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The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

The fourth industrial revolution and digital transformation have caused paradigm changes in the procedures of goods production and services through disruptive technologies, and they have formed new methods for business models. Health and medicine fields have been under the effect of these technology advancements. The concept of smart hospital is formed according to these technological transformations. The aim of this research, other than explanation of smart hospital components, is to present a model for evaluating a hospital readiness for becoming a smart hospital.

This research is an applied one, and has been carried out in three phases and according to design science research. Based on the previous studies, in the first phase, the components and technologies effecting a smart hospital are recognized. In the second phase, the extracted components are prioritized using type-2 fuzzy analytic hierarchical process based on the opinion of experts; later, the readiness model is designed. In the third phase, the presented model would be tested in a hospital.

The research results showed that the technologies of internet of things, robotics, artificial intelligence, radio-frequency identification as well as augmented and virtual reality had the most prominence in a smart hospital.

The innovation and originality of the forthcoming research is to explain the concept of smart hospital, to rank its components and to provide a model for evaluating the readiness of smart hospital. Contribution of this research in terms of theory explains the concept of smart hospital and in terms of application presents a model for assessing the readiness of smart hospitals.

Vision, audition, olfactory, tactile and taste are five important senses that human uses to interact with the real world. As facing more and more complex environments, a sensing system is essential for intelligent robots with various types of sensors. To mimic human-like abilities, sensors similar to human perception capabilities are indispensable. However, most research only concentrated on analyzing literature on single-modal sensors and their robotics application.

This study presents a systematic review of five bioinspired senses, especially considering a brief introduction of multimodal sensing applications and predicting current trends and future directions of this field, which may have continuous enlightenments.

This review shows that bioinspired sensors can enable robots to better understand the environment, and multiple sensor combinations can support the robot’s ability to behave intelligently.

The review starts with a brief survey of the biological sensing mechanisms of the five senses, which are followed by their bioinspired electronic counterparts. Their applications in the robots are then reviewed as another emphasis, covering the main application scopes of localization and navigation, objection identification, dexterous manipulation, compliant interaction and so on. Finally, the trends, difficulties and challenges of this research were discussed to help guide future research on intelligent robot sensors.

General Data Protection Regulation (GDPR) of the European Union (EU) was passed to protect data privacy. Though the GDPR intended to address issues related to data privacy in the EU, it created an extra-territorial effect through Articles 3, 45 and 46. Extra-territorial effect refers to the application or the effect of local laws and regulations in another country. Lawmakers around the globe passed or intensified their efforts to pass laws to have personal data privacy covered so that they meet the adequacy requirement under Articles 45–46 of GDPR while providing comprehensive legislation locally. This study aims to analyze the Malaysian and Saudi Arabian legislation on health data privacy and their adequacy in meeting GDPR data privacy protection requirements.

The research used a systematic literature review, legal content analysis and comparative analysis to critically analyze the health data protection in Malaysia and Saudi Arabia in comparison with GDPR and to see the adequacy of health data protection that could meet the requirement of EU data transfer requirement.

The finding suggested that the private sector is better regulated in Malaysia than the public sector. Saudi Arabia has some general laws to cover health data privacy in both public and private sector organizations until the newly passed data protection law is implemented in 2024. The finding also suggested that the Personal Data Protection Act 2010 of Malaysia and the Personal Data Protection Law 2022 of Saudi Arabia could be considered “adequate” under GDPR.

The research would be able to identify the key principles that could identify the adequacy of the laws about health data in Malaysia and Saudi Arabia as there is a dearth of literature in this area. This will help to propose suggestions to improve the laws concerning health data protection so that various stakeholders can benefit from it.

Purpose: The study aims to extract insights into five significant industries, pharmaceutical, space, defence, renewal energy, and information technology (IT), which have huge potential to make India achieving a five trillion-dollar economy in the future.

Design/methodology/approach: The authors focus on future-driven industries which are not only making India a third highest gross domestic product (GDP) producer country but also reviewing the different aspects of these industries and how they can assist India in achieving a five trillion-dollar economies along with determining India’s self-reliance through different governments initiatives in this direction.

Findings: The findings highlight the importance of inclusiveness of policymakers, stakeholders, private players, foreign investors, and the masses. Their significant contributions especially in the pharmaceutical, space, defence, renewal energy, and IT sectors in terms of creativities, innovations, intellect, executions, implementations, and improvements can assist India in achieving its five trillion-dollars economy soon.

Practical implications: This study offers (1) convincing insights into five key industries, pharmaceutical, space, defence, renewal energy, and IT, which have huge potential to increase total GDP volume shortly and (2) the investment areas for the masses where they can see their world not only self-reliant but also will see huge growth in their invested amount in these industries in future.

Originality/value: The insights of five key industries, pharmaceutical, space, defence, renewal energy, and IT, highlight that India has the potential to achieve a five trillion-dollar economy in the future; however, it does not ignore the significant contribution of other industries in making of total GDP.

This study has mainly aimed to compare and contrast two completely different image processing algorithms that are very adaptive for detecting prostate cancer using wearable Internet of Things (IoT) devices. Cancer in these modern times is still considered as one of the most dreaded disease, which is continuously pestering the mankind over a past few decades. According to Indian Council of Medical Research, India alone registers about 11.5 lakh cancer related cases every year and closely up to 8 lakh people die with cancer related issues each year. Earlier the incidence of prostate cancer was commonly seen in men aged above 60 years, but a recent study has revealed that this type of cancer has been on rise even in men between the age groups of 35 and 60 years as well. These findings make it even more necessary to prioritize the research on diagnosing the prostate cancer at an early stage, so that the patients can be cured and can lead a normal life.

The research focuses on two types of feature extraction algorithms, namely, scale invariant feature transform (SIFT) and gray level co-occurrence matrix (GLCM) that are commonly used in medical image processing, in an attempt to discover and improve the gap present in the potential detection of prostate cancer in medical IoT. Later the results obtained by these two strategies are classified separately using a machine learning based classification model called multi-class support vector machine (SVM). Owing to the advantage of better tissue discrimination and contrast resolution, magnetic resonance imaging images have been considered for this study. The classification results obtained for both the SIFT as well as GLCM methods are then compared to check, which feature extraction strategy provides the most accurate results for diagnosing the prostate cancer.

The potential of both the models has been evaluated in terms of three aspects, namely, accuracy, sensitivity and specificity. Each model’s result was checked against diversified ranges of training and test data set. It was found that the SIFT-multiclass SVM model achieved a highest performance rate of 99.9451% accuracy, 100% sensitivity and 99% specificity at 40:60 ratio of the training and testing data set.

The SIFT-multi SVM versus GLCM-multi SVM based comparison has been introduced for the first time to perceive the best model to be used for the accurate diagnosis of prostate cancer. The performance of the classification for each of the feature extraction strategies is enumerated in terms of accuracy, sensitivity and specificity.

This study aims to examine the mediating role of social influence on the relationship between key predictors of E-pharmacy adoption among young consumers based on the unified theory of adoption and use of technology (UTAUT).

This study employs a quantitative correlational research design. Based on cluster sampling, data was collected from 306 university students from three public universities in southwestern Nigeria. Data was analysed using partial least square structural equation modeling.

The primary determinant driving the adoption of e-pharmacy is performance expectancy. Social influence plays a partial mediating role in linking performance expectancy to e-pharmacy adoption. In contrast, it fully mediates the relationship between effort expectancy, facilitating conditions and the adoption of e-pharmacy services.

This study provides theoretical clarity on recent issues within the UTAUT framework. Findings highlight the complexity of how social factors interact with individual beliefs and external conditions in determining technology acceptance.

Research includes information relevant to access the impact of e-pharmacy services on healthcare accessibility, affordability and quality in developing countries.

The findings extend the adoption of technology literature in healthcare and offer a new understanding of adoption dynamics. The results emphasize the importance of performance expectancy in driving e-pharmacy adoption, providing a clear direction for stakeholders to enhance service quality and user experience of e-pharmacy. Additionally, the mediating effect of social influence highlights the significance of peer recommendations, celebrity endorsements and social media campaigns in shaping consumer adoption of e-pharmacies among young people.

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

Gesture recognition plays an important role in many fields such as human–computer interaction, medical rehabilitation, virtual and augmented reality. Gesture recognition using wearable devices is a common and effective recognition method. This study aims to combine the inverse magnetostrictive effect and tunneling magnetoresistance effect and proposes a novel wearable sensing glove applied in the field of gesture recognition.

A magnetostrictive sensing glove with function of gesture recognition is proposed based on Fe-Ni alloy, tunneling magnetoresistive elements, Agilus30 base and square permanent magnets. The sensing glove consists of five sensing units to measure the bending angle of each finger joint. The optimal structure of the sensing units is determined through experimentation and simulation. The output voltage model of the sensing units is established, and the output characteristics of the sensing units are tested by the experimental platform. Fifteen gestures are selected for recognition, and the corresponding output voltages are collected to construct the data set and the data is processed using Back Propagation Neural Network.

The sensing units can detect the change in the bending angle of finger joints from 0 to 105 degrees and a maximum error of 4.69% between the experimental and theoretical values. The average recognition accuracy of Back Propagation Neural Network is 97.53% for 15 gestures.

The sensing glove can only recognize static gestures at present, and further research is still needed to recognize dynamic gestures.

A new approach to gesture recognition using wearable devices.

This study has a broad application prospect in the field of human–computer interaction.

The sensing glove can collect voltage signals under different gestures to realize the recognition of different gestures with good repeatability, which has a broad application prospect in the field of human–computer interaction.

The purpose of this paper is to investigate the process by which the level of immersion in virtual reality-based behavioral simulation (VR-BS) impacts on the non-cognitive and cognitive outcomes. The cognitive outcome is measured using the increase in the level of Sharia financial literacy, while the noncognitive outcome is measured using the behavioral intention to use VR-BS.

The method consists of two parts: First, the development of VR-BS, in the context of sharia financial literacy, using the waterfall model. Second, testing the effectiveness of VR-BS using the theory of interactive media effects framework. The participants were 142 students from three secondary schools (two Islamic religious schools and one public school) in Yogyakarta and Central Java, Indonesia. Partial least squares structural equation modeling was used for testing the hypotheses.

VR-BS creates a perceived coolness and vividness, which in turn has an impact on increasing the participants’ engagement. Also, the use of VR has an impact on natural mapping, which increases a user’s engagement through its perceived ease of use. As predicted, the user’s engagement affects VR’s behavior, mediated by the user’s attitude toward VR media. VR’s interactivity, however, does not impact on the cognitive aspect.

The participants were not randomly selected, as the data were collected during the COVID-19 pandemic. As a result, the majority of the participants had never tried VR before this study. The participants, however, were digital natives.

It is implied from the findings that Islamic financial business actors and the relevant government agencies (e.g. the Indonesian Financial Services Authority [OJK], the Ministry of Education, Culture, Research and Technology and the Ministry of Religious Affairs) should collaborate to best prepare the future generation of ummah by using VR-BS in their joint promotion and education programs. The results of the current study reveal that the use of VR-BS may attract people to engage in Islamic financial activities. By engaging in such activities, or at least engaging in real-life simulations/classes/workshops, people may gradually acquire more knowledge about Islamic finance.

As predicted, the user’s engagement has an impact on behavior toward VR-BS, which is mediated by attitude toward VR-BS.