The SWOT analysis, pivotal in the competitive healthcare sector, aids dentists in identifying and analyzing internal strengths and weaknesses, as well as external opportunities and threats. This study focuses on senior dental students, aiming to scrutinize their use of SWOT analysis and evaluate its efficacy as an educational tool for career decision-making in dental entrepreneurship. The research sample comprises 116 senior dental students in the final stages of their dental education, with data collection accomplished through the administration of an e-questionnaire. The data extracted from the SWOT analysis encompass internal and external factors, gender distinctions, and outcomes derived from Stepwise Binary Logistic Regression concerning predictor markers. The results from the SWOT analysis of 114 questionnaires, revealed that participants identified communication skills (50%) and organization skills (49.10%) as their primary strengths, followed by favorable personal traits contributing to goal success (36%). Weaknesses predominantly centered around emotional and personal traits like anxiety (41.20%) and other characteristics, alongside practical challenges such as lack of initial capital (24.60%). Main opportunities included collaboration with experienced dentists (33.30%) and access to training programs (27.20%), while economic instability in Greece (77.20%) and the saturated dentist profession (26.30%) were perceived as significant threats. Gender differences were notable, with female dentists more likely to report organization skills as a strength and anxiety as a weakness. Values such as industriousness, persistence, and ethics were commonly shared, with actions focusing on training programs (57.9%) and gaining experience with experienced dentists (29.8%). Cluster analysis identified two subgroups, with one emphasizing on utilizing all available options (n=49) and the other prioritizing on gaining professional skills and experience (n=65). Logistic regression indicated that participants valuing industriousness were less likely to explore all available options, while those recognizing personal traits were more likely to do so. The study's outcomes highlight key predictor factors linked to a proactive orientation in career decision-making among senior dental students. These insights offer valuable implications for educational institutions and career counselors.

Given the complexity and variability of gastrointestinal polyps, there is a growing need for more accurate machine-learning algorithms that can assist medical professionals in decision-making processes. In our study, we employed machine learning (ML) techniques to classify gastrointestinal polyps seen in colonoscopy images, that present a risk of colon cancer, affecting a large portion of the population. The dataset used in the research comprised 152 instances and included three types of lesions, totaling 76 polyps. The study consisted of applying Logistic Regression (LR) to classify gastrointestinal images. The principal motivation for choosing this algorithm consists in the fact that is preferred by many medical researchers. Another motivation consisted in the fact to compare the polyps classification accuracy of physicians and LR with optimized hyperparameters. The approached model performance was evaluated with the following metrics: Accuracy, Precision, Recall, F1_Score and Macro-average. We used the Multiclass Logistic Regression (mLR) classifier to classify polyps into hyperplastic (27.63%), serrated (19.74%), and adenoma lesions (52.63%). It should be noted that serrated polyps are hard to diagnose, even by physicians, because they have characteristics of both hyperplastic and adenoma ones. One of the objectives consisted in the study of the hyperparameters tunning of the Logistic Regression to find the best-fitted model. To check the influence of the data mix on the results obtained, for the two best models (LR with liblinear solver, L1 penalty and C = 0.01), it was performed a comprehensive statistical analysis. Based on an algorithm that admits obtaining accurate results. Such analyses are mistakenly applied in the scientific literature. The model with optimized hyperparameters achieved an accuracy score of 70.39%, indicating a reasonable level of prediction, surpassing both beginner and expert physicians and obtaining results similar to those obtained by Random Subspace and Random Forest. The model successfully classifies the hyperplastic and adenoma polyps but showed to have a more limited ability to predict the serrated ones.

Explainable AI (XAI) has emerged as a pivotal tool in healthcare diagnostics, offering much-needed transparency and interpretability in complex AI models. XAI techniques, such as SHAP, Grad-CAM, and LIME, enable clinicians to understand AI-driven decisions, fostering greater trust and collaboration between human and machine in clinical settings. This review explores the key benefits of XAI in enhancing diagnostic accuracy, personalizing patient care, and ensuring compliance with regulatory standards. However, despite its advantages, XAI faces significant challenges, including balancing model accuracy with interpretability, scaling for real-time clinical use, and mitigating biases inherent in medical data. Ethical concerns, particularly surrounding fairness and accountability, are also discussed in relation to AI's growing role in healthcare. The review emphasizes the importance of developing hybrid models that combine high accuracy with improved interpretability and suggests that future research should focus on explainable-by-design systems, reducing computational costs, and addressing ethical issues. As AI continues to integrate into healthcare, XAI will play an essential role in ensuring that AI systems are transparent, accountable, and aligned with the ethical standards required in clinical practice.

Artificial Intelligence (AI) is revolutionizing decision-making procedures in healthcare information systems (HIS), increasing the efficacy, accuracy, and personalization of healthcare. Notwithstanding remarkable progresses, yet there remain gaps in the holistic comprehension and implication of AI-driven technologies across various HIS scenarios. These gaps, consisting limited integration of AI across a variety of HIS parts and inadequate emphasize on interpretability and privacy, motivate our study to conduct a structured evaluation of recent AI applications in this domain. In this study, we present a novel taxonomy for the AI implication in medical decision-making. We analyzed 30 articles and categorized them into six groups including: Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Computer-Aided Diagnostics (CAD), Robotic-Assisted Surgery, and Virtual Health Assistants (VHAs/chatbots). Our study aims to propose a holistic perspective of the fast evolving perspective of AI in HIS. The majority of the investigated studies were published from 2023 and 2022, with Springer as the leading publisher (33%). Python and MATLAB were the most well-known simulation languages, applied in 48% and 20% of the papers, respectively, reflecting the technical trends in the area. By emphasizing on pivotal criteria like interpretability, accuracy, and privacy, this research aims to elucidate the crucial issues and development driving AI-driven decision-making in HIS.

Background: Over the past few years there has been immense evolution in various areas particularly in the areas of digital technologies wherein the pace of change is very high. Industrial areas such as operations and supply chain management together with advanced technologies such as machine learning, big data analytics, artificial intelligence, as well as the Internet of Things, create completely different forms of operational models for various industries. In the area of healthcare too, these emerging computational sophistication is introduced to revolutionise the approaches to prevent, diagnose and treat diverse diseases and illnesses. Objective: The objective of this study is to provide an extensive review of the contemporary approaches utilizing data to cope with significant mental disorders. From over 60 relevant scholarly articles published between 2011 and 2023, it discusses how tools such as predictive modelling, social media analysis, data from smartphones, and chatbots help with issues such as early detection, telemonitoring, provision of psychological support, and individualised prevention. Method: An initial literature review to analyse over 60 research articles, which include empirical studies that were conducted between 2011 and 2023. The research assessed implemented novel digital approaches to mental health interventions including big data analytics for predicting condition status, machine learning for examining social media content, behaviour monitoring through smartphone sensors, and using conversational agents or chatbots. The following is an overview of general conclusions from experimental and descriptive secondary research studies published in professional outlets concerning possible advantages and disadvantages of data science applied to important concerns in mental health. Results: Research reveals that integrating subtle e-health tools in tandem with typical treatment approaches holds the potential to expand mental health services to more or less integrate them into clients’ day-to-day lives, and practically individualize effective treatments accordingly. Technological solutions for instance allow remote risk assessment, symptom monitoring and determination of treatment compliance. New lines of virtualized paradigm solve social challenges that interfere with the conventional provision and consumption of care. However, questions of privacy and the long-term effects as well as clinical adoption are yet to be solved in a analytically distinct manner.

The relentless challenges posed by the COVID-19 pandemic have placed immense pressure on densely populated regions, such as Hong Kong, particularly in the context of an aging population. The burgeoning demand for healthcare services has necessitated the optimization of resource al-location within healthcare systems. This research presents an innovative analytical queueing model specifically tailored to address the intricate gridlock dynamics prevalent in healthcare systems, with a focused application in the challenging landscape of COVID-19 management. Hong Kong, like numerous other regions, has experienced a demo-graphic transition leading to heightened healthcare demands. Traditional methodologies for scrutinizing gridlock issues in healthcare settings, including simulation models, often prove suboptimal within the context of sophisticated optimization frameworks. This study, however, delves into analytical queueing models, offering a more efficient and effective approach. These models have been underexplored, primarily due to the intricacies involved in modeling gridlock propagation within restricted capacity, a critical concern magnified by the COVID-19 pandemic. The novel Queueing Model introduced in this research incorporates the maintenance of queue capacities and topological aspects as exogenous parameters, rendering it exceptionally adaptable for diverse gridlock-related applications. This adaptability is especially pertinent to healthcare management during the COVID-19 crisis. The model meticulously integrates the concept of gridlock, furnishing a comprehensive comprehension of its origins and repercussions. Validation of this model involves rigorous comparison with established methodologies, exact results, and simulation data, consistently high-lighting its versatility and precision. By applying the model to healthcare management, we scrutinize the dynamics of patient flow within a Hong Kong hospital, placing specific emphasis on the critical issue of bed blockage, further exacerbated by the COVID-19 pandemic. The model's gridlock decomposition unearths the nuanced impact of bed blockage on distinct hospital units, addressing the imperative requirement for quantifying in-patient bed blockage within healthcare systems during a pandemic.

Viral hepatitis caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) pose a significant global health challenge, with an increasing number of deaths and a substantial burden on healthcare systems, among prisoners in particular. Nigeria ranks fifth in Africa for prison population, with 98.2% being males and are more likely than females to have higher rates of viral hepatitis and other risky behaviours. This literature review explores the impact of social determinants of health (SDoH) on the prevalence and outcomes of HBV and HCV infections among male prison inmates in Nigeria, by examining factors such as overcrowding, inadequate healthcare access, and food insecurity. This review reports the updated data on HBV and HCV infection rates among inmates in different regions in Nigeria, with a special focus on related risk factors, highlighting how these determinants contribute to the high rates of hepatitis infections in this category. The review underscores the urgent need for a multifaceted approach involving policy changes, improved healthcare access, and enhanced preventive measures to address these health disparities. The findings aim to inform public health strategies and interventions to reduce the transmission and impact of viral hepatitis in prison settings, aligning with global health goals and promoting health equity among this population group.

Despite the potential benefits of generative Artificial Intelligence (genAI), concerns about its psy-chological impact on medical students, especially with regard to job displacement, are apparent. This pilot study, conducted in Jordan during July–August 2024, aimed to examine the specific fears, anxieties, mistrust, and ethical concerns medical students could harbor towards genAI. Using a cross-sectional survey design, data were collected from 164 medical students studying in Jordan across various academic years, employing a structured self-administered questionnaire with an internally consistent FAME scale—representing Fear, Anxiety, Mistrust, and Ethics and comprising 12 items, with three items for each construct. The results indicated variable levels of anxiety towards genAI among the participating medical students: 34.1% reported no anxiety about genAI role in their future careers (n = 56), while 41.5% were slightly anxious (n = 61), 22.0% somewhat anxious (n = 36), and 2.4% extremely anxious (n = 4). Among the FAME constructs, Mistrust was the most agreed upon (mean: 12.35±2.78), followed by Ethics construct (mean: 10.86±2.90), Fear (mean: 9.49±3.53), and Anxiety (mean: 8.91±3.68). Sex, academic level, and Grade Point Average (GPA) did not significantly affect the students’ perceptions of genAI. However, there was a notable direct association between the students’ general anxiety about genAI and elevated scores in the Fear, Anxiety, and Ethics constructs of the FAME scale. Prior exposure to genAI and its previous use did not significantly modify the scores of the FAME scale. These findings highlighted the critical need for refined educational strategies to address the integration of genAI in medical training. The results demonstrated a pervasive anxiety, fear, mistrust, and ethical concerns among medical students regarding the deployment of genAI in healthcare, indicating the necessity for curriculum modifi-cations that focus specifically on these areas. Interventions should be tailored to increase genAI familiarity and competency, which would alleviate apprehension and equip future physicians to engage with this inevitable technology effectively. The study also highlighted the importance of incorporating ethical discussions into medical courses to address mistrust and concerns about the human-centered aspects of genAI. Conclusively, the study calls for a proactive evolution of medical education to prepare students for AI-driven healthcare practices shortly to ensure that physicians are well-prepared, confident, and ethically informed in their professional interactions with genAI technologies.

This research addresses the challenge of integrating fairness considerations into optimization problems, focusing on the development of multi-objective optimization techniques that balance traditional efficiency metrics with fairness constraints. In many algorithmic decision-making processes, optimizing for efficiency alone can inadvertently perpetuate or exacerbate biases, leading to inequitable outcomes. This study explores how data analysis and machine learning can be leveraged to identify and mitigate these biases, ensuring that optimization models yield both efficient and fair solutions. The research presents a framework for incorporating fairness constraints into multi-objective optimization, utilizing various fairness metrics such as demographic parity, equal opportunity, and disparate impact. It investigates the application of this framework across different domains, including resource allocation in healthcare systems, loan approvals in finance, and personalized learning platforms in education. By analyzing real-world data sets, the study demonstrates how fairness-aware optimization can lead to more equitable outcomes without significantly compromising efficiency. Empirical results from case studies show that multi-objective optimization with fairness constraints can effectively balance the trade-off between efficiency and equity. The research also discusses the computational challenges and ethical considerations associated with implementing fairness constraints in optimization models. Strategies for addressing these challenges, such as regularization techniques and fairness-aware machine learning algorithms, are evaluated and presented. Findings highlight the importance of incorporating fairness into optimization processes to prevent algorithmic biases and promote social equity. The study concludes with practical recommendations for policymakers, practitioners, and researchers on adopting fairness-aware optimization techniques to ensure that algorithmic decision-making processes are both efficient and just.

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

Background: The Covid-19 pandemic was in reality a syndemic there the balance between a multitude of biological (Nature-made) and socio-political (men-made) factors determined individually the outcome of every single infection, on a scale of nothing-to-fatal. Blaming the virus for everything bad that happened to us after January 2020 is incorrect. Now, in ~ 2 years distance from the syndemic, it may be timely and possible to uncover the human contribution to this historical event and learn of it. Objective: To identify and use some qualitative approach to estimate the objective, statistical / epidemiological ‘value’ of the virus-selected individuals (in age-normalized groups) for the rest of the society. This non-political, non-religious approach may be the only way to understand the benefit / cost ratio of actions and emergency orders we had to survive in addition to the virus itself. Methods: Statistical data were obtained from publicly available official databases, including the relevant actuarial tables and evaluated by widely used statistical methods. The estimation of statistical/epidemiological ‘value of life’ was based, strictly and exclusively, on the real age and the statistically calculated Residual Life Expectancy (RLE, i.e. remaining years alive at a given age). The American healthcare expenses were compared to the corresponding healthcare costs/capita in other similarly well-developed west-European countries. This was the only available method to get some ideas about the magnitude of the well-established (rational) healthcare spending and compare it to the American standards. Results 1) Life expectancy uninterruptedly increased in USA since 1933, due to continuous reduction of Mortality Rate of the youngest and oldest citizens. COVID didn’t stop the population growth (+8.2 M /2020-2023). 2) The total count of Excess COVID associated Deaths (XCD) is ~ 2-times as much as the official statistic was able to explain by virus infection. It is speculated that these Complementary Covid Deaths (CCD) are probably the unexpected side-effects of the pandemic related emergency orders. 3) The worldwide ‘longevity’ trend exponentially increased the healthcare expenses, especially the care of elderly. However the USA is unique in this aspect and the healthcare costs are irrationally much higher than in any other country. 4) We can see a large economic incentive in the development of excesses under COVID syndemic. Conclusions The harm of the COVID virus - as well as the benefits of the mandatory, emergency actions during the pandemic - were grossly over-estimated. Professional, unbiased (non-religious, non-political) and independent reviews are strongly recommended.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

With Internet of Things (IoT) gaining presence throughout different industries a lot of new technologies have been introduced to support this undertaking. Implications on one such technology, wireless systems allowed for the use of different communication methods to achieve the goal of transferring data reliably, with more cost efficiency and over longer distances. Anywhere from a single house with only a few IoT devices such as a smart light bulb or a smart thermostat connected to the network, all the way to a complex system that can control power grids throughout countries, IoT has been becoming a necessity in everyday lives. This paper presents an overview of the devices, systems and wireless technologies used in different IoT architectures (Healthcare, Vehicular Networks, Mining, Learning, Energy, Smart Cities, Behaviors and Decision Making), their upbringings and challenges to this date and some foreseen in the future.

One in 12 babies is born with a rare genetic disease. Sadly, most cases are undetected until later age, missing time for early treatment and opportunity to prevent complications. Humanity has entered a new era where Big Data collected by governments, including 2D and 3D facial scans, are available. Many rare genetic diseases can be identified by artificial intelligence (AI) analysis of the facial photo. Phenotyping AI utilizations facilitate comprehensive and accurate genetic evaluations. AI processing of this Big Data to identify rare genetic diseases could bring unimaginable benefits to healthcare, although this would be a questionable step in terms of citizen privacy and could lead to future "Orwellian" ramifications with government abuse. Going forward, a balance must be found between protecting the privacy of citizens and the enticing use of AI for their health risks and cost savings through prevention. The unimaginable potential of AI early diagnostics from facial photos also raises various ethical and legal concerns. This paper presents concept, protentional methods and legal and other limitations within EU legal framework in contrast with potential benefits. This paper is focused on AI utilization to early diagnostic of rare genetic diseases. Shift of paradigm in the screening for rare genetic diseases in population with AI face analysis is expected to have a significant impact. The potential of AI algorithms utilizations similar to face2gene app in general population or systematically on Big governmental datasets recording facial traits changes in time can have significant impact on public health but at the same time give raise to profound concern as violation of one’s privacy.

Aims: To compare traditional versus dry wipes hygiene in hospitalized, bedridden patients with regard to economic and environmental impact and patient and professional satisfaction. Background: Evidence shows that dry hygiene with disposable wipes can reduce the risk of infection compared to traditional hygiene with soap and water. However, there is a need to compare the costs and environmental impact (waste) between the two methods. Design: Environmental impact and patient and user satisfaction were assessed through an ad hoc survey and cost minimization analysis in accordance with the Consolidated Health Economic Evaluation Reporting Standards. Methods: The environmental impact and costs associated with traditional versus dry hygiene were compared in an observational analysis. A single-center, randomized controlled trial was performed in a tertiary hospital to assess patient and professional satisfaction in patients with a urinary catheter recovering from heart surgery between January 2019 and February 2020. Results: Direct hygiene costs were similar using traditional (€15.82) and dry (€14.35) methods. However, the analysis of indirect costs, including those related to the probability of contracting catheter-associated urinary tract infections, showed that the total cost per hygiene session was €1345 higher with the traditional method. When using a high-efficiency energy system from a guaranteed renewable source, dry hygiene does not lead to a higher environmental impact (220.07 g CO2 traditional vs. 215.17g CO2 dry). However, emissions from conventional energy systems are 840.14 g CO2 higher when using traditional hygiene. Both patients and professionals express greater satisfaction with the dry method. Conclusion: Dry hygiene is not inferior to traditional hygiene in terms of satisfaction, and it can provide environmental benefits at a significantly lower cost. Implications for the profession and/or patient care: This study provides evidence supporting the introduction of the dry hygiene method, with is associated with higher user and professional satisfaction, lower economic cost, and similar or lower environmental impact compared to soap and water.

The SARS-CoV-2 virus that causes the COVID-19 disease has wreaked havoc on the world community in terms of every imaginable parameter. The research output on COVID-19 has been nothing short of phenomenal, especially in the medical and biomedical sciences, where the search for a potential vaccine is being conducted in earnest. Much of the advanced research has been distributed in the leading medical journals, including the Journal of the American Medical Association (JAMA), where the latest research is distributed on a daily basis. The purpose of this paper is to provide some perspectives on 44 interesting and highly topical research papers that have been published in JAMA, at the time of writing, within the past two weeks. The diverse topics include public health, general medicine, internal medicine, oncology, paediatrics, geriatrics, and biostatistics.