Recent Advancements in Emerging Technologies for Healthcare Management Systems: A Survey

Report on the Development of the Accreditation Council for Education in Nutrition and Dietetics' Academic "Accreditation Standards for Advanced Practice Doctoral Education in Nutrition and Dietetics"

The dietetics profession is facing a shortage of registered dietitian nutritionists (RDNs) with a terminal degree. The need for doctoral-prepared RDNs was augmented with the entry-level RDN requiring a graduate degree and exacerbated by the number of retirees from the baby-boomer generation. Advanced practice doctoral (APD) programs can assist in meeting the increased need for doctoral-prepared RDNs. The Accreditation Council for Education in Nutrition and Dietetics Board established an Expanded Standards Committee to develop the APD academic accreditation standards, which included creating and validating doctoral-level competencies in dietetics practice. The development began with a review of the literature, including a review of advanced practice standards for nondietetic, health-related professional programs and professional-doctorate accreditation standards, and focus groups to investigate the perception and need. A rigorous, iterative, Delphi research process was used to develop the academic standards, competencies, and respective performance indicators. The iterative approach resulted in 8 validated standards with 14 competencies with 34 performance indicators for the academic accreditation APD standards. The APD standards define an advanced practice professional doctorate curriculum that is distinct from the entry-level graduate degree in clinical nutrition programs, as well as the research PhD, as it incorporates advanced didactic coursework, advanced practice residency, and applied practice-based research to achieve specific practice-based competencies. Academic accreditation at the doctoral level ensures quality programs that are educating RDNs who are competent at the advanced-practice level, enhancing not only professional practice, but also advancing research supporting practice, education, and leadership.

Garcia Adaptive Equilibrium Theory (GAET): A New Nursing Theory for Mental Health Nursing

Mental health nursing demands a dynamic, patient-centered approach that goes beyond crisis intervention and symptom management. Traditional models like the Roy Adaptation Model and Tidal Model emphasize adaptation and recovery but fall short in capturing the fluid, nonlinear nature of mental health. The Garcia Adaptive Equilibrium Theory (GAET) introduces a new framework viewing mental health as a continuous balancing process. In this model, nurses serve as Equilibrium Facilitators, identifying points of instability and intervening early to prevent crises.Central to GAET is the Equilibrium Spectrum, which conceptualizes mental health as a fluctuating continuum-from stability to severe distress. Unlike traditional psychiatric approaches focused on reactive treatment, GAET incorporates Equilibrium Forecasting, a proactive assessment strategy that integrates subjective tools (scales, narratives) and objective data (biomarkers, physiologic signs) to anticipate deterioration.By reframing mental health care around real-time stabilization, GAET promotes early intervention, sustained well-being, and a shift toward preventive, holistic care. This theory redefines psychiatric nursing as an active, interdisciplinary practice focused on maintaining equilibrium rather than responding to disruption-empowering nurses to lead in mental health stabilization and prevention.

Embracing a Penta helix hub framework for co-creating sustaining and potentially disruptive sterilization innovation that enables artificial intelligence and sustainability: A scoping review

The supply of safe pipeline medical devices is of paramount importance. Opportunities exist to transform reusable medical devices for improved processing that meets diverse patient needs. There is increased interest in multi-actor hub frameworks to meet innovation challenges globally. The purpose of this scoping paper was to identify critical decontamination and sterilization needs for the medtech and pharmaceutical sectors with a focus on understanding how to effectively use the Penta helix hub framework that combines academia, industry, healthcare, policy-makers/regulators and patients/society. A PRISMA scoping review of PubMed publications was conducted over the period 2010 to January 2025. Thirty of the 124 'helix hub' papers addressed innovation where only 3 of 16 healthcare-focused helices used or mentioned the need for key performance indicators (KPIs). Early-phase helix innovation ecosystems are mainly supported by qualitative or non-empirical data. This review explores multi-actor needs along with describing quantifiable KPIs at micro (end-user), meso (innovation hub) and macro (regional, national and international) levels. This integrated Penta hub approach will help to effectively plan, co-create, manage, analyse and utilize voluminous data, for example there are ca. 60,000 and 56,000 publications per year on artificial intelligence (AI) and medical devices respectively along, with some 35,000 adverse reports on devices submitted to the US FDA. This review addresses sustaining and potentially disruptive opportunities for decontamination and sterilization that includes the use of AI-enabled devices, bespoke training and sustainability.

Prevalence and associated factors of caesarean section delivery: analysis from the Nepal Demographic and Health Survey 2022

OBJECTIVE

Ensuring equitable access to emergency obstetric care is essential for reducing maternal mortality. This study examines the prevalence and associated factors of caesarean section (CS) delivery in Nepal during 2022.

DESIGN

This study used secondary data from the 2022 Nepal Demographic and Health Survey (NDHS) for the analysis, employing stratified two-stage cluster sampling. The sample comprised 1977 live births from women aged 15-49 years, with CS delivery serving as the outcome variable. Independent variables were categorised into residency, socio-economic, maternal health service and maternal factors. Binary logistic regression was applied to estimate crude and adjusted odds ratios (AORs) for associations, with statistical significance assessed at p<0.05. A complex sample analysis was performed to account for the stratified survey design. : Setting : Nepal. PARTICIPANTS: This study included 1977 live births from mothers who delivered within the 2 years preceding the survey, representing women aged 15-49. : Outcome variable : CS delivery.

RESULTS

The study analysed factors associated with CS delivery in Nepal using NDHS 2022 data. Rural residence (AOR: 0.581; p<0.001) and regional disparities, particularly in Terai (AOR: 2.651; p<0.05), significantly influenced CS delivery rates. Higher maternal education (AOR: 3.207; p<0.01) and wealth index (richest quintile AOR: 6.729; p<0.001) were associated with increased odds of CS delivery. Delivery in private institutions (AOR: 5.862; p<0.001) and maternal age (35-49 years AOR: 6.151; p<0.001) showed strong associations, while higher birth orders reduced the probability of CS.

CONCLUSION

The factors influencing CS rates in Nepal include socio-economic status, maternal education, geographical region and access to maternal health services. Regional disparities and the rising prevalence of CS underscore the necessity for equitable healthcare resource allocation and the implementation of need-based approaches and clear, evidence-based guidelines to ensure the appropriate use of CS for improving maternal and child health outcomes.

Digital Health Policy and Cybersecurity Regulations Regarding Artificial Intelligence (AI) Implementation in Healthcare

The landscape of healthcare is rapidly changing with the increasing usage of machine and deep learning artificial intelligence and digital tools to assist in various sectors. This study aims to analyze the feasibility of the implementation of artificial intelligence (AI) models into healthcare systems. This review included English-language publications from databases such as SCOPUS, PubMed, and Google Scholar between 2000 and 2024. AI integration in healthcare systems will assist in large-scale dataset analysis, access to healthcare information, surgery data and simulation, and clinical decision-making in addition to many other healthcare services. However, with the reliance on AI, issues regarding medical liability, cybersecurity, and health disparities can form. This necessitates updates and transparency on health policy, AI training, and cybersecurity measures. To support the implementation of AI in healthcare, transparency regarding AI algorithm training and analytical approaches is key to allowing physicians to trust and make informed decisions about the applicability of AI results. Transparency will also allow healthcare systems to adapt appropriately, provide AI services, and create viable security measures. Furthermore, the increased diversity of data used in AI algorithm training will allow for greater generalizability of AI solutions in patient care. With the growth of AI usage and interaction with patient data, security measures and safeguards, such as system monitoring and cybersecurity training, should take precedence. Stricter digital policy and data protection guidelines will add additional layers of security for patient data. This collaboration will further bolster security measures amongst different regions and healthcare systems in addition to providing more means to innovative care. With the growing digitization of healthcare, advancing cybersecurity will allow effective and safe implementation of AI and other digital systems into healthcare and can improve the safety of patients and their personal health information.

Healthcare Practitioners' Perceptions of mHealth Application Barriers: Challenges to Adoption and Strategies for Enhancing Digital Health Integration

BACKGROUND

Mobile health (mHealth) applications have transformed healthcare delivery by enhancing accessibility, patient monitoring, and clinician communication. Despite these advantages, significant barriers hinder their adoption among healthcare practitioners, limiting their effectiveness in primary care settings. Understanding these barriers is crucial for optimizing mHealth integration into healthcare systems.

AIM

This study examines healthcare practitioners' perceptions of barriers to mHealth application adoption, with a focus on the Sehaty app (version 1.3) in Saudi Arabia. It aims to identify key challenges, assess their impact on user engagement and system efficiency, and provide insights for enhancing digital health implementation.

METHODS

A cross-sectional survey was conducted among 409 primary healthcare practitioners using the Sehaty app. The study employed a structured questionnaire assessing ten major barriers to mHealth adoption, including technical, usability, privacy, and integration challenges. Descriptive statistics, ANOVA, t-tests, and correlation analyses were performed to examine differences across demographic groups and relationships among identified barriers.

RESULTS

Findings revealed that technical and usability challenges were the most significant barriers, with system compatibility (Mean = 3.64), slow performance (Mean = 3.43), and excessive task complexity (Mean = 3.45) among the most cited issues. Training and support limitations (Mean = 3.28) and workflow integration difficulties (Mean = 3.24) further hindered adoption. Correlation analysis indicated weak interdependencies among barriers, suggesting that targeted interventions addressing specific concerns may be more effective. ANOVA results showed that digital literacy significantly influenced perceptions of communication barriers (p = 0.046), while gender differences in usability and productivity constraints were marginally significant.

CONCLUSIONS

The study underscores the necessity for improved system interoperability, user-centered design, and enhanced technical support to promote mHealth adoption. Addressing these challenges through strategic policy initiatives and infrastructure improvements is essential for fostering a more integrated and effective digital healthcare ecosystem.

Digital Toxicology Teleconsultation for Adult Poisoning Cases in Saudi Hospitals: A Nationwide Study

Background/Objectives: Poisoning represents a significant global public health challenge, particularly with its complex manifestations in adult populations. Understanding regional epidemiology through digital health systems is crucial for developing evidence-based prevention and management strategies. This nationwide study analyzes hospital-based toxicology teleconsultation data from the Toxicology Consultation Service-Saudi Medical Appointments and Referrals Center (TCS-SMARC) platform to characterize the epidemiological patterns, clinical features, and outcomes of adult poisoning cases across Saudi regions. Methods: We conducted a retrospective cross-sectional analysis of 6427 adult poisoning cases where hospitals sought teleconsultation from the Saudi Toxicology Consultation Service (TCS) from January to December 2023. Descriptive statistics were used to analyze poisoning rates by demographic characteristics, agents responsible for the poisoning, clinical presentations, and management decisions. Population-adjusted rates were calculated using the national census data. Associations between variables were analyzed using cross-tabulations and chi-square tests. Results: Young adults aged 18-35 years constituted most cases (58.67%), with the highest population-adjusted rates observed among those aged 18-24 (5.15 per 10,000). Medicine-related poisonings were the most common across all regions (50.04%), followed by bites and stings (15.31%). Regional analysis indicated relatively uniform poisoning rates across Business Units (BUs) (2.02-2.74 per 10,000). Most cases (87.44%) were asymptomatic, with 91.71% exhibiting normal Glasgow Coma Scale scores, although substance abuse cases had higher rate of severe manifestations (24.34%). Significant seasonal variations were observed (p < 0.001), with peak incidents occurring in the summer (29.25%). Management decisions primarily involved hospital observation (40.27%) and admission (30.34%), with agent-specific variations in care requirements (p < 0.001). Conclusions: This comprehensive analysis demonstrates the effectiveness of Saudi Arabia's digital health infrastructure in capturing and managing nationwide poisoning data. The integrated digital platform enables real-time surveillance, standardized triage, enhanced access to specialized toxicology services, and coordinated management across diverse geographical contexts. Our findings inform evidence-based recommendations for targeted prevention strategies, particularly for young adults and medicine-related poisonings, while establishing a scalable model for digital health-enabled poisoning management.

Integrating generative adversarial networks with IoT for adaptive AI-powered personalized elderly care in smart homes

The need for effective and personalized in-home solutions will continue to rise with the world population of elderly individuals expected to surpass 1.6 billion by the year 2050. The study presents a system that merges Generative Adversarial Network (GAN) with IoT-enabled adaptive artificial intelligence (AI) framework for transforming personalized elderly care within the smart home environment. The reason for the application of GANs is to generate synthetic health data, which in turn addresses the scarcity of data, especially of some rare but critical conditions, and helps enhance the predictive accuracy of the system. Continuous data collection from IoT sensors, including wearable sensors (e.g., heart rate monitors, pulse oximeters) and environmental sensors (e.g., temperature, humidity, and gas detectors), enables the system to track vital indications of health, activities, and environment for early warnings and personalized suggestions through real-time analysis. The AI adapts to the unique pattern of healthy and behavioral habits in every individual's lifestyle, hence offering personalized prompts, reminders, and sends off emergency alert notifications to the caregiver or health provider, when required. We were showing significant improvements like 30% faster detection of risk conditions in a large-scale real-world test setup, and 25% faster response times compared with other solutions. GANs applied to the synthesis of data enable more robust and accurate predictive models, ensuring privacy with the generation of realistic yet anonymized health profiles. The system merges state-of-the-art AI with GAN technology in advancing elderly care in a proactive, dignified, secure environment that allows improved quality of life and greater independence for the aging individual. The work hence provides a novel framework for the utilization of GAN in personalized healthcare and points out that this will help reshape elderly care in IoT-enabled "smart" homes.

The Role of Dynamic Computed Tomography Angiography in Endoleak Detection and Classification After Endovascular Aneurysm Repair: A Comprehensive Review

Backgroud: The use of dynamic computed tomography angiography (dCTA) for the detection of endoleaks in patients who underwent endovascular repair of abdominal aortic aneurysms is gaining interest. This study aims to provide an overview of the current applications of dCTA technologies in vascular surgery. Methods: We performed a comprehensive review by searching in the PubMed database and Cochrane Library (last search: 1 November 2024). We included studies considering endoleak investigation after endovascular aneurysm repair (EVAR). We included papers that reported the outcome of applications of dCTA, excluding case reports or very limited case series (≤4). Finally, 14 studies regarding 377 computed tomography angiographies (CTA) were included and evaluated. Results: Persistent perfusion of the aneurysm sac is the most common complication after EVAR. Imaging-based surveillance post-EVAR is essential with the aim of early detection, characterization, and localization of endoleaks to guide therapeutic intervention or follow-up. dCTA detected 36 type I endoleaks versus 16 identified with standard CTA and 138 versus 95 type II endoleaks. Conclusions: The emergence of dCTA offers a promising solution through enhanced temporal resolution, allowing the visualization of real-time flow dynamics within the aneurysmal sac essential to establishing endoleak treatment or post-EVAR follow-up.

Quantum Computing in the Realm of Neurosurgery

Quantum computing leverages the principles of quantum mechanics to provide unprecedented computational power by processing data in a fundamentally different way from classical binary computers. Quantum computers use "qubits" which superimpose 0 and 1. Because qubits can exist in multiple states at the same time, quantum computers can perform "quantum parallelism" wherein data are processed simultaneously rather than sequentially. The quantum parallelism is what enables the computer to have exponentially larger processing capabilities and consider all potential outcomes simultaneously to derive solutions. Our study aims to explore aspects of neurosurgery through which quantum computing could improve patient outcomes and enhance quality of care. Quantum computing has the potential for future applications in neuroprosthetics, neurostimulation, surgical precision, diagnosis, and patient privacy and security. It promises improved patient outcomes, enhanced surgical precision, and personalized healthcare delivery. With its inherent sensitivity and precision, quantum computing could advance the understanding of disease processes and development, providing neurosurgeons with deeper insight into patient pathologies. Challenges such as biocompatibility, cost, and ethical considerations remain significant barriers to integrating the technology into neurosurgical practice. Addressing these challenges will be crucial for realizing the transformative potential of quantum computing in advancing neurosurgical care and improving clinical outcomes.

An Evaluation of Opportunities and Challenges of Blockchain Technology in Healthcare

In a globalised world, patients may need to manage and track their health records with healthcare professionals anywhere and anytime with high data privacy and security. Blockchain technology (BcT) provides these necessities. This study aims to provide a general overview of the usability of BcT in healthcare applications. BcT has been adopted in many sectors, including healthcare. However, there is no unified record format among hospitals for tracking patients' medical histories. In BcT, recorded data cannot be modified without affecting the chain hash code. The blockchain creates a secure and public digital ledger. BcT can also ensure accurate and timely financial transactions by automating payment processes among providers, insurers, and patients through smart contracts. In conclusion, BcT plays a key role in the healthcare sector. It enables safer, cheaper, more dependable, and more effective pharmacotherapy by providing data security, traceability, and immutability.

Practical Recommendations for Navigating Digital Tools in Hospitals: Qualitative Interview Study

BACKGROUND

The digitalization of health care organizations is an integral part of a clinician's daily life, making it vital for health care professionals (HCPs) to understand and effectively use digital tools in hospital settings. However, clinicians often express a lack of preparedness for their digital work environments. Particularly, new clinical end users, encompassing medical and nursing students, seasoned professionals transitioning to new health care environments, and experienced practitioners encountering new health care technologies, face critically intense learning periods, often with a lack of adequate time for learning digital tools, resulting in difficulties in integrating and adopting these digital tools into clinical practice.

OBJECTIVE

This study aims to comprehensively collect advice from experienced HCPs in Switzerland to guide new clinical end users on how to initiate their engagement with health ITs within hospital settings.

METHODS

We conducted qualitative interviews with 52 HCPs across Switzerland, representing 24 medical specialties from 14 hospitals. The interviews were transcribed verbatim and analyzed through inductive thematic analysis. Codes were developed iteratively, and themes and aggregated dimensions were refined through collaborative discussions.

RESULTS

Ten themes emerged from the interview data, namely (1) digital tool understanding, (2) peer-based learning strategies, (3) experimental learning approaches, (4) knowledge exchange and support, (5) training approaches, (6) proactive innovation, (7) an adaptive technology mindset, (8) critical thinking approaches, (9) dealing with emotions, and (10) empathy and human factors. Consequently, we devised 10 recommendations with specific advice to new clinical end users on how to approach new health care technologies, encompassing the following: take time to get to know and understand the tools you are working with; proactively ask experienced colleagues; simply try it out and practice; know where to get help and information; take sufficient training; embrace curiosity and pursue innovation; maintain an open and adaptable mindset; keep thinking critically and use your knowledge base; overcome your fears, and never lose the human and patient focus.

CONCLUSIONS

Our study emphasized the importance of comprehensive training and learning approaches for health care technologies based on the advice and recommendations of experienced HCPs based in Swiss hospitals. Moreover, these recommendations have implications for medical educators and clinical instructors, providing advice on effective methods to instruct and support new end users, enabling them to use novel technologies proficiently. Therefore, we advocate for new clinical end users, health care institutions and clinical instructors, academic institutions and medical educators, and regulatory bodies to prioritize effective training and cultivating technological readiness to optimize IT use in health care.

Nurses' and midwives' perception of the leadership skills and attributes required of future leaders

AIM

Identify the skills and knowledge future nurse and midwife leaders might require in the next 6 years. Design/methodology/approach: An online questionnaire elicited health professionals' perspectives on the future requirements for nurse and midwife leaders. Qualitative data were generated in response on health care and the likely leadership skills for the future. Data were extracted and analysed using qualitative content analysis.

FINDINGS

Four generic categories were abstracted from the core category 'Nursing and Midwifery Leadership'. These were values/traits; creating positive healthcare cultures; digital capability/competence; and systems thinking. Limitations/implications. This first stage evaluation has gained a wide variety of perspectives regarding the perceived skills and knowledge future nurse and midwife leaders might need. This is important to enable those who deliver leadership development programmes to plan appropriately, ensuring their programmes are designed and adjusted in response to the needs of a shifting health and care landscape. However, over 50% of respondents were White, so the data may not be representative of the diversity of registered nurses and midwives. The findings may not have direct relevance to the global context due to geographical limitations.

Latest advancements and prospects in the next-generation of Internet of Things technologies

The Internet of Things (IoT) is a sophisticated network of objects embedded with electronic systems that enable devices to collect and exchange data. IoT is a recent trending leading technology and changing the way we live. However, it has several challenges especially efficiency, architecture, complexity, and network topology. The traditional technologies are not enough to provide support. It is evident from the literature that complex networks are used to study the topology and the structure of a network and are applied to modern technologies. Thus, the capability of powerful computational tools and the existence of theoretical frameworks enable complex networks to derive new approaches in analyzing IoT-based technologies in terms of improving efficiency, architecture, complexity, and topology. In this direction, limited research has been carried out. The integration aspect remains a key challenge. Therefore, in order to fill this gap. Herein, we design a comprehensive literature review. In this research effort, we explore a newly leading emerging technology named the Social Internet of Things (SIoT). It is developed to overcome the challenges in IoT. We discuss the importance and the key applications of SIoT. We first presented a conceptual view along with a recent technological roadmap. The big data play an important role in the modern world. We discuss big data and the 5 Vs along with suitable applications and examples. Then, we highlighted the key concepts in complex networks, scale-free, random networks, and small-world networks. We explored and presented various graph models and metrics aligned with social networks and the most recent trends. The novelty of this research is to propose a synergy of complex networks to the IoT, SIoT, and big data together. We discuss the advantages of integration in detail. We present a detailed discussion on complex networks emerging technologies and cyber-physical systems (CPS). Briefly, our literature review covers the most recent advancements and developments in 10 years. In addition, our critical analysis is based on up-to-date surveys and case studies. Finally, we outline the impact of recent emerging technologies on challenges applications, and solutions for the future. This paper provides a good reference for researchers and readers in the IoT domain.

Utilizing Blockchain Technology for Healthcare and Biomedical Research: A Review

Blockchain is a decentralized, secure, and immutable public ledger that offers significant benefits over conventional centralized systems by preventing data breaches and cyber-attacks. It has a great potential to improve data security, privacy, and interoperability in healthcare and biomedical research. This review discusses the basic principles and the historical evolution of blockchain and evaluates the implications of blockchain for the existing healthcare infrastructure. It also highlights blockchain technology's advantages in electronic health records, supply chain management, clinical trials, and telemedicine. However, this technology faces several hurdles, including regulatory issues, technical complexity, and economic costs, which suggest a gradual adoption over time. In addition, the review emphasizes its ability to ensure data integrity, enhance collaboration, and protect intellectual property in biomedical research. This review shows that blockchain can enhance healthcare data management by providing secure, efficient, and patient-centric solutions. Furthermore, it also discusses the implications of blockchain for the future of healthcare and biomedical research and suggests that ongoing research and interdisciplinary approaches are essential for overcoming current barriers and realizing the full potential of this technology. Future research should focus on developing privacy-preserving hybrid data storage solutions that comply with international laws and regulations, thus enhancing the sustainability and scalability of this technology in healthcare.

Revolutionizing healthcare and medicine: The impact of modern technologies for a healthier future-A comprehensive review

The increasing integration of new technologies is driving a fundamental revolution in the healthcare sector. Developments in artificial intelligence (AI), machine learning, and big data analytics have completely transformed the diagnosis, treatment, and care of patients. AI-powered solutions are enhancing the efficiency and accuracy of healthcare delivery by demonstrating exceptional skills in personalized medicine, early disease detection, and predictive analytics. Furthermore, telemedicine and remote patient monitoring systems have overcome geographical constraints, offering easy and accessible healthcare services, particularly in underserved areas. Wearable technology, the Internet of Medical Things, and sensor technologies have empowered individuals to take an active role in tracking and managing their health. These devices facilitate real-time data collection, enabling preventive and personalized care. Additionally, the development of 3D printing technology has revolutionized the medical field by enabling the production of customized prosthetics, implants, and anatomical models, significantly impacting surgical planning and treatment strategies. Accepting these advancements holds the potential to create a more patient-centered, efficient healthcare system that emphasizes individualized care, preventive care, and better overall health outcomes. This review's novelty lies in exploring how these technologies are radically transforming the healthcare industry, paving the way for a more personalized and effective healthcare for all. It highlights the capacity of modern technology to revolutionize healthcare delivery by addressing long-standing challenges and improving health outcomes. Although the approval and use of digital technology and advanced data analysis face scientific and regulatory obstacles, they have the potential for transforming translational research. as these technologies continue to evolve, they are poised to significantly alter the healthcare environment, offering a more sustainable, efficient, and accessible healthcare ecosystem for future generations. Innovation across multiple fronts will shape the future of advanced healthcare technology, revolutionizing the provision of healthcare, enhancing patient outcomes, and equipping both patients and healthcare professionals with the tools to make better decisions and receive personalized treatment. As these technologies continue to develop and become integrated into standard healthcare practices, the future of healthcare will probably be more accessible, effective, and efficient than ever before.

Examining attitudes to uptake and sustainment of technology among care aides in the delivery of care: a scoping review protocol

INTRODUCTION

Care aides are health workers who deliver hands-on care to patients across the healthcare continuum. The use of technology in healthcare delivery is increasing, and evidence regarding how care aides' attitudes may either facilitate or hinder the adoption of healthcare technologies is lacking.The aim of the proposed scoping review is to examine available evidence regarding care aides' attitudes towards the adoption of innovation and factors that may influence the sustainable use of technology in healthcare delivery. Published studies, grey literature and review articles that identify a method for the review, conference abstracts and website publications regarding the attitude, uptake and sustainable use of technology in care delivery by care aides will be included. For abstracts that have resulted in publications, the full publications will be included. The search for evidence commenced in June 2023 and will end in March 2024.

METHODS AND ANALYSIS

The Joanna Briggs Institute (JBI) method will be used to conduct the review. The CINAHL, Cochrane Library, EMBASE, MEDLINE, ProQuest, PubMed, SCOPUS, PROSPERO, Web of Science and JBI Evidence Synthesis databases will be searched using keywords for publications within the last 20 years to examine trends in health technology and attitudes of care aides towards innovation over the last two decades. A search of grey literature and websites will be conducted. The reference list of the retrieved articles will be used to identify additional literature. The search results will be exported into a literature management tool for screening and analysis. Article screening will be performed by two authors and if a third is needed to resolve any differences. Data analysis will be guided by two theoretical frameworks.

ETHICS AND DISSEMINATION

No ethics approval is required. The findings will be disseminated in a peer-reviewed journal and presented in conferences.

REGISTRATION DETAILS

https://doi.org/10.17605/OSF.IO/CZQUP.

Unlocking insights: Navigating COVID-19 challenges and Emulating future pandemic Resilience strategies with strengthening natural immunity

The original COVID-19 vaccines, developed against SARS-CoV-2, initially mitigated hospitalizations. Bivalent vaccine boosters were used widely during 2022-23, but the outbreaks persisted. Despite this, hospitalizations, mortality, and outbreaks involving dominant mutants like Alpha and Delta increased during winters when the population's vitamin D levels were at their lowest. Notably, 75 % of human immune cell/system functions, including post-vaccination adaptive immunity, rely on adequate circulatory vitamin D levels. Consequently, hypovitaminosis compromises innate and adaptive immune responses, heightening susceptibility to infections and complications. COVID-19 vaccines primarily target SARS-CoV-2 Spike proteins, thus offering only a limited protection through antibodies. mRNA vaccines, such as those for COVID-19, fail to generate secretory/mucosal immunity-like IgG responses, rendering them ineffective in halting viral spread. Additionally, mutations in the SARS-CoV-2 binding domain reduce immune recognition by vaccine-derived antibodies, leading to immune evasion by mutant viruses like Omicron variants. Meanwhile, the repeated administration of bivalent boosters intended to enhance efficacy resulted in the immunoparesis of recipients. As a result, relying solely on vaccines for outbreak prevention, it became less effective. Dominant variants exhibit increased affinity to angiotensin-converting enzyme receptor-2, enhancing infectivity but reducing virulence. Meanwhile, spike protein-related viral mutations do not impact the potency of widely available, repurposed early therapies, like vitamin D and ivermectin. With the re-emergence of COVID-19 and impending coronaviral pandemics, regulators and health organizations should proactively consider approval and strategic use of cost-effective adjunct therapies mentioned above to counter the loss of vaccine efficacy against emerging variants and novel coronaviruses and eliminate vaccine- and anti-viral agents-related serious adverse effects. Timely implementation of these strategies could reduce morbidity, mortality, and healthcare costs and provide a rational approach to address future epidemics and pandemics. This perspective critically reviews relevant literature, providing insights, justifications, and viewpoints into how the scientific community and health authorities can leverage this knowledge cost-effectively.

Advances in artificial intelligence for drug delivery and development: A comprehensive review

Artificial intelligence (AI) has emerged as a powerful tool to revolutionize the healthcare sector, including drug delivery and development. This review explores the current and future applications of AI in the pharmaceutical industry, focusing on drug delivery and development. It covers various aspects such as smart drug delivery networks, sensors, drug repurposing, statistical modeling, and simulation of biotechnological and biological systems. The integration of AI with nanotechnologies and nanomedicines is also examined. AI offers significant advancements in drug discovery by efficiently identifying compounds, validating drug targets, streamlining drug structures, and prioritizing response templates. Techniques like data mining, multitask learning, and high-throughput screening contribute to better drug discovery and development innovations. The review discusses AI applications in drug formulation and delivery, clinical trials, drug safety, and pharmacovigilance. It addresses regulatory considerations and challenges associated with AI in pharmaceuticals, including privacy, data security, and interpretability of AI models. The review concludes with future perspectives, highlighting emerging trends, addressing limitations and biases in AI models, and emphasizing the importance of collaboration and knowledge sharing. It provides a comprehensive overview of AI's potential to transform the pharmaceutical industry and improve patient care while identifying further research and development areas.

Revolutionizing Gastrointestinal Disorder Management: Cutting-Edge Advances and Future Prospects

In recent years, remarkable strides have been made in the management of gastrointestinal disorders, transforming the landscape of patient care and outcomes. This article explores the latest breakthroughs in the field, encompassing innovative diagnostic techniques, personalized treatment approaches, and novel therapeutic interventions. Additionally, this article emphasizes the use of precision medicine tailored to individual genetic and microbiome profiles, and the application of artificial intelligence in disease prediction and monitoring. This review highlights the dynamic progress in managing conditions such as inflammatory bowel disease, gastroesophageal reflux disease, irritable bowel syndrome, and gastrointestinal cancers. By delving into these advancements, we offer a glimpse into the promising future of gastroenterology, where multidisciplinary collaborations and cutting-edge technologies converge to provide more effective, patient-centric solutions for individuals grappling with gastrointestinal disorders.

A Systematic Review and Bayesian Network Meta-Analysis Comparing In-Person, Remote, and Blended Interventions in Physical Activity, Diet, Education, and Behavioral Modification on Gestational Weight Gain among Overweight or Obese Pregnant Individuals

BACKGROUND

Despite the well-documented adverse outcomes associated with obesity during pregnancy, this condition remains a promising modifiable risk factor.

OBJECTIVES

The aim of this study was to ascertain the most effective treatment modalities for gestational weight gain (GWG) in pregnant women classified as overweight or obese.

METHODS

A systematic search was conducted across 4 electronic databases: Embase, EBSCOhost, PubMed, and Web of Science. To assess the quality of evidence, the Confidence In Network Meta-Analysis (CINeMA) approach, grounded in the Grading of Recommendations Assessment, Development, and Evaluation framework, was employed. A Bayesian network meta-analysis was conducted to synthesize the comparative effectiveness of treatment modalities based on GWG outcomes.

RESULTS

The analysis incorporated 60 randomized controlled trials, encompassing 16,615 participants. Modes of intervention administration were classified as remote (R: eHealth [e] and mHealth [m]), in-person (I), and a combination of both (I+R). The interventions comprised 5 categories: education (E), physical activity (PA), dietary (D), behavior modification (B), and combinations thereof. The quality of the evidence, as evaluated by CINeMA, ranged from very low to high. Compared to the control group, the I-D intervention (mean difference [MD]: -1.27; 95% confidence interval [CI]: -2.23, -0.32), I-PADB (MD: -0.60, 95% CI: -1.19, -0.00), and I-B (MD: -0.34, 95% CI: -0.57, -0.10) interventions showed significant efficacy in reducing GWG.

CONCLUSIONS

Preliminary findings suggest that the I-D intervention is the most efficacious in managing GWG among pregnant women who are overweight or obese, followed by I-PADB and I-B+R-B(m) treatments. These conclusions are drawn from evidence of limited quality and directness, including insufficient data on PA components used in the interventions. Owing to the absence of robust, direct evidence delineating significant differences among various GWG management strategies, it is tentatively proposed that the I-D intervention is likely the most effective approach. This study was registered with PROSPERO as CRD42023473627.

Health disparity among older adults in urban China: The role of local fiscal conditions

This study explores the disparities in older adults' self-rated health within the urban landscape of China. Drawing on the 1% national population survey of China in 2015, it highlights how variations in city development contribute to geographical health disparities among older residents. In the era of the decentralized fiscal system, a crucial mechanism identified is the role of cities' local fiscal revenue in connecting their socioeconomic development and the health status among older adults. Despite efforts by cities in lower socioeconomic positions to increase fiscal expenditure and address deficits through central transfer payments, they prove inadequate in effectively mitigating population health disparities. The prioritization of economic growth and neglect of public service provision responsibilities are fundamental causes within this fiscal framework. The findings underscore the urgent need for increased central transfer payments in public services to address the growing disparities in older adults' health.

Putting the pieces together: towards an integrative framework for healthcare performance

PURPOSE

In the past few decades, performance measuring systems have become important managerial tools for healthcare organizations. Healthcare performance metrics are a useful tool in understanding how healthcare organizations achieve their goals while satisfying the needs of their patients and conforming to national and international standards. Various efforts have been made to assess healthcare performance. Most of these measures are focused on a single perspective or developed by a single source to meet management and strategic objectives on time.

DESIGN/METHODOLOGY/APPROACH

We develop a review of the literature to shed light on the measures used to assess performance in the healthcare sector at various points in time, as well as to establish a thorough understanding of healthcare performance measurement.

FINDINGS

Developing real-time digital traceability of metrics and an integrative perspective that increases the actionability of information acquired is an attractive potential made possible by the introduction of new technologies and the digitization of data.

ORIGINALITY/VALUE

We conclude that a proper measurement system should be one to combine patient, physician, non-medical staff and system perspective, which will further facilitate the assessment of healthcare performance and the comparative function.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

Aguhyper: a hyperledger-based electronic health record management framework

The increasing importance of healthcare records, particularly given the emergence of new diseases, emphasizes the need for secure electronic storage and dissemination. With these records dispersed across diverse healthcare entities, their physical maintenance proves to be excessively time-consuming. The prevalent management of electronic healthcare records (EHRs) presents inherent security vulnerabilities, including susceptibility to attacks and potential breaches orchestrated by malicious actors. To tackle these challenges, this article introduces AguHyper, a secure storage and sharing solution for EHRs built on a permissioned blockchain framework. AguHyper utilizes Hyperledger Fabric and the InterPlanetary Distributed File System (IPFS). Hyperledger Fabric establishes the blockchain network, while IPFS manages the off-chain storage of encrypted data, with hash values securely stored within the blockchain. Focusing on security, privacy, scalability, and data integrity, AguHyper's decentralized architecture eliminates single points of failure and ensures transparency for all network participants. The study develops a prototype to address gaps identified in prior research, providing insights into blockchain technology applications in healthcare. Detailed analyses of system architecture, AguHyper's implementation configurations, and performance assessments with diverse datasets are provided. The experimental setup incorporates CouchDB and the Raft consensus mechanism, enabling a thorough comparison of system performance against existing studies in terms of throughput and latency. This contributes significantly to a comprehensive evaluation of the proposed solution and offers a unique perspective on existing literature in the field.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Transformative Frontiers: A Comprehensive Review of Emerging Technologies in Modern Healthcare

The rapid evolution of emerging technologies in healthcare is reshaping the field of medical practices and patient outcomes, ushering in an era of unprecedented innovation. This narrative review touches upon the transformative impacts of various technologies, including virtual reality (VR), augmented reality (AR), the internet of medical things (IoMT), remote patient monitoring (RPM), financial technology (fintech) integration, cloud migration, and the pivotal role of machine learning (ML). It emphasizes the collaborative impact of these technologies, which is reshaping the healthcare landscape. Virtual reality and AR revolutionize medical training, IoMT extends healthcare boundaries, RPM facilitates proactive care, and fintech integration enhances financial processes. Cloud migration ensures scalable and efficient data management, while ML harnesses algorithms for diagnostic precision and personalized treatment.

Navigating the Future: The Transformative Impact of Artificial Intelligence on Hospital Management- A Comprehensive Review

This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.

Digital Health in Diabetes and Cardiovascular Disease

BACKGROUND

Digital health technologies are rapidly evolving and transforming the care of diabetes and cardiovascular disease (CVD).

PURPOSE OF THE REVIEW

In this review, we discuss emerging approaches incorporating digital health technologies to improve patient outcomes through a more continuous, accessible, proactive, and patient-centered approach. We discuss various mechanisms of potential benefit ranging from early detection to enhanced physiologic monitoring over time to helping shape important management decisions and engaging patients in their care. Furthermore, we discuss the potential for better individualization of management, which is particularly important in diseases with heterogeneous and complex manifestations, such as diabetes and cardiovascular disease. This narrative review explores ways to leverage digital health technology to better extend the reach of clinicians beyond the physical hospital and clinic spaces to address disparities in the diagnosis, treatment, and prevention of diabetes and cardiovascular disease.

CONCLUSION

We are at the early stages of the shift to digital medicine, which holds substantial promise not only to improve patient outcomes but also to lower the costs of care. The review concludes by recognizing the challenges and limitations that need to be addressed for optimal implementation and impact. We present recommendations on how to navigate these challenges as well as goals and opportunities in utilizing digital health technology in the management of diabetes and prevention of adverse cardiovascular outcomes.

Innovative Techniques for Infection Control and Surveillance in Hospital Settings and Long-Term Care Facilities: A Scoping Review

Healthcare-associated infections (HAIs) pose significant challenges in healthcare systems, with preventable surveillance playing a crucial role. Traditional surveillance, although effective, is resource-intensive. The development of new technologies, such as artificial intelligence (AI), can support traditional surveillance in analysing an increasing amount of health data or meeting patient needs. We conducted a scoping review, following the PRISMA-ScR guideline, searching for studies of new digital technologies applied to the surveillance, control, and prevention of HAIs in hospitals and LTCFs published from 2018 to 4 November 2023. The literature search yielded 1292 articles. After title/abstract screening and full-text screening, 43 articles were included. The mean study duration was 43.7 months. Surgical site infections (SSIs) were the most-investigated HAI and machine learning was the most-applied technology. Three main themes emerged from the thematic analysis: patient empowerment, workload reduction and cost reduction, and improved sensitivity and personalization. Comparative analysis between new technologies and traditional methods showed different population types, with machine learning methods examining larger populations for AI algorithm training. While digital tools show promise in HAI surveillance, especially for SSIs, challenges persist in resource distribution and interdisciplinary integration in healthcare settings, highlighting the need for ongoing development and implementation strategies.

Digitalization in orthopaedics: a narrative review

Advances in technology and digital tools like the Internet of Things (IoT), artificial intelligence (AI), and sensors are shaping the field of orthopaedic surgery on all levels, from patient care to research and facilitation of logistic processes. Especially the COVID-19 pandemic, with the associated contact restrictions was an accelerator for the development and introduction of telemedical applications and digital alternatives to classical in-person patient care. Digital applications already used in orthopaedic surgery include telemedical support, online video consultations, monitoring of patients using wearables, smart devices, surgical navigation, robotic-assisted surgery, and applications of artificial intelligence in forms of medical image processing, three-dimensional (3D)-modelling, and simulations. In addition to that immersive technologies like virtual, augmented, and mixed reality are increasingly used in training but also rehabilitative and surgical settings. Digital advances can therefore increase the accessibility, efficiency and capabilities of orthopaedic services and facilitate more data-driven, personalized patient care, strengthening the self-responsibility of patients and supporting interdisciplinary healthcare providers to offer for the optimal care for their patients.

A public value-based model to understand patients' adoption of eHealth: Theoretical underpinnings and empirical application

Objective

The main objective of this study is to develop an eHealth adoption model based on patients' perceptions of public value dimensions and empirically apply the model to understand the adoption of a governmental health app by Jordanian patients. The study attempts to contribute to overcoming the narrow focus of contemporary theories such as UTAUT and, ultimately, to designing more effective implementation strategies in order to address the current delays in global eHealth adoption.

Methods

We conducted a quantitative survey of 430 Jordanian patients, utilizing structural equation modeling (SEM) to process the empirical data. Specifically, we applied an SEM two-step approach that involved (a) evaluating the model-data fit through a review of the measurement model(s) and common method bias; and (b) analyzing the structural model.

Results

Our findings confirm that the proposed patients' value scale is valid and reliable. Its five dimensions (hedonistic motivation, utilitarian motivation, social value, ethical public value, and public trust value) significantly correlate with patients' public value, and the latter directly affects their use of eHealth apps, with habits mediating this relationship. Among the dimensions, hedonistic motivations tend to be prioritized over utilitarian ones. Ethical and trust values also play an essential role, particularly in how health technology handles patients' data and upholds their dignity and self-esteem.

Conclusions

This study highlights the holistic nature of eHealth adoption by patients and the crucial role of public values in their use behavior. It provides useful insights for policymakers and developers.

Advancements in Understanding and Classifying Chronic Orofacial Pain: Key Insights from Biopsychosocial Models and International Classifications (ICHD-3, ICD-11, ICOP)

In exploring chronic orofacial pain (COFP), this review highlights its global impact on life quality and critiques current diagnostic systems, including the ICD-11, ICOP, and ICHD-3, for their limitations in addressing COFP's complexity. Firstly, this study outlines the global burden of chronic pain and the importance of distinguishing between different pain types for effective treatment. It then delves into the specific challenges of diagnosing COFP, emphasizing the need for a more nuanced approach that incorporates the biopsychosocial model. This review critically examines existing classification systems, highlighting their limitations in fully capturing COFP's multifaceted nature. It advocates for the integration of these systems with the DSM-5's Somatic Symptom Disorder code, proposing a unified, multidisciplinary diagnostic approach. This recommendation aims to improve chronic pain coding standardization and acknowledge the complex interplay of biological, psychological, and social factors in COFP. In conclusion, here, we highlight the need for a comprehensive, universally applicable classification system for COFP. Such a system would enable accurate diagnosis, streamline treatment strategies, and enhance communication among healthcare professionals. This advancement holds potential for significant contributions to research and patient care in this challenging field, offering a broader perspective for scientists across disciplines.

Artificial intelligence and telemedicine in the field of anaesthesiology, intensive care and pain medicine: A European survey

BACKGROUND

The potential role of artificial intelligence in enhancing human life and medical practice is under investigation but the knowledge of the topic among healthcare providers is under-investigated.

OBJECTIVES

To investigate knowledge of artificial intelligence in physicians working in the field of anaesthesiology, intensive care, and pain medicine. As secondary outcomes, we investigated the main concerns on the implementation of artificial intelligence.

DESIGN

Online survey.

SETTING

Anaesthesiology, intensive care and pain medicine.

VOLUNTEERS

We invited clinicians specialised in anaesthesia, resuscitation, intensive care and pain medicine who were active members of the European Society of Anaesthesiology and Intensive Care (ESAIC).

INTERVENTION

Online survey from 28 June 2022 to 29 October 2022.

MAIN OUTCOME MEASURES

Primary outcome was to investigate knowledge of artificial intelligence and telemedicine of participants.

RESULTS

A total of 4465 e-mails were sent and 220 specialists, age 46.5 ± 10.2; 128 men (58.2%) responded to the survey. In general, some knowledge of artificial intelligence and machine learning was reported by 207 of 220 (94.1%) and 180 of 220 (81.8%) members, respectively. In anaesthesiology, 168 of 220 (76.4%) and 151 of 220 (68.6%) have heard of artificial intelligence and machine learning. In intensive care, 154 of 220 (70.0%) and 133 of 220 (60.5%) had heard of artificial intelligence and machine learning, while these figures were much lower in pain medicine [artificial intelligence: only 70/220 (31.8%) and machine learning 67/220 (30.5%)]. The main barriers to implementing these tools in clinical practice were: lack of knowledge of algorithms leading to the results; few validation studies available and not enough knowledge of artificial intelligence. Knowledge of telemedicine was reported in 212 of 220 (96.4%) members.

CONCLUSION

Most anaesthesiologists are aware of artificial intelligence and machine learning. General thinking about the application of artificial intelligence in anaesthesiology, intensive care and pain management was positive overall, with most participants not considering this tool as a threat to their profession.

Factors Influencing the Aged in the Use of Mobile Healthcare Applications: An Empirical Study in China

Mobile healthcare applications are of significant potential value in the development of the aged-care industry due to their great convenience, high efficiency, and low cost. Since the cognition and utilization rates of mobile healthcare applications for the elderly are still low, this study explored the factors that affect the elderly's adoption of mobile healthcare applications. This study conducted a questionnaire survey on the elderly in China and received 365 valuable responses. This study combined the technology acceptance model, protection motivation theory, and perceived risk theory to build a research model of factors affecting the use of mobile healthcare applications by the elderly. The data were analyzed using a structural equation model. The results were as follows: according to the empirical research, (1) perceived usefulness and perceived ease of use positively affect the use attitude of the elderly; perceived usefulness and user attitude positively affect the behavior intention of the elderly; perceived ease of use positively affects perceived usefulness; (2) perceived severity has a significant positive correlation with use attitude; perceived susceptibility and attitude to use have no significant impact; (3) perceived risk is negatively correlated with the use attitude and behavioral intention. The above-mentioned factors should be taken into consideration during the development of mobile healthcare applications for the aged to upgrade the overall service quality of mobile healthcare applications, thus enhancing the operational level of mobile healthcare applications and the health literacy of the aged.

Artificial Intelligence and Machine Learning Based Intervention in Medical Infrastructure: A Review and Future Trends

People in the life sciences who work with Artificial Intelligence (AI) and Machine Learning (ML) are under increased pressure to develop algorithms faster than ever. The possibility of revealing innovative insights and speeding breakthroughs lies in using large datasets integrated on several levels. However, even if there is more data at our disposal than ever, only a meager portion is being filtered, interpreted, integrated, and analyzed. The subject of this technology is the study of how computers may learn from data and imitate human mental processes. Both an increase in the learning capacity and the provision of a decision support system at a size that is redefining the future of healthcare are enabled by AI and ML. This article offers a survey of the uses of AI and ML in the healthcare industry, with a particular emphasis on clinical, developmental, administrative, and global health implementations to support the healthcare infrastructure as a whole, along with the impact and expectations of each component of healthcare. Additionally, possible future trends and scopes of the utilization of this technology in medical infrastructure have also been discussed.