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Pressure from competition; inflexible third-party reimbursements; greater demand from government, regulatory and certifying agencies; discerning patients; and the quest of healthcare entities for greater profitably place demands and high expectations for service quality impacting overall patient experience. Extending a prior multivariate, single-period model of varied medical practices predicting patient experience to a three-year time period to understand whether there was a change in overall assessment using data analytics. The paper aims to discuss these issues.

SEM was employed on a per year and aggregated, three-year basis to gain insights into qualitative psychometric constructs predicting overall patient experience and strength of the relationships.

Statistically significant differences were uncovered between years indicating the strength of the relationships of latent variables on overall performance.

Study focused on data gathered from a questionnaire mailed to patients who visited various outpatient medical clinics in a rural community with over 4,000 responses during the three-year study period. A higher percentage of female respondents over the age of 45 may limit the generalizability of the findings.

Practitioners can gain a broader understanding of different factors influencing overall patient experience. Administrative processes associated with the primary care provider are inconsequential. Patients are not as concerned with patient flow as they are with patient safety and health.

This research informs healthcare quality management of psychometrics and analytics to improve the overall patient experience in outpatient medical clinics.

It is the responsibility of the national governments to deliver healthcare services that are both effective and affordable to everyone. There are still gaps in this supply, which is extremely demanding. In this sense, companies are attempting to reach neglected markets and disrupt the marketplace with novel solutions. Although there are still anecdotal examples, a thorough literature evaluation is lacking. This study aims to provide a synthesis of the future of healthcare start-ups.

Papers that included the term “healthcare start-ups,” “health-tech start-ups,” “start-up,” “Artificial intelligence in healthcare,” and “Health tech start-ups in India” were considered for the analysis. The Biblioshiny package under the R programming tool was considered for a detailed analysis of the papers.

A total of 854 documents were related to healthcare start-ups, from which only 14 papers are related to health-tech start-ups and four papers are related to artificial intelligence in healthcare start-ups. It has been found from the past works of literature that the effectiveness of technology for information and communication in healthcare has significantly increased in recent years. Technology has already begun to permeate the healthcare market from other fields and industries. One way that the internet will help the industry evolve is by integrating digital health into daily life.

The study is not using other databases but is limited to Google Scholar and Scopus. A significant constraint of this study is the paucity of relevant literature in reputable publications on health and information systems. Another restriction was that gray literature, such as any journal or newspaper written by members of the health community about health-tech start-ups, was not taken into account.

Healthcare players should exhibit a fundamental openness to novel solutions to facilitate the digitalization of the healthcare system. Developing technology is widely used, and from an innovation perspective, a start-up should focus on innovation by employing technology and offering revolutionary healthcare solutions.

The novelty of this research is based on its presentation of an organized and thorough literature evaluation, which defines the current state of the art concerning green start-ups. To create a sustainable start-up, a thorough study of the information gained in respect of its healthcare start-up is presented.

The purpose of this paper is to present near set theory using the perceptual indiscernibility and tolerance relations, to demonstrate the practical application of near set theory to the image correspondence problem, and to compare this method with existing image similarity measures.

Image‐correspondence methodologies are present in many systems that are depended on daily. In these systems, the discovery of sets of similar objects (aka, tolerance classes) stems from human perception of the objects being classified. This view of perception of image‐correspondence springs directly from Poincaré's work on visual spaces during 1890s and Zeeman's work on tolerance spaces and visual acuity during 1960s. Thus, in solving the image‐correspondence problem, it is important to have systems that accurately model human perception. Near set theory provides a framework for measuring the similarity of digital images (and perceptual objects, in general) based on features that describe them in much the same way that humans perceive objects.

The contribution of this paper is a perception‐based classification of images using near sets.

The method presented in this paper represents a new approach to solving problems in which the goal is to match human perceptual groupings. While the results presented in the paper are based on measuring the resemblance between images, the approach can be applied to any application that can be formulated in terms of sets such that the objects in the sets can be described by feature vectors.

Nowadays, the digitized economy and technological advancements are increasing at a faster pace. One such technology that is gaining popularity in the healthcare sector is Artificial Intelligence (AI). AI has been debated much, searched so well due to the implications, issues and for its benefits in terms of ease, it will offer. The following research has focused on examining the ethical dilemmas associated with AI when it will be introduced in the healthcare sector.

A narrative review method focusing on content analysis has been used in the research. The authors have employed a deductive approach to determine the ethical facets of adopting AI in the healthcare sector. The current study is complemented by a review of related studies. The secondary data have been collected from authentic resources available on the Internet.

Patient privacy, biased results, patient safety and Human errors are some major ethical dilemmas that are likely to be faced once AI will be introduced in healthcare. The impact of ethical dilemmas can be minimized by continuous monitoring but cannot be eliminated in full if AI is introduced in healthcare. AI overall will increase the performance of the healthcare sector. However, we need to address some recommendations to mitigate the ethical potential issues that we could observe using AI. Technological change and AI can mimic the overall intellectual process of humans, which increases its credibility and also offers harm to humans.

Patient safety is the most crucial ethical concern because AI is a new technology and technology can lead to failure. Thus, we need to be certain that these new technological developments are ethically applied. The authors need to evaluate and assess the organizational and legal progress associated with the emergence of AI in the healthcare sector. It also highlights the importance of covering and protecting medical practitioners regarding the different secondary effects of this artificial medical progress. The research stresses the need of establishing partnerships between computer scientists and clinicians to effectively implement AI. Lastly, the research highly recommends training of IT specialists, healthcare and medical staff about healthcare ethics.

The purpose of this paper is to show an efficient method for the detection of signs of early lung cancer. Various image processing algorithms are presented for different types of lesions, and a scheme is proposed for the combination of results.

A computer aided detection (CAD) scheme was developed for detection of lung cancer. It enables different lesion enhancer algorithms, sensitive to specific lesion subtypes, to be used simultaneously. Three image processing algorithms are presented for the detection of small nodules, large ones, and infiltrated areas. The outputs are merged, the false detection rate is reduced with four separated support vector machine (SVM) classifiers. The classifier input comes from a feature selection algorithm selecting from various textural and geometric features. A total of 761 images were used for testing, including the database of the Japanese Society of Radiological Technology (JSRT).

The fusion of algorithms reduced false positives on average by 0.6 per image, while the sensitivity remained 80 per cent. On the JSRT database the system managed to find 60.2 per cent of lesions at an average of 2.0 false positives per image. The effect of using different result evaluation criteria was tested and a difference as high as 4 percentage points in sensitivity was measured. The system was compared to other published methods.

The study described in the paper proves the usefulness of lesion enhancement decomposition, while proposing a scheme for the fusion of algorithms. Furthermore, a new algorithm is introduced for the detection of infiltrated areas, possible signs of lung cancer, neglected by previous solutions.

A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs).

Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results.

Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed.

A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image.

This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing.

In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.

The purpose of this paper is to explain why ROC analysis is an inappropriate replacement for probative analysis in lineup research.

Taking as the medical example comparing two methods to detect the presence of a malignant tumor (Mickes et al., 2012), and operationally defining ROC analysis: radiologists are shown the results from two methods. Their confidence judgments create a graph of correct identifications by mistaken ones. The author can compare the methods on radiologists’ ability to differentiate sick from healthy. Lineup researchers create two distinct lineups. In target-present lineups, witnesses differentiate between the target and the foils, not the target and the innocent suspect. In target-absent lineups, witnesses cannot even differentiate between innocent suspects and foils, having seen none.

Eyewitness ROC curves are similar to probative analysis, but provide less useful information.

Researchers ware warned against using ROC when conducting lineup research.

Preventing inappropriate use of ROC analysis.

Content‐based image retrieval (CBIR) is an important research area for automatically retrieving images of user interest from a large database. Due to many potential applications, facial image retrieval has received much attention in recent years. Similar to face recognition, finding appropriate image representation is a vital step for a successful facial image retrieval system. Recently, many efficient image feature descriptors have been proposed and some of them have been applied to face recognition. It is valuable to have comparative studies of different feature descriptors in facial image retrieval. And more importantly, how to fuse multiple features is a significant task which can have a substantial impact on the overall performance of the CBIR system. The purpose of this paper is to propose an efficient face image retrieval strategy.

In this paper, three different feature description methods have been investigated for facial image retrieval, including local binary pattern, curvelet transform and pyramid histogram of oriented gradient. The problem of large dimensionalities of the extracted features is addressed by employing a manifold learning method called spectral regression. A decision level fusion scheme fuzzy aggregation is applied by combining the distance metrics from the respective dimension reduced feature spaces.

Empirical evaluations on several face databases illustrate that dimension reduced features are more efficient for facial retrieval and the fuzzy aggregation fusion scheme can offer much enhanced performance. A 98 per cent rank 1 retrieval accuracy was obtained for the AR faces and 91 per cent for the FERET faces, showing that the method is robust against different variations like pose and occlusion.

The proposed method for facial image retrieval has a promising potential of designing a real‐world system for many applications, particularly in forensics and biometrics.

Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.

Breast cancer is one of the most common malignant tumors in women, which badly have an effect on women's physical and psychological health and even danger to life. Nowadays, mammography is considered as a fundamental criterion for medical practitioners to recognize breast cancer. Though, due to the intricate formation of mammogram images, it is reasonably hard for practitioners to spot breast cancer features.

The performance analysis was done for both segmentation and classification. From the analysis, the accuracy of the proposed IAP-CSA-based fuzzy was 41.9% improved than the fuzzy classifier, 2.80% improved than PSO, WOA, and CSA, and 2.32% improved than GWO-based fuzzy classifiers. Additionally, the accuracy of the developed IAP-CSA-fuzzy was 9.54% better than NN, 35.8% better than SVM, and 41.9% better than the existing fuzzy classifier. Hence, it is concluded that the implemented breast cancer detection model was efficient in determining the normal, benign and malignant images.

This paper adopts the latest Improved Awareness Probability-based Crow Search Algorithm (IAP-CSA)-based Region growing and fuzzy classifier for enhancing the breast cancer detection of mammogram images, and this is the first work that utilizes this method.

Breast cancer (BC) presents a major public health challenge world-over including India. While several risk-factors, early signs and symptoms of BC are known, the knowledge and awareness of this disease remains poor among the population. The present study aimed to determine the extent of knowledge and awareness of BC, its risk factors, early signs and symptoms and breast self-examination (BSE) practice as an early detection method among Indian college-going female students.

The authors conducted a cross-sectional survey at a University in Delhi-NCR. Data on socio-demographic, knowledge and awareness of BC including BSE was collected using a pretested questionnaire. Chi-square test and logistic regression analysis was performed. All tests were two-sided and significance was set at p < 0.05.

A total of 866 female students participated in the study with mean age of 22.32 (±0.146) years having mean body mass index (BMI) of 21.22 (±3.52). As high as 82.1% of the participants had heard of BC but while 74.8% thought early detection is possible, 70.7% believed BC cannot be prevented. Gene mutations (60.2%) were identified as a significant risk factor, while breast pain (61.4%) was commonly recognized as a sign of BC. Only 29.8% of students ever performed BSE. Increased odds of performing BSE (OR = 3.4) was found among students who recognized gene mutations as an important BC risk factor.

Knowledge and awareness of BC including BSE among female college students were found to be below average. It is suggested that there is an urgent need for increasing BC awareness among young girls through workshops and mobile-health interventions.

This study provides new information on the level of knowledge and awareness of BC risk factors, sign and symptoms and self-examination practice among young college girls. Moreover, this study advocates the need for design and implementation of a sustainable digital health model for active population BC screening, which is not being done currently.

BC is a highly aggressive disease, which is now one of the leading causes of morbidity and mortality in India and world over. Although the knowledge of BC risk factors and its signs and symptoms have increased, the awareness of these elements among the general population at large is low and/or missing, especially in India. Furthermore, as a consequence of unorganized screening programs in the country, majority of women are presenting young with locally advanced disease. Understanding the existing level of knowledge and educating school, college and University students of the pertinent factors and screening practices such as BSE could drastically help in improving the self-screening and/or clinical examination rates. This could potentially lead to early detection and improved prognosis, thus ameliorating disease burden.

This study is one of the few studies conducted in India among young female college students belonging to non-medical backgrounds, delineating the level of knowledge and awareness of BC risk factors and signs and symptoms along with practice of early detection method such as BSE. The study has a considerable sample size and provides valuable evidence for a need to implement programs incorporating digital health models for accelerating awareness and screening of young girls in both rural and urban settings.