Artificial Intelligence Decision Research Articles

Explainable Artificial Intelligence (XAI) in Healthcare: Enhancing Transparency and Trust

Artificial Intelligence has now taken a full-fledged role in healthcare and has started driving innovations not only in diagnostics and treatment planning but also in patient monitoring and operational efficiency. This will enable complex medical data analysis, extracting patterns and insights that no human is capable of. However, most of these models are per se opaque-that is, the so-called "black-box" problem-there are still great challenges in areas such as transparency, trust, and ethical applications in a clinical setting. This lack of interpretability can stand in the way of acceptance or integration for AI technologies when issues of understanding and accountability are relevant. Explainable AI solves these problems by making real artificial intelligence decisions understand the decisions made to humans. XAI techniques offer well-understandable and interpretable explanations of the models with minimum degradations in performance. This review article explains in detail the critical role of XAI in healthcare, underpinning how this field can bring more transparency into AI applications. We explain some of the current methods of XAI: model-agnostic techniques like LIME and SHAP, interpretable models relating to decision trees and linear models, and visualization techniques like saliency maps and mechanisms of attention.

Ethical and Regulatory Challenges of Generative Artificial Intelligence in Healthcare: A Chinese Perspective.

To provide practical insights that delve into the ethical issues and regulatory implications of generative artificial intelligence (GenAI) in healthcare. Ethical Challenges and Regulatory Impact in China is used as an example. Despite China's efforts to strike a delicate balance between protecting public welfare and promoting technological advancement, numerous unresolved issues persist in the practical integration of generative artificial intelligence into healthcare settings. Key issues such as data application, privacy protection, cost-effectiveness and regulatory remain areas of ambiguity that require clarification. Stringent ethical guidelines, data privacy protection measures and continuous supervision and evaluation of artificial intelligence decisions will help enhance the expected benefits of GenAI in healthcare. The potential use of GenAI in healthcare has garnered widespread attention, emerging as a significant global research topic. However, its application in this domain presents substantial ethical and regulatory challenges. Compared to other fields, GenAI's role in healthcare is more sensitive and complex, necessitating an urgent assessment of its ethical implications for future development and deployment. Challenges and ethical considerations are particularly pronounced in developing countries with limited healthcare resources.

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.

Artificial intelligence and real decisions: predictive systems and generative AI vs. emotive-cognitive legal deliberations

IntroductionActivation of NLRP3-containing inflammasome, which is responsible for IL-1β maturation, has been shown to contribute to Alzheimer’s disease (AD)-associated pathogenesis in both APP- and tau-transgenic mice. However, effects of NLRP3 on pericytes and subsequent cerebrovascular pathology in AD remain unknown.MethodsNLRP3-deficient and wild-type AD animal models were generated by crossing human P301S tau-transgenic mice and Nlrp3 knockout mice. AD-associated neuroinflammation, tauopathy, vasculature and pericyte coverage in the brain were investigated using immunohistological and molecular biological methods. To investigate how NLRP3 regulates pericyte activation and survival, pericytes from the brains of Nlrp3 knockout and wild-type mice were cultured, treated with IL-1β and H2O2 at different concentrations and analyzed by confocal microscopy and flow cytometry after staining with fluorescently labelled phalloidin, annexin-V and PDGFRβ antibody.ResultsDeficiency of NLRP3 (1) reduced Iba-1, GFAP and AT8 antibody-immunoreactive phosphorylated tau-positive cells, without significantly altering transcription of inflammatory genes, (2) preserved cerebral vasculature and pericyte coverage and up-regulated Osteopontin gene transcription, and (3) improved cognitive function in tau-transgenic mice. In cell culture, NLRP3 deficiency prevented pericyte apoptosis. Treatment with IL-1β or H2O2 increased the expression of PDGFRβ in NLRP3-deficient pericytes, but decreased it in NLRP3 wild-type pericytes in a dose-dependent manner.DiscussionInhibition of NLRP3 can promote pericyte survival, improve cerebrovascular function, and attenuate AD pathology in the brain of tau-transgenic mice. Our study supports NLRP3 as a novel therapeutic target for Alzheimer’s patients.

Revolutionizing Tumor Diagnosis with AI: an Indepth Analysis of Image Datasets

the research explores how AI specifically deep learning Representations like CNNs Improves tumor diagnosis by improving image analysis Precision and speed. it too addresses challenges inch artificial intelligence decision-making and its consolidation into Goal workflows

The effects of artificial intelligence and victims’ deservingness information on citizens’ blame attribution towards administrative errors

ABSTRACT This study examines how citizens attribute blame to government authorities for administrative errors made by artificial intelligence (AI) compared to human decision-makers. Based on blame attribution theory, we conducted a vignette-based survey experiment with 1,098 Chinese citizens, revealing that respondents assign less blame for errors caused by AI or AI-assisted decisions. Additionally, disclosing victims’ deservingness information heightened blame attribution. These findings contribute to the literature on administrative accountability, highlighting how citizens respond to AI-related errors and informing the growing use of AI in public sector decision-making.

Ethical Implications of AI in Financial Services: Bias, Transparency, and Accountability

This article examines the ethical implications of artificial intelligence (AI) in financial services, focusing on issues of bias, transparency, and accountability. As AI adoption in finance grows rapidly, with 85% of institutions now using AI, it brings both tremendous benefits and significant ethical challenges. The article explores how AI can perpetuate biases in lending and credit scoring, the "black box" problem of opaque AI decision-making, and the complexities of establishing accountability for autonomous AI systems. It analyzes strategies for mitigating these issues, including developing interpretable AI models, implementing robust governance structures, and creating industry-specific ethical guidelines. The discussion highlights the critical need for evolving regulatory frameworks and ethical standards to ensure responsible AI use while harnessing its potential to transform financial services.

A radiographic artificial intelligence tool to identify candidates suitable for partial knee arthroplasty.

Knee osteoarthritis is a prevalent condition frequently necessitating knee replacement surgery, with demand projected to rise substantially. Partial knee arthroplasty (PKA) offers advantages over total knee arthroplasty (TKA), yet its utilisation remains low despite guidance recommending consideration alongside TKA in shared decision making. Radiographic decision aids exist but are underutilised due to clinician time constraints. This research develops a novel radiographic artificial intelligence (AI) tool using a dataset of knee radiographs and a panel of expert orthopaedic surgeons' assessments. Six AI models were trained to identify PKA candidacy. 1241 labelled four-view radiograph series were included. Models achieved statistically significant accuracies above random assignment, with EfficientNet-ES demonstrating the highest performance (AUC 95%, F1 score 83% and accuracy 80%). The AI decision tool shows promise in identifying PKA candidates, potentially addressing underutilisation of this procedure. Its integration into clinical practice could enhance shared decision making and improve patient outcomes. Further validation and implementation studies are warranted to assess real-world utility and impact.

AI and Trust in Organizations

As the use of artificial intelligence (AI) expands in both society and organizations, building and maintaining trust in AI systems is becoming increasingly important. While AI promises benefits like improved efficiency and data-driven decision making, concerns are growing around a lack of transparency, accountability, fairness and control over how data is collected and used. This erosion of trust poses a threat to the widespread adoption of AI. However, the article argues that organizations have an opportunity to proactively build trust through transparent, accountable and fair development and application of AI. Drawing from research findings, it outlines strategies for providing transparency into AI systems and decision making, ensuring accountability in development and deployment, and engaging stakeholders. If implemented, these practices can help alleviate fears, demonstrate a commitment to responsible and ethical use of AI, and gain greater public acceptance and adoption of the technology's potential benefits.

Artificial intelligence and smart decision making in smart cities: a parallel moderated mediation approach

ABSTRACT The growing use of artificial intelligence (AI) for decision-making in smart cities has ignited serious debates on the positive aspects and potential harms of AI in smart cities. However, research on this subject is yet to be comprehensive. While previous studies have examined the direct link between AI and smart decision-making (SDM), extant results remain unclear. Thus, using social cognitive theory and integrated fear acquisition theory, the current research examines the direct impact of AI on SDM by introducing work-life interface and lack of transparency anxiety as mediators. The study also examines the moderating role of support orientation. We rely on cross-sectional data collected from citizens of Turkey to examine the relationships. The study’s research framework was validated using 433 valid samples collected from Turkish citizens through a simple random sampling method. The findings indicate that AI has a positive impact on SDM. AI has a positive impact on work-life interface. Work life interface has a positive impact on SDM. AI has a positive impact on lack of transparency anxiety. Lack of transparency anxiety has a negative impact on SDM. Both work-life interface and lack of transparency anxiety were discovered to be effective mediators between the use of AI and SDM. The positive impact of AI on work-life interface is further enhanced at high level of support orientation. The positive impact of AI on SDM is further enhanced at high level of support orientation. The findings offer useful guides for making well-informed decisions and executing policies that are beneficial for all in smart cities.

Safeguarding Patients in the AI Era: Ethics at the Forefront of Pharmacovigilance.

Artificial intelligence is increasingly being used in pharmacovigilance. However, the use of artificial intelligence in pharmacovigilance raises ethical concerns related to fairness, non-discrimination, compliance, and responsibility as the central ethical principles in risk assessment and regulatory requirements. This paper explores these concerns and provides a roadmap to how to address these challenges by considering data collection, privacy protection, transparency and accountability, model training, and explainability in artificial intelligence decision making for drug safety surveillance. A number of responsible approaches have been identified including an ethics framework and best practices to enhance artificial intelligence use in healthcare. The document also recognizes some initiatives that have demonstrated the importance of ethics in artificial intelligence pharmacovigilance. Nevertheless, the major needs mentioned in this paper are transparency, accountability, data protection, and fairness, which stress the necessity of collaboration to construct a cognitive framework aimed at integrating ethical artificial intelligence into pharmacovigilance. In conclusion, innovation should be balanced with ethical responsibility to enhance public health outcomes as well as patient safety.

Advanced interpretable diagnosis of Alzheimer's disease using SECNN-RF framework with explainable AI.

Early detection of Alzheimer's disease (AD) is vital for effective treatment, as interventions are most successful in the disease's early stages. Combining Magnetic Resonance Imaging (MRI) with artificial intelligence (AI) offers significant potential for enhancing AD diagnosis. However, traditional AI models often lack transparency in their decision-making processes. Explainable Artificial Intelligence (XAI) is an evolving field that aims to make AI decisions understandable to humans, providing transparency and insight into AI systems. This research introduces the Squeeze-and-Excitation Convolutional Neural Network with Random Forest (SECNN-RF) framework for early AD detection using MRI scans. The SECNN-RF integrates Squeeze-and-Excitation (SE) blocks into a Convolutional Neural Network (CNN) to focus on crucial features and uses Dropout layers to prevent overfitting. It then employs a Random Forest classifier to accurately categorize the extracted features. The SECNN-RF demonstrates high accuracy (99.89%) and offers an explainable analysis, enhancing the model's interpretability. Further exploration of the SECNN framework involved substituting the Random Forest classifier with other machine learning algorithms like Decision Tree, XGBoost, Support Vector Machine, and Gradient Boosting. While all these classifiers improved model performance, Random Forest achieved the highest accuracy, followed closely by XGBoost, Gradient Boosting, Support Vector Machine, and Decision Tree which achieved lower accuracy.

Artificial Intelligence and Ethics in Healthcare: A Bibliometric Analysis

The rapid proliferation of artificial intelligence (AI) in healthcare services has underscored the importance of ethical considerations. This development highlights the need to examine ethical implications, debates, concerns, and thoughts from diverse and broad perspectives. In this context, the study focuses on the ethical dimensions of AI in the healthcare domain. AI is increasingly being used in various healthcare applications, but this usage brings along ethical challenges. The aim of the research is to identify themes, trends, and critical points related to AI ethics in healthcare. Through literature review and bibliometric analyses, it is observed that AI ethics research in healthcare revolves around fundamental concepts such as ethics, AI, machine learning, healthcare services, and privacy. Additionally, the leading countries, authors, and institutions in the field are examined. The intensity of collaboration and knowledge sharing in the literature is steadily increasing. In conclusion, considering the potential benefits and challenges of AI use in healthcare, addressing ethical issues, ensuring data security, and enhancing transparency in AI decision processes are crucial. The study aims to provide a deeper understanding of AI ethics topics in the existing literature and guide future research.

Artificial Intelligence and Military Decision Making: Revisiting OODA Loop Framework

Artificial Intelligence and Military Decision Making: Revisiting OODA Loop Framework

Utility of ChatGPT in neuroanesthesia: Still long way to go

With the evolution of Artificial Intelligence (AI), complex and intricate medical decisions are expected to benefit from the insights of such AI-powered large language models like ChatGPT. This study was conducted to examine ChatGPT 3.5's performance in neuroanesthesia through ten clinical cases. While the model shows moderate competency in some respects, it could not generate correct responses for over half of the scenarios underscoring the need for specific training and refinement before practical use in specialized clinical decision support.

Improving realty management ability based on big data and artificial intelligence decision-making.

Realty management relies on data from previous successful and failed purchase and utilization outcomes. The cumulative data at different stages are used to improve utilization efficacy. The vital problem is selecting data for analyzing the value incremental sequence and profitable utilization. This article proposes a knowledge-dependent data processing scheme (KDPS) to augment precise data analysis. This scheme operates on two levels. Data selection based on previous stagnant outcomes is performed in the first level. Different data processing is performed in the second level to mend the first level's flaws. Data processing uses knowledge acquired from the sales process, amenities, and market value. Based on the knowledge determined from successful realty sales and incremental features, further processing for new improvements and existing stagnancy mitigation is recommended. The stagnancy and realty values are used as knowledge for training the data processing system. This ensures definite profitable features meeting the amenity requirements under reduced stagnancy time. The proposed scheme improves the processing rate, stagnancy detection, success rate, and training ratio by 8.2%, 10.25%, 10.28%, and 7%, respectively. It reduces the processing time by 8.56% compared to the existing methods.

Artificial intelligence decision making tools in food metabolomics: Data fusion unravels synergies within the hazelnut (Corylus avellana L.) metabolome and improves quality prediction

This study investigates the metabolome of high-quality hazelnuts (Corylus avellana L.) by applying untargeted and targeted metabolome profiling techniques to predict industrial quality. Utilizing comprehensive two-dimensional gas chromatography and liquid chromatography coupled with high-resolution mass spectrometry, the research characterizes the non-volatile (primary and specialized metabolites) and volatile metabolomes. Data fusion techniques, including low-level (LLDF) and mid-level (MLDF), are applied to enhance classification performance. Principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA) reveal that geographical origin and postharvest practices significantly impact the specialized metabolome, while storage conditions and duration influence the volatilome. The study demonstrates that MLDF approaches, particularly supervised MLDF, outperform single-fraction analyses in predictive accuracy. Key findings include the identification of metabolites patterns causally correlated to hazelnut’s quality attributes, of them aldehydes, alcohols, terpenes, and phenolic compounds as most informative. The integration of multiple analytical platforms and data fusion methods shows promise in refining quality assessments and optimizing storage and processing conditions for the food industry.

Application of artificial intelligence in technology management: status quo, challenges and opportunities

ABSTRACT Technology management (TM) aims to enhance technical competitiveness, providing organizations with a market advantage. The increasing interest in artificial intelligence (AI) within TM has sparked debate: supporters praise AI's decision-making capabilities, while critics stress the importance of human expertise. This study advocates for integrating AI into TM as a supportive tool for human decision-making rather than replacing domain experts. To achieve this, a comprehensive literature review examines the integration of AI in TM, exploring potential benefits and challenges. The research analyzes text mining works, existing data, and the suitability of AI models for various TM tasks. Additionally, it reviews AI applications in TM, identifying opportunities and challenges.

Reporting radiographers' interaction with Artificial Intelligence-How do different forms of AI feedback impact trust and decision switching?

Artificial Intelligence (AI) has been increasingly integrated into healthcare settings, including the radiology department to aid radiographic image interpretation, including reporting by radiographers. Trust has been cited as a barrier to effective clinical implementation of AI. Appropriating trust will be important in the future with AI to ensure the ethical use of these systems for the benefit of the patient, clinician and health services. Means of explainable AI, such as heatmaps have been proposed to increase AI transparency and trust by elucidating which parts of image the AI 'focussed on' when making its decision. The aim of this novel study was to quantify the impact of different forms of AI feedback on the expert clinicians' trust. Whilst this study was conducted in the UK, it has potential international application and impact for AI interface design, either globally or in countries with similar cultural and/or economic status to the UK. A convolutional neural network was built for this study; trained, validated and tested on a publicly available dataset of MUsculoskeletal RAdiographs (MURA), with binary diagnoses and Gradient Class Activation Maps (GradCAM) as outputs. Reporting radiographers (n = 12) were recruited to this study from all four regions of the UK. Qualtrics was used to present each participant with a total of 18 complete examinations from the MURA test dataset (each examination contained more than one radiographic image). Participants were presented with the images first, images with heatmaps next and finally an AI binary diagnosis in a sequential order. Perception of trust in the AI systems was obtained following the presentation of each heatmap and binary feedback. The participants were asked to indicate whether they would change their mind (or decision switch) in response to the AI feedback. Participants disagreed with the AI heatmaps for the abnormal examinations 45.8% of the time and agreed with binary feedback on 86.7% of examinations (26/30 presentations).'Only two participants indicated that they would decision switch in response to all AI feedback (GradCAM and binary) (0.7%, n = 2) across all datasets. 22.2% (n = 32) of participants agreed with the localisation of pathology on the heatmap. The level of agreement with the GradCAM and binary diagnosis was found to be correlated with trust (GradCAM:-.515;-.584, significant large negative correlation at 0.01 level (p = < .01 and-.309;-.369, significant medium negative correlation at .01 level (p = < .01) for GradCAM and binary diagnosis respectively). This study shows that the extent of agreement with both AI binary diagnosis and heatmap is correlated with trust in AI for the participants in this study, where greater agreement with the form of AI feedback is associated with greater trust in AI, in particular in the heatmap form of AI feedback. Forms of explainable AI should be developed with cognisance of the need for precision and accuracy in localisation to promote appropriate trust in clinical end users.

Artificial Intelligence and Corporate Investment Decisions

Artificial Intelligence and Corporate Investment Decisions