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Background The integration of large language models (LLMs) like ChatGPT in diagnostic medicine, with a focus on digital pathology, has garnered significant attention. However, understanding the challenges and barriers associated with the use of LLMs in this context is crucial for their successful implementation. Methods A scoping review was conducted to explore the challenges and barriers of using LLMs, in diagnostic medicine with a focus on digital pathology. A comprehensive search was conducted using electronic databases, including PubMed and Google Scholar, for relevant articles published within the past four years. The selected articles were critically analyzed to identify and summarize the challenges and barriers reported in the literature. Results The scoping review identified several challenges and barriers associated with the use of LLMs in diagnostic medicine. These included limitations in contextual understanding and interpretability, biases in training data, ethical considerations, impact on healthcare professionals, and regulatory concerns. Contextual understanding and interpretability challenges arise due to the lack of true understanding of medical concepts and lack of these models being explicitly trained on medical records selected by trained professionals, and the black-box nature of LLMs. Biases in training data pose a risk of perpetuating disparities and inaccuracies in diagnoses. Ethical considerations include patient privacy, data security, and responsible AI use. The integration of LLMs may impact healthcare professionals’ autonomy and decision-making abilities. Regulatory concerns surround the need for guidelines and frameworks to ensure safe and ethical implementation. Conclusion The scoping review highlights the challenges and barriers of using LLMs in diagnostic medicine with a focus on digital pathology. Understanding these challenges is essential for addressing the limitations and developing strategies to overcome barriers. It is critical for health professionals to be involved in the selection of data and fine tuning of the models. Further research, validation, and collaboration between AI developers, healthcare professionals, and regulatory bodies are necessary to ensure the responsible and effective integration of LLMs in diagnostic medicine.

This review aims to present current advancements in wearable technologies and IoT-based applications to support independent living. The secondary aim was to investigate the barriers and challenges of wearable sensors and Internet-of-Things (IoT) monitoring solutions for older adults. For this work, we considered falls and activity of daily life (ADLs) for the ageing population (older adults). A total of 327 articles were screened, and 14 articles were selected for this review. This review considered recent studies published between 2015 and 2019. The research articles were selected based on the inclusion and exclusion criteria, and studies that support or present a vision to provide advancement to the current space of ADLs, independent living and supporting the ageing population. Most studies focused on the system aspects of wearable sensors and IoT monitoring solutions including advanced sensors, wireless data collection, communication platform and usability. Moderate to low usability/ user-friendly approach is reported in most of the studies. Other issues found were inaccurate sensors, battery/ power issues, restricting the users within the monitoring area/ space and lack of interoperability. The advancement of wearable technology and the possibilities of using advanced IoT technology to assist older adults with their ADLs and independent living is the subject of many recent research and investigation.

Severe combined immunodeficiency disease (SCID) is the most severe form of primary immunodeficiency disorders (PID). T-cell receptor excision circle (TREC) copy number analysis is an efficient tool for population-based newborn screening (NBS) for SCID and other T cell lymphopenias. We sought to assess the incidence of SCID among Saudi newborn population and examine the feasibility of using targeted next generation sequencing PID gene panel (T-NGS PID) on DNA isolated from dried blood spots (DBSs) in routine NBS programs as a mutation screening tool for samples with low TREC count. Punches from 8,718 DBS collected on Guthrie cards were processed anonymously for the TREC assay. DNA was extracted from samples with confirmed low TREC count, then screened for 22q11.2 deletion syndrome by real-time polymerase chain reaction and for mutations in PID-related genes by T-NGS PID panel. Detected mutations were confirmed by Sanger sequencing. Sixteen out of the 8,718 samples were confirmed to have low TREC copy number. Autosomal recessive mutations in were confirmed in three samples. Two additional samples were positive for the 22q11.2 deletion syndrome. In this study, we provide evidence for high incidence of SCID among Saudi population (1/2,906 live births) and demonstrate the feasibility of using T-NGS PID panel on DNA extracted from DBSs as a new reliable, rapid, and cost-effective mutation screening method for newborns with low TREC assay, which can be implemented as part of NBS programs for SCID.

The main aim of this study is to investigate the opportunities, challenges, and barriers in implementing generative artificial intelligence (Gen AI) in personalized patient care plans (PPCPs). This systematic review paper provides a comprehensive analysis of the current state, potential applications, and opportunities of Gen AI in patient care settings. This review aims to serve as a key resource for various stakeholders such as researchers, medical professionals, and data governance. We adopted the PRISMA review methodology and screened a total of 247 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2021 and 2024 (inclusive). The selection criteria were based on the inclusion of studies that report on the opportunities and challenges in improving PPCPs using Gen AI. We found that a holistic approach is required involving strategy, communications, integrations, and collaboration between AI developers, healthcare professionals, regulatory bodies, and patients. Developing frameworks that prioritize ethical considerations, patient privacy, and model transparency is crucial for the responsible deployment of Gen AI in healthcare. Balancing these opportunities and challenges requires collaboration between wider stakeholders to create a robust framework that maximizes the benefits of Gen AI in healthcare while addressing the key challenges and barriers such as explainability of the models, validation, regulation, and privacy integration with the existing clinical workflows.

Shariah governance is the mechanism to monitor and implicate shariah compliance in Islamic banks. The study’s goal of exploring the Islamic bank’s governance attributes and credit scores or rating relationship in the presence of shariah board attributes as moderators. The study collected time-variant data from 22 Asian banks (286 observations) from 2006 to 2018. Applied descriptive statistics, correlations, Likelihood Ratio (LR) test and the Ordered Logistic Regression model, a suitable technique for the ordinal dependent variable. The study findings provide evidence of shariah governance’s moderating role in the relationship between corporate governance attributes and credit rating. Moreover, shariah board characteristics strengthen the association between the corporate board and credit worthiness nexus. This research recommends that credit score evaluating agencies consider the shariah governance characteristics in evaluating Ib’s credit rating. The shariah governance attributes as part of credit rating can be an appropriate method for investors to measure the shariah compliance level of Ibs. Accordingly, Ibs can gain the confidence of investors or sukuk investors by improving shariah compliance and can access competitive fund sources. The study’s uniqueness is in determining the impact of shariah governance attributes as moderators on the board-rating nexus. This study suggested that credit rating agencies revise or amend their assessment procedures for Ibs. Abundant literature is available from the owner’s point of view. Nonetheless, this research explores governance and shariah governance attributes concerning Sukuk holders.

Falls are one of the common health and well-being issues among the older adults. Internet of things (IoT)-based health monitoring systems have been developed over the past two decades for improving healthcare services for older adults to support an independent lifestyle. This research systematically reviews technological applications related to falls detection and falls management. The systematic review was conducted in accordance to the preferred reporting items for systematic reviews and meta-analysis statement (PRISMA). Twenty-four studies out of 806 articles published between 2015 and 2017 were identified and included in this review. Selected studies were related to pre-fall and post-fall applications using motion sensors (10; 41.67%), environment sensors (10; 41.67%) and few studies used the combination of these types of sensors (4; 16.67%). As an outcome of this review, we postulated a falls management framework (FMF). FMF considered pre- and post-fall strategies to support older adults live independently. A part of this approach involved active analysis of sensor data with the aim of helping the older adults manage their risk of fall and stay safe in their home. FMF aimed to serve the researchers, developers, clinicians and policy makers with pre- and post-falls management strategies to enhance the older adults’ independent living and well-being.

Background With the growth of information technology, there is a need for the evaluation of cost-effective means of monitoring and support of field workers involved in large epidemiological surveys. Aim The aim of this research was to measure the performance of a survey help desk that used knowledge management tools to improve its productivity and efficiency. Knowledge management tools are based on information technologies that improve the creation, sharing, and use of different types of knowledge that are critical for effective decision-making. Methods The Saudi National Mental Health Survey’s help desk developed and used specific knowledge management tools including a computer file system, feedback from experts and a call ticketing system. Results are based on the analyses of call records recorded by help desk agents in the call ticketing system using descriptive analysis, Wilcoxon rank-sum test (p < 0.01) and Goodman and Kruscal test (gamma). The call records were divided into two phases and included details such as types of calls, priority level and resolution time. Results The average time to resolve a reported problem decreased overall, decreased at each priority level and led to increased first contact resolution. Conclusion This study is the first of its kind to show how the use of knowledge management tools lead to a more efficient and productive help desk within a health survey environment in Saudi Arabia. Further research on help desk performance, particularly within health survey environments and the Middle Eastern region is needed to support this conclusion.

Background and Objective. Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. Methods. A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. Results. A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. Conclusion. The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

Health monitoring systems have rapidly evolved during the past two decades and have the potential to change the way health care is currently delivered. Although smart health monitoring systems automate patient monitoring tasks and, thereby improve the patient workflow management, their efficiency in clinical settings is still debatable. This paper presents a review of smart health monitoring systems and an overview of their design and modeling. Furthermore, a critical analysis of the efficiency, clinical acceptability, strategies and recommendations on improving current health monitoring systems will be presented. The main aim is to review current state of the art monitoring systems and to perform extensive and an in-depth analysis of the findings in the area of smart health monitoring systems. In order to achieve this, over fifty different monitoring systems have been selected, categorized, classified and compared. Finally, major advances in the system design level have been discussed, current issues facing health care providers, as well as the potential challenges to health monitoring field will be identified and compared to other similar systems.

Objective Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians’ performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS’ diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of off-line tests. The training and testing data set are selected randomly from 30 sets of patients’ data. Results The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist’s and FLMS’s diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.