bettering overall population health. AI has also transformed administrative tasks within hospitals besides its clinical applications. For example, AI driven automation is now used to streamline scheduling of appointments or billing processes or even storage of patient records which are normally considered routine administrative works. In addition, through these means there is reduced burden on administrative duties for healthcare workers who continue to perform them with increased precision and timeliness. The net effect is that time and resources can be redirected towards more direct patient care thus improving service quality across the board’s entire system

Use of artificial intelligence in healthcare, such as machine learning-based predictive algorithms, holds promise for advancing outcomes, but few systems are used in routine clinical practice. Trust has been cited as an important challenge to meaningful use of artificial intelligence in clinical practice. Artificial intelligence systems often involve automating cognitively challenging tasks. Therefore, previous literature on trust in automation may hold important lessons for artificial intelligence applications in healthcare. In this perspective, we argue that informatics should take lessons from literature on trust in automation such that the goal should be to foster appropriate trust in artificial intelligence based on the purpose of the tool, its process for making recommendations, and its performance in the given context. We adapt a conceptual model to support this argument and present recommendations for future work.

Background Trust in artificial intelligence (AI) systems is critical for ethical and effective adoption, particularly among non-expert users. This review aimed to identify factors influencing trust and practical considerations for designing explainable AI (XAI) systems. Methods We systematically reviewed the literature following PRISMA 2020 guidelines and using backward and forward citation searches. The primary search was carried out in Web of Science (WoS), with the final search completed in January 2025. Eligible studies were peer-reviewed publications examining trust in AI among non-expert users and/or design considerations for XAI. Exclusion criteria removed studies focused solely on expert users or unrelated to trust or explainability. Results 35 studies involving diverse non-expert populations were included. We used a thematic synthesis approach to identify 33 trust-related factors grouped into personal characteristics, sociocultural influences, design and interaction attributes, and system-level properties (e.g., age, emotions, digital literacy, social norms, usability, aesthetics, explanation type, accuracy, transparency, regulatory compliance). Additionally, 120 design considerations were extracted across nine domains, including type and form of explanation; user experience and interaction; trust, transparency and human oversight; robustness, safety and reliability; data, traceability and the AI lifecycle; regulation and compliance; ethics, values and social responsibility; evaluation methods and metrics; and usage context and organizational factors. Conclusions The review synthesizes emerging perspectives on AI trust and explainability and identifies six domains that warrant further research to support the responsible design and deployment of XAI systems. Limitations include heterogeneous evaluation practices and inconsistent operationalization of trust among non-expert users, which complicates comparability across studies and hinder the responsible deployment of XAI systems.

Artificial intelligence (AI) is increasingly integrated into healthcare decision-making. Public trust in AI remains a critical determinant of its acceptance and effective use. Evidence on the factors shaping trust in AI within Middle Eastern contexts, particularly Saudi Arabia, remains limited. Therefore, we aimed to identify the determinants of trust in AI for health-related decision-making and to examine a theory-informed mediation pathway in which patient satisfaction mediates the association between patient-doctor relationships and trust in AI. We conducted a cross-sectional, facility-based survey of adults in Saudi Arabia, using an electronic questionnaire distributed in four primary healthcare centers. We performed multiple linear regression to assess the association of trust in AI for health-related decision-making with patient satisfaction, patient-doctor relationships, sociodemographic characteristics, and healthcare-related factors. A mediation analysis was also employed to evaluate the indirect and direct association linking patient-doctor relationships, patient satisfaction, and trust in AI. Our findings showed that patient satisfaction was positively associated with trust in AI (β = 0.54, 95% CI: 0.18-0.90), while patient-doctor relationships showed an inverse association (β = -0.34, 95% CI: -0.48 to -0.20), possibly reflecting a greater reliance on physicians' clinical judgment and a reduced perceived need for AI-supported decision-making. Trust in AI varied across age groups, with a lower trust observed in older age categories compared with younger adults. No strong associations were observed for sex, education, body mass index, or healthcare-related factors. Patient-doctor relationship quality was indirectly associated with trust in AI via patient satisfaction (ACME = 0.138, 95% CI: 0.043-0.246), alongside a direct association with trust in AI (ADE = -0.313, 95% CI: -0.456 to -0.160). This means that patient-doctor relationships influenced trust in AI both directly and indirectly through patient satisfaction, suggesting that, while interpersonal care may reduce the reliance on AI (direct effect), enhancing patient satisfaction can partially offset this effect and promote trust in AI (indirect effect). These findings highlight that fostering patient-centered care and satisfaction may be crucial for promoting public trust in AI, which has important implications for AI governance, ethical deployment, and the design of AI-supported healthcare systems.