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The purpose of this paper is to investigate the relationship between hospital adoption and use of computed tomography (CT) scanners, and magnetic resonance imaging (MRI) machines and in-patient mortality and length of stay.

This study used panel data (2007–2010) from 124 hospital corporations operating in Ontario, Canada. Imaging use focused on medical patients accounting for 25 percent of hospital discharges. Main outcomes were in-hospital mortality rates and average length of stay. A model for each outcome-technology combination was built, and controlled for hospital structural characteristics, market factors and patient characteristics.

In 2010, 36 and 59 percent of hospitals had adopted MRI machines and CT scanners, respectively. Approximately 23.5 percent of patients received CT scans and 3.5 percent received MRI scans during the study period. Adoption of these technologies was associated with reductions of up to 1.1 percent in mortality rates and up to 4.5 percent in length of stay. The imaging use–mortality relationship was non-linear and varied by technology penetration within hospitals. For CT, imaging use reduced mortality until use reached 19 percent in hospitals with one scanner and 28 percent in hospitals with 2+ scanners. For MRI, imaging use was largely associated with decreased mortality. The use of CT scanners also increased length of stay linearly regardless of technology penetration (4.6 percent for every 10 percent increase in use). Adoption and use of MRI was not associated with length of stay.

These results suggest that there may be some unnecessary use of imaging, particularly in small hospitals where imaging is contracted out. In larger hospitals, the results highlight the need to further investigate the use of imaging beyond certain thresholds. Independent of the rate of imaging use, the results also indicate that the presence of CT and MRI devices within a hospital benefits quality and efficiency.

To the authors’ knowledge, this study is the first to investigate the combined effect of adoption and use of medical imaging on outcomes specific to CT scanners and MRI machines in the context of hospital in-patient care.

This paper aims to apply the Six Sigma methodology to improve workflow by eliminating the causes of failure in the medical imaging department of a private Turkish hospital.

Implementation of the design, measure, analyse, improve and control (DMAIC) improvement cycle, workflow chart, fishbone diagrams and Pareto charts were employed, together with rigorous data collection in the department. The identification of root causes of repeat sessions and delays was followed by failure, mode and effect analysis, hazard analysis and decision tree analysis.

The most frequent causes of failure were malfunction of the RIS/PACS system and improper positioning of patients. Subsequent to extensive training of professionals, the sigma level was increased from 3.5 to 4.2.

The data were collected over only four months.

Six Sigma's data measurement and process improvement methodology is the impetus for health care organisations to rethink their workflow and reduce malpractice. It involves measuring, recording and reporting data on a regular basis. This enables the administration to monitor workflow continuously.

The improvements in the workflow under study, made by determining the failures and potential risks associated with radiologic care, will have a positive impact on society in terms of patient safety. Having eliminated repeat examinations, the risk of being exposed to more radiation was also minimised.

This paper supports the need to apply Six Sigma and present an evaluation of the process in an imaging department.

This case examines issues surrounding the choice of financing arrangements for the acquisition of Radiologix in July 2006. The case follows Mark Stolper, the CFO of RadNet, as he considers how to raise the $363 million in funds necessary to finance the acquisition. When completed, the combined firms will be the largest private diagnostic-imaging provider in the United States. When Stolper joined RadNet in 2003, he confronted a company with “too much debt, and the wrong kind of debt.” His goal is to finance the acquisition in a way that further enhances the financial strength and operating flexibility of the company. Given the large size of funding required, the firm is unlikely to be able to fund the entire transaction with first-lien or bank debt. His financial advisors differ in their recommendations for how to raise the remaining funds—one suggests using second-lien debt, and the other, high-yield debt.

The purpose of the case is to familiarize students with frequently encountered types of debt financing that are used to finance mergers and acquisitions and other corporate transactions. The case provides information on the distinctions among first-lien, second-lien, and high-yield debt in relation to their price, availability, flexibility of covenants, repayment ease, and composition of likely investors. The case is designed for use in courses that cover corporate financing, M&As, and debt financing.

The purposes of this paper are (1) to explore the overall development of AI technologies and applications that have been demonstrated to be fundamentally important in the healthcare industry, and their related commercialized products and (2) to identify technologies with promise as the basis of useful applications and profitable products in the AI-healthcare domain.

This study adopts a technology-driven technology roadmap approach, combined with natural language processing (NLP)-based patents analysis, to identify promising and potentially profitable existing AI technologies and products in the domain of AI healthcare.

Robotics technology exhibits huge potential in surgical and diagnostics applications. Intuitive Surgical Inc., manufacturer of the Da Vinci robotic system and Ion robotic lung-biopsy system, dominates the robotics-assisted surgical and diagnostic fields. Diagnostics and medical imaging are particularly active fields for the application of AI, not only for analysis of CT and MRI scans, but also for image archiving and communications.

This study is a pioneering attempt to clarify the interrelationships of particular promising technologies for application and related products in the AI-healthcare domain. Its findings provide critical information about the patent activities of key incumbent actors, and thus offer important insights into recent and current technological and product developments in the emergent AI-healthcare sector.

Pervasive health-care computing applications in medical field provide better diagnosis of various organs such as brain, spinal card, heart, lungs and so on. The purpose of this study is to find brain tumor diagnosis using Machine learning (ML) and Deep Learning(DL) techniques. The brain diagnosis process is an important task to medical research which is the most prominent step for providing the treatment to patient. Therefore, it is important to have high accuracy of diagnosis rate so that patients easily get treatment from medical consult. There are many earlier investigations on this research work to diagnose brain diseases. Moreover, it is necessary to improve the performance measures using deep and ML approaches.

In this paper, various brain disorders diagnosis applications are differentiated through following implemented techniques. These techniques are computed through segment and classify the brain magnetic resonance imaging or computerized tomography images clearly. The adaptive median, convolution neural network, gradient boosting machine learning (GBML) and improved support vector machine health-care applications are the advance methods used to extract the hidden features and providing the medical information for diagnosis. The proposed design is implemented on Python 3.7.8 software for simulation analysis.

This research is getting more help for investigators, diagnosis centers and doctors. In each and every model, performance measures are to be taken for estimating the application performance. The measures such as accuracy, sensitivity, recall, F1 score, peak-to-signal noise ratio and correlation coefficient have been estimated using proposed methodology. moreover these metrics are providing high improvement compared to earlier models.

The implemented deep and ML designs get outperformance the methodologies and proving good application successive score.

The purpose of this paper is to provide a detailed accounting of energy and materials consumed during magnetic resonance imaging (MRI).

The first and second stages of ISO standard (ISO 14040:2006 and ISO 14044:2006) were followed to develop life cycle inventory (LCI). The LCI data collection took the form of observations, time studies, real-time metered power consumption, review of imaging department scheduling records and review of technical manuals and literature.

The carbon footprint of the entire MRI service on a per-patient basis was measured at 22.4 kg CO₂eq. The in-hospital energy use (process energy) for performing MRI is 29 kWh per patient for the MRI machine, ancillary devices and light fixtures, while the out-of-hospital energy consumption is approximately 260 percent greater than the process energy, measured at 75 kWh per patient related to fuel for generation and transmission of electricity for the hospital, plus energy to manufacture disposable, consumable and reusable products. The actual MRI and standby energy that produces the MRI images is only about 38 percent of the total life cycle energy.

The focus on methods and proof-of-concept meant that only one facility and one type of imaging device technology were used to reach the conclusions. Based on the similar studies related to other imaging devices, the provided transparent data can be generalized to other healthcare facilities with few adjustments to utilization ratios, the share of the exam types, and the standby power of the facilities’ imaging devices.

The transparent detailed life cycle approach allows the data from this study to be used by healthcare administrators to explore the hidden public health impact of the radiology department and to set goals for carbon footprint reductions of healthcare organizations by focusing on alternative imaging modalities. Moreover, the presented approach in quantifying healthcare services’ environmental impact can be replicated to provide measurable data on departmental quality improvement initiatives and to be used in hospitals’ quality management systems.

No other research has been published on the life cycle assessment of MRI. The share of outside hospital indirect environmental impact of MRI services is a previously undocumented impact of the physician’s order for an internal image.

The purpose of the study is to classify the radiographic images into three categories such as fillings, cavity and implant to identify dental diseases because dental disease is a very common dental health problem for all people. The detection of dental issues and the selection of the most suitable method of treatment are both determined by the results of a radiological examination. Dental x-rays provide important information about the insides of teeth and their surrounding cells, which helps dentists detect dental issues that are not immediately visible. The analysis of dental x-rays, which is typically done by dentists, is a time-consuming process that can become an error-prone technique due to the wide variations in the structure of teeth and the dentist's lack of expertise. The workload of a dental professional and the chance of misinterpretation can be decreased by the availability of such a system, which can interpret the result of an x-ray automatically.

This study uses deep learning (DL) models to identify dental diseases in order to tackle this issue. Four different DL models, such as ResNet-101, Xception, DenseNet-201 and EfficientNet-B0, were evaluated in order to determine which one would be the most useful for the detection of dental diseases (such as fillings, cavity and implant).

Loss and accuracy curves have been used to analyze the model. However, the EfficientNet-B0 model performed better compared to Xception, DenseNet-201 and ResNet-101. The accuracy, recall, F1-score and AUC values for this model were 98.91, 98.91, 98.74 and 99.98%, respectively. The accuracy rates for the Xception, ResNet-101 and DenseNet-201 are 96.74, 93.48 and 95.65%, respectively.

The present study can benefit dentists from using the DL model to more accurately diagnose dental problems.

This study is conducted to evaluate dental diseases using Convolutional neural network (CNN) techniques to assist dentists in selecting the most effective technique for a particular clinical condition.

The purpose of this paper is to develop a set of research propositions concerned with how the alignment between socially responsible corporate image and corporate identity might be enhanced through the reduction of scepticism by considering diagnostic dimensions of the corporate social responsibility (CSR) image advertising claim.

The paper reviews corporate image advertising, the tool investigated for informing about the firm's CSR record, discusses the scepticism construct and theoretical explanations of why this communication approach might induce scepticism, considers extant empirical findings that lend support to these theories, and describes several elements of CSR advertising claims considered to be diagnostic and capable of inhibiting scepticism responses to CSR image advertisements among consumers. Research propositions are advanced and discussed.

The paper provides conceptual insights into reducing consumer scepticism toward CSR‐based corporate identity communicated via corporate image advertising.

The paper advances four research propositions, and proposes a method for testing these propositions.

The paper acknowledges the increase in CSR‐based corporate image advertising, discusses why such communication approaches may be prone to consumer scepticism, and considers message elements to inhibit this persuasion‐eroding cognitive response.

This paper suggests a study to understand how corporate identity based on CSR achievements can be more persuasively communicated via CSR‐based corporate image advertising

Nowadays, applications dealing with information extracted from images are commonplace. The widespread use of multimedia information (images, video, audio etc.) makes necessary applications capable of storing, and therefore retrieving, it. Information extracted from images is usually complex and high dimensional. The extraction of non‐textual low‐level indexing features from images is now a research field, and this process principally suffers because of the computational cost of the high dimensionality of those features. A new way to classify and match low‐level features extracted from images, for retrieval purposes, is presented in this paper. M‐tree and R‐tree structures are used, as well as an incremental version of the k‐means classification alogrithm. This set of alogrithms is used to solve the problem of low performance when retrieving previously catalogued images.

Rapidly evolving fintech and decentralized finance environments present an opportunity to reconsider how best to teach financial reporting, internal controls, auditing, taxation, and accounting information systems. Industrial firms have found considerable success in growing the customer value/cost ratio by applying “design thinking” (DT) to product and service innovation. DT may serve a similar, value-enhancing role in curriculum development and accounting pedagogy. The authors demonstrate the application of DT to the accounting curriculum using non-fungible tokens (NFTs) as an illustration. This chapter defines NFTs and DT, proposes a DT-based curriculum development model, and offers specific recommendations for teaching about NFTs in the classroom.