Artificial Intelligence Decision Research Articles

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

Enterprise value creation effects of artificial intelligence technology from the perspective of digital agility: evidence from China

ABSTRACT As a pivotal technology driving the latest wave of technological revolution and industry transformation, artificial intelligence (AI) is permeating the processes of business operations widely. Grounded in the dynamic capability theory, the research use Word2Vec and Python to extract metrics for AI technology from annual reports of Chinese A-share listed companies from 2012 to 2022. This research empirically investigates the impact of AI technology on the level of enterprise value creation. The findings reveal that the application of AI technology by listed companies significantly enhances their value creation, a conclusion that remains robust following various endogeneity and robustness tests. AI technology empowers enterprise value creation through the mechanisms of sensing, integrating, and restructuring digital agility. Furthermore, the value-creation effect of AI technology is more significant in manufacturing and the eastern region. The results elucidate the logic behind the influence of AI technology on enterprise value creation and provide practical support for organisations in implementing AI strategic planning and decision-making.

Expert gaze as a usability indicator of medical AI decision support systems: a preliminary study

Given the current state of medical artificial intelligence (AI) and perceptions towards it, collaborative systems are becoming the preferred choice for clinical workflows. This work aims to address expert interaction with medical AI support systems to gain insight towards how these systems can be better designed with the user in mind. As eye tracking metrics have been shown to be robust indicators of usability, we employ them for evaluating the usability and user interaction with medical AI support systems. We use expert gaze to assess experts’ interaction with an AI software for caries detection in bitewing x-ray images. We compared standard viewing of bitewing images without AI support versus viewing where AI support could be freely toggled on and off. We found that experts turned the AI on for roughly 25% of the total inspection task, and generally turned it on halfway through the course of the inspection. Gaze behavior showed that when supported by AI, more attention was dedicated to user interface elements related to the AI support, with more frequent transitions from the image itself to these elements. When considering that expert visual strategy is already optimized for fast and effective image inspection, such interruptions in attention can lead to increased time needed for the overall assessment. Gaze analysis provided valuable insights into an AI’s usability for medical image inspection. Further analyses of these tools and how to delineate metrical measures of usability should be developed.

Valuing good health care: How medical doctors, scientists and patients relate ethical challenges with artificial intelligence decision-making support tools in prostate cancer diagnostics to good health care.

Artificial intelligence (AI) is increasingly used in health care to improve diagnostics and treatment. Decision-making tools intended to help professionals in diagnostic processes are developed in a variety of medical fields. Despite the imagined benefits, AI in health care is contested. Scholars point to ethical and social issues related to the development, implementation, and use of AI in diagnostics. Here, we investigate how three relevant groups construct ethical challenges with AI decision-making tools in prostate cancer (PCa) diagnostics: scientists developing AI decision support tools for interpreting MRI scans for PCa, medical doctors working with PCa and PCa patients. This qualitative study is based on participant observation and interviews with the abovementioned actors. The analysis focuses on how each group draws on their understanding of 'good health care' when discussing ethical challenges, and how they mobilise different registers of valuing in this process. Our theoretical approach is inspired by scholarship on evaluation and justification. We demonstrate how ethical challenges in this area are conceptualised, weighted and negotiated among these participants as processes of valuing good health care and compare their perspectives.

The Use of AI in Medicine: Health Data, Privacy Risks and More

In the era of advancements in artificial intelligence (AI) and machine learning, the healthcare industry has become one of the major areas where such technologies are being actively adopted and utilized. The global health care sector generated more than 2.3 zettabytes of data worldwide in 2020. Analysts estimate that the global market for artificial intelligence (AI) in medicine will grow to $13 billion by 2025, with a significant increase in newly established companies. Artificial intelligence in medicine is used to predict, detect and diagnose various diseases and pathologies. The sources of data can be various results of medical research (EEG, X-ray images, laboratory tests, e.g. tissues, etc.). At the same time, there are understandable concerns that AI will undermine the patient-provider relationship, contribute to the deskilling of providers, undermine transparency, misdiagnose or inappropriately treat because of errors within AI decision-making that are hard to detect, exacerbate existing racial or societal biases, or introduce algorithmic bias that will be hard to detect. Traditional research methods, general and special ones, with an emphasis on the comparative legal method, were chosen. For the AI to work it needs to be trained, and it’s learning from all sorts of information given to it. The main part of the information on which AI is trained is health data, which is sensitive personal data. The fact that personal data is qualified as sensitive personal data indicates the significance of the information contained, the high risks in case it’s leaking, and hence the need for stricter control and regulation. The article offers a detailed exploration of the legal implications of AI in medicine, highlighting existing challenges, the current state of regulation, and proposes future perspectives and recommendations for legislation adapted to the era of medical AI. Given the above, the study is divided into three parts: international framework, that will focus primarily on applicable WHO documents; risks and possible ways to minimize them, where the authors have tried to consider various issues related to the use of AI in medicine and find options to address them; and relevant case-study.

"Problem Decision Making in Healthcare: Human Decision or Artificial Intelligence Decision?"

"Problem Decision Making in Healthcare: Human Decision or Artificial Intelligence Decision?"

Exploring decision-makers’ challenges and strategies when selecting multiple systematic reviews: insights for AI decision support tools in healthcare

BackgroundSystematic reviews (SRs) are being published at an accelerated rate. Decision-makers may struggle with comparing and choosing between multiple SRs on the same topic. We aimed to understand how healthcare...

Paradigm of symbiosis as a mechanism of artificial intelligence and customers’ patronage decision in the hospitality sector: the moderating role of customer engagement

ABSTRACT Study aims to explore the effect of artificial intelligence (AI) performance on customer values and patronage decisions; determine the relationship between AI service enhancement and various levels of perceived value; and adopt customer engagement as a moderator that affects customer value and patronage decision. Study adopts the Means-End Chain theory, synthesised by the Multi-Attribute Value theory to integrate AI performance and perceived values. Adopted quantitative approach and structured equation modelling and study are based on a total of 418 valid samples. Shedding light on how AI resources can be effectively managed within the service paradigm during customer interactions and AI services, this study is expected to offer theoretical and practical insights. Results contribute to the theoretical framework surrounding the relationship between AI and customer patronage decision making, thereby enriching the AI academic field. The study advocates for a symbiotic understanding of AI and customer engagement for improved service performance.

Strengthening the Role of Artificial Intelligence Technology in the Role of Court Judges in Indonesia

This research explores the strengthening role of artificial intelligence (AI) technology in supporting the duties and responsibilities of court judges in Indonesia. In the digital era, the integration of AI in the judicial system is necessary to improve efficiency, accuracy, and fairness in legal decision-making. This research uses a qualitative method with literature studies, in-depth interviews, and case analysis from other countries that have implemented AI in their justice systems. The results show that AI can reduce judges' workload, speed up case resolution, and minimize human error and bias. However, there are challenges such as ethical issues, data privacy, and public trust in AI decisions. This research emphasizes the need for a clear regulatory framework and specialized training for judges to ensure the effective and responsible use of AI. In addition, the importance of collaboration between governments, legal institutions, and technology developers to create AI solutions that comply with the principles of fairness and transparency was expressed. In conclusion, although AI has great potential to strengthen the role of judges in Indonesia, its implementation must be done carefully to maintain the principles of fairness.

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D–3D deep learning framework for colorectal cancer detection

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,000 deaths. The most common screening technique is minimally invasive colonoscopy using either reflected white light endoscopy or narrow-band imaging. However, current imaging modalities have only moderate sensitivity and specificity for lesion detection. We have developed a novel fluorescence excitation-scanning hyperspectral imaging (HSI) approach to sample image and spectroscopic data simultaneously on microscope and endoscope platforms for enhanced diagnostic potential. Unfortunately, fluorescence excitation-scanning HSI datasets pose major challenges for data processing, interpretability, and classification due to their high dimensionality. Here, we present an end-to-end scalable Artificial Intelligence (AI) framework built for classification of excitation-scanning HSI microscopy data that provides accurate image classification and interpretability of the AI decision-making process. The developed AI framework is able to perform real-time HSI classification with different speed/classification performance trade-offs by tailoring the dimensionality of the dataset, supporting different dimensions of deep learning models, and varying the architecture of deep learning models. We have also incorporated tools to visualize the exact location of the lesion detected by the AI decision-making process and to provide heatmap-based pixel-by-pixel interpretability. In addition, our deep learning framework provides wavelength-dependent impact as a heatmap, which allows visualization of the contributions of HSI wavelength bands during the AI decision-making process. This framework is well-suited for HSI microscope and endoscope platforms, where real-time analysis and visualization of classification results are required by clinicians.

Constructing and implementing a performance evaluation indicator set for artificial intelligence decision support systems in pediatric outpatient clinics: an observational study

Artificial intelligence (AI) decision support systems in pediatric healthcare have a complex application background. As an AI decision support system (AI-DSS) can be costly, once applied, it is crucial to focus on its performance, interpret its success, and then monitor and update it to ensure ongoing success consistently. Therefore, a set of evaluation indicators was explicitly developed for AI-DSS in pediatric healthcare, enabling continuous and systematic performance monitoring. The study unfolded in two stages. The first stage encompassed establishing the evaluation indicator set through a literature review, a focus group interview, and expert consultation using the Delphi method. In the second stage, weight analysis was conducted. Subjective weights were calculated based on expert opinions through analytic hierarchy process, while objective weights were determined using the entropy weight method. Subsequently, subject and object weights were synthesized to form the combined weight. In the two rounds of expert consultation, the authority coefficients were 0.834 and 0.846, Kendall's coordination coefficient was 0.135 in Round 1 and 0.312 in Round 2. The final evaluation indicator set has three first-class indicators, fifteen second-class indicators, and forty-seven third-class indicators. Indicator I-1(Organizational performance) carries the highest weight, followed by Indicator I-2(Societal performance) and Indicator I-3(User experience performance) in the objective and combined weights. Conversely, 'Societal performance' holds the most weight among the subjective weights, followed by 'Organizational performance' and 'User experience performance'. In this study, a comprehensive and specialized set of evaluation indicators for the AI-DSS in the pediatric outpatient clinic was established, and then implemented. Continuous evaluation still requires long-term data collection to optimize the weight proportions of the established indicators.

Unlocking Trust in AI Decision-Making: The Crucial Role of Confidence, Transparency, and User Perception

Artificial Intelligence (AI) has become an integral part of decision-making processes across a spectrum of applications, from autonomous vehicles and healthcare diagnostics to financial forecasting and customer service. As AI systems increasingly take on roles that directly impact human lives and societal structures, the issue of trust in their decision-making capabilities assumes paramount importance. Confidence, as a measurable attribute within AI systems, plays a pivotal role in shaping this trust dynamic. This abstract is drawn from the conference "AI Decision-Making: The Role of Confidence," which explores the multifaceted dimensions of confidence in AI decision-making and its profound implications for accuracy and trust calibration. Researchers, practitioners, and industry experts converge to discuss the challenges and opportunities surrounding this critical concept.

Responsible AI for cardiovascular disease detection: Towards a privacy-preserving and interpretable model

Background and objectiveCardiovascular disease (CD) is a major global health concern, affecting millions with symptoms like fatigue and chest discomfort. Timely identification is crucial due to its significant contribution to global mortality. In healthcare, artificial intelligence (AI) holds promise for advancing disease risk assessment and treatment outcome prediction. However, machine learning (ML) evolution raises concerns about data privacy and biases, especially in sensitive healthcare applications. The objective is to develop and implement a responsible AI model for CD prediction that prioritize patient privacy, security, ensuring transparency, explainability, fairness, and ethical adherence in healthcare applications. MethodsTo predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions. ResultsOur investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development. ConclusionsOur study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.

The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges

The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the impact of AI on healthcare possibly in all dimensions including precision medicine, diagnostics, drug development, automation of the process, etc., explicating whether AI is a blessing or a curse or both. With the availability of enough data and analysis to examine the topic at hand, however, its application is still functioning in quite early stages in many fields, the present work will endeavour to provide an answer to the question. This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.

VisionCam: A Comprehensive XAI Toolkit for Interpreting Image-Based Deep Learning Models

Artificial intelligence (AI), a rapidly developing technology, has revolutionized various aspects of our lives. However many AI models' complex inner workings are still unknown, frequently compared to a "black box." Particularly in crucial fields, this lack of explainability (XAI) reduces responsible AI research and reduces public confidence, and is accompanied by a growing demand for transparency and interpretability in AI decision-making. In response, this paper introduces a Python Extensible Toolkit for Explainable AI (XAI), This toolkit comprises nine state-of-the-art techniques for explaining AI models (especially deep learning models) decisions in image processing: GradCAM, GradCAM++, GradCAMElementWise, HriesCAM, RespondCAM, ScoreCAM, SmoothGradCAM++, XgradCAM, and AblationCAM. Each tool offers unique insights into model decision-making processes of deep learning models that work with image data, addressing various aspects of interpretability. Through case studies, we demonstrate the toolkit's impact on improving transparency and interpretability in AI systems that analyze visual information. The source code for the VisionCam toolkit is accessible at https://github.com/VisionCAM.

Artificial intelligence-driven decision making and firm performance: a quantitative approach

PurposeThe purpose of this study is to investigate the relationship between artificial intelligence (AI) and decision making in the development of AI-related capabilities. We investigate if and how AI-driven decision making has an impact on firm performance. We also investigate the role played by environmental dynamism in the development of AI capabilities and AI-driven decision making.Design/methodology/approachWe surveyed 346 managers in the United States using established scales from the literature and leveraged p modelling to analyse the data.FindingsResults indicate that AI-driven decision making is positively related to firm performance and that big data-powered AI positively influences AI-driven decision making. Moreover, there is a positive relationship between big data-powered AI and the development of AI capability within a firm. It is also found that the control variables of firm size and age do not significantly affect firm performance. Finally, environmental dynamism does not have a positive and significant moderating effect on the path connecting big data-powered AI and AI-driven decision making, while it exerts a positive moderating effect on the development of AI capability to strengthen AI-driven decision making.Originality/valueThese findings extend the resource-based view by highlighting the capabilities developed within the firm to manage big data-powered AI. This research also provides theoretically grounded guidance to managers wanting to align their AI-driven decision making with superior firm performance.

Automatized self-supervised learning for skin lesion screening

Melanoma, the deadliest form of skin cancer, has seen a steady increase in incidence rates worldwide, posing a significant challenge to dermatologists. Early detection is crucial for improving patient survival rates. However, performing total body screening (TBS), i.e., identifying suspicious lesions or ugly ducklings (UDs) by visual inspection, can be challenging and often requires sound expertise in pigmented lesions. To assist users of varying expertise levels, an artificial intelligence (AI) decision support tool was developed. Our solution identifies and characterizes UDs from real-world wide-field patient images. It employs a state-of-the-art object detection algorithm to locate and isolate all skin lesions present in a patient’s total body images. These lesions are then sorted based on their level of suspiciousness using a self-supervised AI approach, tailored to the specific context of the patient under examination. A clinical validation study was conducted to evaluate the tool’s performance. The results demonstrated an average sensitivity of 95% for the top-10 AI-identified UDs on skin lesions selected by the majority of experts in pigmented skin lesions. The study also found that the tool increased dermatologists’ confidence when formulating a diagnosis, and the average majority agreement with the top-10 AI-identified UDs reached 100% when assisted by our tool. With the development of this AI-based decision support tool, we aim to address the shortage of specialists, enable faster consultation times for patients, and demonstrate the impact and usability of AI-assisted screening. Future developments will include expanding the dataset to include histologically confirmed melanoma and validating the tool for additional body regions.

Comparing performance of primary care clinicians in the interpretation of SPIROmetry with or without Artificial Intelligence Decision support software (SPIRO-AID): a protocol for a randomised controlled trial

IntroductionSpirometry is a point-of-care lung function test that helps support the diagnosis and monitoring of chronic lung disease. The quality and interpretation accuracy of spirometry is variable in primary care....

Explainable artificial intelligence for medical imaging: Review and experiments with infrared breast images

AbstractThere is a growing trend of using artificial intelligence, particularly deep learning algorithms, in medical diagnostics, revolutionizing healthcare by improving efficiency, accuracy, and patient outcomes. However, the use of artificial intelligence in medical diagnostics comes with the critical need to explain the reasoning behind artificial intelligence‐based predictions and ensure transparency in decision‐making. Explainable artificial intelligence has emerged as a crucial research area to address the need for transparency and interpretability in medical diagnostics. Explainable artificial intelligence techniques aim to provide insights into the decision‐making process of artificial intelligence systems, enabling clinicians to understand the factors the algorithms consider in reaching their predictions. This paper presents a detailed review of saliency‐based (visual) methods, such as class activation methods, which have gained popularity in medical imaging as they provide visual explanations by highlighting the regions of an image most influential in the artificial intelligence's decision. We also present the literature on non‐visual methods, but the focus will be on visual methods. We also use the existing literature to experiment with infrared breast images for detecting breast cancer. Towards the end of this paper, we also propose an “attention guided Grad‐CAM” that enhances the visualizations for explainable artificial intelligence. The existing literature shows that explainable artificial intelligence techniques are not explored in the context of infrared medical images and opens up a wide range of opportunities for further research to make clinical thermography into assistive technology for the medical community.