Healthcare Technology Research Articles

A comparative analysis of generative artificial intelligence responses from leading chatbots to questions about endometriosis

IntroductionThe use of generative artificial intelligence (AI) has begun to permeate most industries, including medicine, and patients will inevitably start using these large language model (LLM) chatbots as a modality for education. As healthcare information technology evolves, it is imperative to evaluate chatbots and the accuracy of the information they provide to patients and to determine if there is variability between them. ObjectiveThis study aimed to evaluate the accuracy and comprehensiveness of three chatbots in addressing questions related to endometriosis and determine the level of variability between them. Study DesignThree LLMs, including Chat GPT-4 (Open AI), Claude (Anthropic), and Bard (Google) were asked to generate answers to 10 commonly asked questions about endometriosis. The responses were qualitatively compared to current guidelines and expert opinion on endometriosis and rated on a scale by nine gynecologists. The grading scale included the following: (1) Completely incorrect, (2) mostly incorrect and some correct, (3) mostly correct and some incorrect, (4) correct but inadequate, (5) correct and comprehensive. Final scores were averaged between the nine reviewers. Kendall's W and the related chi-square test were used to evaluate the reviewers’ strength of agreement in ranking the LLMs’ responses for each item. ResultsAverage scores for the 10 answers amongst Bard, Chat GPT, and Claude were 3.69, 4.24, and 3.7, respectively. Two questions showed significant disagreement between the nine reviewers. There were no questions the models could answer comprehensively or correctly across the reviewers. The model most associated with comprehensive and correct responses was ChatGPT. Chatbots showed an improved ability to accurately answer questions about symptoms and pathophysiology over treatment and risk of recurrence. ConclusionThe analysis of LLMs revealed that, on average, they mainly provided correct but inadequate responses to commonly asked patient questions about endometriosis. While chatbot responses can serve as valuable supplements to information provided by licensed medical professionals, it is crucial to maintain a thorough ongoing evaluation process for outputs to provide the most comprehensive and accurate information to patients. Further research into this technology and its role in patient education and treatment is crucial as generative AI becomes more embedded in the medical field.

A Preliminary Sentiment Analysis on Digital Barriers among Senior Citizens During Covid-19 Pandemic

Nowadays, digital technology has been an important element in assisting the community in their daily routine. During the COVID-19 era, The Special Committee for Ensuring Access to COVID-19 Vaccine Supply (JKJAV) has been initiated to assist the health and well-being of Malaysian citizens in recovering from the COVID-19 pandemic. MySejahtera, a healthcare application technology, has been used by Malaysian citizens for seeking vaccination information as well as updating their health status. While there are benefits to using the application, there is still a challenge in using the technology among senior citizens. This study aims to investigate digital barriers to using a healthcare application, known as MySejahtera, among senior citizens in Kelantan. This study also reveals lessons learned regarding the use of technology among senior citizens during the Covid-19 era. Convergent parallel mixed method research design is initiated to produce a sentiment analysis for identifying digital barriers issues and challenges among senior citizens in Kelantan. 5 respondents aged from 60 to 80 years old were interviewed to obtain their sentiments on using digital technology for vaccination information and Covid-19 news. This study highlights issues namely awareness of knowledge, misconception about digital technology, information quality and technology usage among the senior citizens in Kelantan. This study summarizes that there is a need to reduce digital barriers among senior citizens to ensure digital inclusion for their benefit in health and well-being.

Artificial intelligence (AI) applications in healthcare and considerations for nursing education

Aim/objectiveTo review the current AI applications in healthcare and explore the implications for nurse educators in innovative integration of this technology in nursing education and training programs. BackgroundThere are a variety of Artificial Intelligence (AI) applications currently supporting patient care in many healthcare settings. A nursing workforce that leverages healthcare technology to enhance efficiency and accuracy of patient health outcomes is necessary. Nurse educators must understand the various uses of AI applications in healthcare to equip themselves to effectively prepare students to use the applications. DesignQualitative synthesis and analysis of existing literature. MethodsGenerative AI (ChatGPT) was used to support the development of a list of the current AI applications in healthcare. Each application was evaluated for relevance and accuracy. A literature review to define and understand the use of each application in clinical practice was completed. The search terms “AI” and “Health Education” were used to review the literature for evidence on educational programs used for training learners. ResultsTen current applications of AI in healthcare were identified and explored. There is little evidence that outlines how to integrate AI education into educational training for nurses. ConclusionA comprehensive multimodal educational approach that uses innovative learning strategies has potential to support the integration of AI concepts into nursing curriculum. The use of simulation and clinical practicum experiences to support experiential learning and to offer opportunities for practical application and training. Considerations for ethical use and appropriate critical evaluation of AI applications are necessary.

Estimating the Effectiveness of some biological agents and Palm biochar in control of Dry bubble disease on the second flush of White mushroom Agaricus bisporus

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Orthodontic Materials and Antimicrobial Nanomaterials

In a time when technology is present in every aspect of our lives, it is crucial to incorporate advanced solutions to protect sensitive medical data in Social Medical Systems (SMS). This study explores the need to improve security in public healthcare by using advanced technologies to strengthen the weaknesses in the growing field of Social Medical Systems. This study specifically examines the analysis of IoT-23 data using machine learning (ML) and deep learning (DL) methods, as technology and healthcare converge. The research highlights the increasing significance of technology in healthcare, specifically focusing on the revolutionary emergence of Social Medical Systems. As these interlinked networks reshape the provision of public healthcare services, security challenges such as data breaches, cyber threats, and privacy concerns become crucial barriers that require innovative solutions. The study utilizes a wide range of machine learning (ML) and deep learning (DL) techniques to examine IoT-23 data, offering a detailed comprehension of the security environment in Social Medical Systems. The chosen models comprise Support Vector Machines (SVM), Isolation Forest, Random Forest, Convolutional Neural Networks (CNN), and Autoencoder. The results and discussions focus on evaluating metrics such as accuracy, precision, recall, and F1 score. These metrics provide insights into how effective each model is in identifying vulnerabilities.

Design, development and optimization of blockchain-based secure data management for nanotechnology in healthcare

The integration of nanotechnology in healthcare has brought about revolutionary advancements, particularly in diagnostics and therapeutics. However, managing the vast and sensitive data generated by these applications poses significant challenges, particularly concerning security, privacy, and regulatory compliance. This study explores the design, development, and optimization of a blockchain-based secure data management system tailored for nanotechnology applications in the United States healthcare sector. The proposed system leverages blockchain's decentralized and immutable ledger technology to enhance data security and privacy, ensure regulatory compliance, and improve overall system performance. Our implementation showed substantial improvements in data security, with an 86.67% reduction in data breaches and an 85% decrease in unauthorized access cases. Additionally, compliance with major data privacy standards like HIPAA, GDPR, and CCPA increased by over 20%. Performance metrics demonstrated robust scalability, maintaining high transaction speeds and manageable latency across various load conditions. Cost analysis revealed significant reductions in data storage, security, and compliance costs, highlighting the economic benefits of adopting blockchain technology in healthcare. Moreover, the system exhibited lower energy consumption compared to traditional data management systems, addressing environmental concerns. Correlation and scatter plot analyses provided insights into the scalability and performance dynamics of the blockchain system. These findings underscore the transformative potential of blockchain technology in addressing the data management challenges of nanotechnology in healthcare. The study concludes with recommendations for adopting blockchain to enhance data security, regulatory compliance, and economic efficiency in the U.S. healthcare sector.

Advances in Mesoporous Silica Nanoparticles: Synthesis, Characterization, and Biomedical Uses

Mesoporous silica nanoparticles (MSNs) have drawn significant attention due to their exceptional properties and diverse range of applications, particularly in nanomedicine. The distinctive properties of MSNs, such as their high surface area, tunable pore size, and versatile surface chemistry, make them ideal candidates for various biomedical applications. This review aims to present a detailed understanding of MSNs, from synthesis and characterization to their versatile applications in biomedicine, highlighting their significant potential in advancing healthcare technologies. The synthesis methods for MSNs were comprehensively discussed, emphasizing the influence of parameters like solvent, base, alkoxysilane concentrations, and template surfactants on the size and shape of the nanostructures. Different types of MSNs, including MCM-41, SBA-15, KIT-6, and hollow MSNs, are discussed, along with their synthesis protocols and unique characteristics. The review also covers various spectroscopic techniques, such as XRD, XPS, FTIR, NMR, and fluorescence spectroscopy, which are crucial for characterizing MSNs. Furthermore, the biomedical applications of MSNs are highlighted, demonstrating their potential in drug delivery systems, imaging, and diagnostics. The review concludes with a discussion of the future perspectives and challenges in the field, providing insights into potential developments and the prospects for clinical translation.

Machine Learning Applied to Edge Computing and Wearable Devices for Healthcare: Systematic Mapping of the Literature

The integration of machine learning (ML) with edge computing and wearable devices is rapidly advancing healthcare applications. This study systematically maps the literature in this emerging field, analyzing 171 studies and focusing on 28 key articles after rigorous selection. The research explores the key concepts, techniques, and architectures used in healthcare applications involving ML, edge computing, and wearable devices. The analysis reveals a significant increase in research over the past six years, particularly in the last three years, covering applications such as fall detection, cardiovascular monitoring, and disease prediction. The findings highlight a strong focus on neural network models, especially Convolutional Neural Networks (CNNs) and Long Short-Term Memory Networks (LSTMs), and diverse edge computing platforms like Raspberry Pi and smartphones. Despite the diversity in approaches, the field is still nascent, indicating considerable opportunities for future research. The study emphasizes the need for standardized architectures and the further exploration of both hardware and software to enhance the effectiveness of ML-driven healthcare solutions. The authors conclude by identifying potential research directions that could contribute to continued innovation in healthcare technologies.

Inside out: Exploring edible biocatalytic biosensors for health monitoring

Edible biosensors can measure a wide range of physiological and biochemical parameters, including temperature, pH, gases, gastrointestinal biomarkers, enzymes, hormones, glucose, and drug levels, providing real-time data. Edible biocatalytic biosensors represent a new frontier within healthcare technology available for remote medical diagnosis. The main challenges to develop edible biosensors are: i) finding edible materials (i.e. redox mediators, conductive materials, binders and biorecognition elements such as enzymes) complying with Food and Drug Administration (FDA), European Food Safety Authority (EFSA) and European Medicines Agency (EMEA) regulations; ii) developing bioelectronics able to operate in extreme working conditions such as low pH (∼pH 1.5 gastric fluids etc.), body temperature (between 37 °C and 40 °C) and highly viscous bodily fluids that may cause surface biofouling issues. Nowadays, advanced printing techniques can revolutionize the design and manufacturing of edible biocatalytic biosensors.This review outlines recent research on biomaterials suitable for creating edible biocatalytic biosensors, focusing on their electrochemical properties such as electrical conductivity and redox potential. It also examines biomaterials as substrates for printing and discusses various printing methods, highlighting challenges and perspectives for edible biocatalytic biosensors.

Generalizability assessment of AI models across hospitals in a low-middle and high income country

The integration of artificial intelligence (AI) into healthcare systems within low-middle income countries (LMICs) has emerged as a central focus for various initiatives aiming to improve healthcare access and delivery quality. In contrast to high-income countries (HICs), which often possess the resources and infrastructure to adopt innovative healthcare technologies, LMICs confront resource limitations such as insufficient funding, outdated infrastructure, limited digital data, and a shortage of technical expertise. Consequently, many algorithms initially trained on data from non-LMIC settings are now being employed in LMIC contexts. However, the effectiveness of these systems in LMICs can be compromised when the unique local contexts and requirements are not adequately considered. In this study, we evaluate the feasibility of utilizing models developed in the United Kingdom (a HIC) within hospitals in Vietnam (a LMIC). Consequently, we present and discuss practical methodologies aimed at improving model performance, emphasizing the critical importance of tailoring solutions to the distinct healthcare systems found in LMICs. Our findings emphasize the necessity for collaborative initiatives and solutions that are sensitive to the local context in order to effectively tackle the healthcare challenges that are unique to these regions.

The Use of AI in Enhancing Patient Communication

Artificial Intelligence (AI) has rapidly evolved in the healthcare sector, significantly transforming patient-provider communication. This paper examines the application of AI, particularly through tools like chatbots and virtual assistants, to enhance patient interaction and address communication barriers. It examines how AI-based tools improve patient accessibility, streamline information exchange, and aid in patient care, particularly in countries like Australia. The study also highlights the benefits and challenges associated with AI-driven communication, including efficiency, patient satisfaction, privacy concerns, and ethical considerations. Ultimately, it suggests that AI technology offers promising solutions for improving patient communication but requires further research and careful implementation to overcome challenges. Keywords: AI in healthcare, patient communication, chatbots, virtual assistants, telemedicine, healthcare technology.

Единые Государственные Информационные Системы Здравоохранения (ЕГИСЗ) в странах Востока.

The introduction of information technology (IT) in healthcare has led to a real revolution in the provision and management of medical care. However, they are developing with different intensity in different countries. The purpose of the study was to test criteria to determine the degree of IT development in healthcare, as well as to illustrate the assessment of IT development in different countries to compare them with each other. To achieve this goal, we have identified a set of criteria covering key aspects of the implementation and use of IT in medical practice. We have selected the countries of the East for this study. We searched information on the level of IT development in healthcare in the Eastern countries in Google open sources and PubMed databases manually, as well as using GPT-3.5 Turbo. According to the availability and development of criteria, the degree of IT development in healthcare was assessed and the categories «Highly», «Medium» and «Less» developed were assigned. As a result, within the study’s methodology framework, the UAE, Saudi Arabia, Qatar, Israel, Singapore, Indonesia, the Philippines, Laos, South Korea, Japan and China were classified as countries with highly developed IT in healthcare. Countries with medium-developed IT in healthcare included Jordan, Oman, Afghanistan, Iran, Iraq, Bahrain, Vietnam, Thailand, Myanmar, Cambodia, India and Bangladesh. Lebanon, Kuwait, East Timor and Brunei were among the countries with the least developed IT in healthcare.

Blockchain Revolutionizing Healthcare Industry: A Survey of Existing Literature

Abstract : Health care is a vast industry that requires huge data storage and sharing and this is where this industry faces multiple challenges and requires an efficient system. This review article aimed to summarize the benefits of the blockchain industry in the healthcare sector. Literature review was conducted through PubMed and Google Scholar. A total of 30 articles were selected for review purposes with keywords health care, blockchain technologies, and benefits of blockchain and blockchain and health care industry with search engines like Google Scholar, Research Gate, and APA journals. A thorough review of existing literature suggests that blockchain technology can be beneficial for many fields and subfields of the healthcare industry including patients, organizations, hospitals, pharmaceuticals, global record-keeping organizations, health professionals, and researchers. Blockchain can be beneficial as it is efficient, reliable, transparent, cost-effective, easily accessible, vast data storage and management enable, and many in many more ways. In a nutshell, the importance of blockchain in the healthcare industry and suggests incorporating this technology in healthcare for efficient working.

A Digital Health Humanities Approach to the Three-Body Problem in Healthcare Education: Balancing the Science, Art and Tech with M.A.G.I.C.

In the provision of patient-centered care, healthcare professionals face a challenging triad that is analogous to the classical "Three-Body Problem" in physics, whereby any shifts in the dynamics of medical knowledge, narrative competence and digital technologies can tip the balance of healthcare education and practice. Drugs/Medications are often caught in the dichotomy of being both "heroes" and "villains", which underscores the necessity of a balanced, informed, and empathetic approach to patient care. Drawing inspiration from the popular novel and Netflix series, the dynamic interplay between the science of medical knowledge, the art of empathy and narrative competence, and the advancement of healthcare technologies to harmonise the diverse, yet interconnected domains, of medication management, medical humanities and digital health is demonstrated - in the emerging field of Digital Health Humanities. The novel project called M.A.G.I.C. is revealed, that blends the rigour of medical science with the nuanced understanding of patient narratives, and the cutting-edge potential of digital innovations like Generative Artificial Intelligence and the Metaverse, to create a holistic, learner-centered approach for the education of the healthcare professions.

Unveiling User Perspectives: Exploring Themes in Femtech Mobile App Reviews for Enhanced Usability and Privacy

Femtech, a growing sector in mobile healthcare technology, caters to women's needs across various life stages with digital solutions like period tracking and pregnancy management apps. Maintaining robust data privacy is crucial due to the sensitive nature of the information involved, such as menstrual cycles and pregnancy status. Our research analyzes user feedback from platforms like the Apple App Store and Google Play Store to understand perceptions of femtech apps, covering accessibility, interface, features, and privacy concerns. Understanding user perspectives helps developers enhance usability and trust, driving further adoption. Prioritizing privacy also fosters industry advancement. This paper stresses the importance of dialogue among developers, users, and policymakers in femtech. Our findings aim to facilitate positive change within the femtech sector, leading to more inclusive, user-centric, and ethically driven advancements, benefiting both the industry and its users.

Trends and Public Perception of Artificial Intelligence in Medical Imaging: A Social Media Analysis.

The rapid advancement of artificial intelligence (AI) in medical imaging has generated significant interest and debate among healthcare professionals, researchers, and the general public. This study aims to explore trends and public perception of AI in medical imaging by analyzing social media discussions. Using a retrospective content analysis approach, social media posts from X (formerly known as Twitter) and Reddit were collected, covering discussions from 2019 to 2024. A total of 1,022 posts were analyzed after data cleaning, employing both qualitative and quantitative methods to examine sentiment, themes, and keyword frequencies. The sentiment analysis revealed that 55% of the comments expressed positive sentiments towards AI in medical imaging, emphasizing its potential to enhance diagnostic accuracy and efficiency. Neutral sentiments accounted for 35% of the posts, while 10% expressed negative sentiments, primarily focusing on concerns related to job displacement, ethical issues, and data privacy. Thematic analysis identified four primary themes: ethical and privacy concerns, job displacement, trust and reliability, and workflow efficiency. Keyword frequency analysis highlighted significant discussions around AI, imaging, and radiology. The results underscore both the optimism and concerns associated with AI in medical imaging, emphasizing the need for ongoing dialogue among technology developers, healthcare providers, and the public. Addressing ethical and privacy concerns, and integrating AI responsibly into clinical workflows, is crucial for maximizing its benefits and minimizing potential risks. These findings provide valuable insights into public perceptions and inform strategies for the effective and ethical implementation of AI technologies in healthcare.

Making inroads in the Indian AI imaging market

Research methodology This case study involves interviews with radiologists of various hospitals and with company personnel. Both primary and secondary data sources have been used. The first-hand perspective from the radiologists highlighted the challenges they face concerning time and the patient load. The company personnel highlighted using machine learning for used cases to make the platform more robust and accurate. This case has been tested with MBA students. Case overview/synopsis An emerging health-care artificial intelligence (AI) start-up, DeepTek.AI, wants to expand its reach in the radiology market. The company intends to leverage technology to assist radiologists in diagnostics. India's health-care sector faces the challenge of needing more trained doctors and nurses to meet the ever-increasing needs of patients. This case study revolves around the radiologists' concerns about implementing the new technology and its ease of use. The features and benefits of integrating AI in diagnostics are the need of the hour, but the reliability of results needs to be ascertained for adopting it. Complexity academic level This case was written for marketing applications and practices, trends in marketing, marketing strategy and technology adoption in marketing courses at the post-graduate level. Consumer adoption of finance, hospitality, travel and health-care technology is vital for increasing the company's market share and growth prospects. The students will have an opportunity to understand the challenges and the opportunities.

Addressing the Digital Divide in Health Education: A Systematic Review.

The disparity in access to essential healthcare resources and services is exacerbated by the digital divide, which presents a significant obstacle to health education. Effective tactics to advance digital equity and provide equitable access to resources for telehealth and digital health are needed to close this gap. Digital databases such as PubMed, MEDLINE, Scopus, Web of Science, and Google Scholar were used to conduct a systematic review. Keywords and Boolean operators including "digital divide," "health education," "digital equity," "telehealth," "digital health literacy," and "strategies" were used in the literature search process. Only peer-reviewed English-language papers that addressed methods for bridging the digital divide in health education were accepted after being screened in accordance with the preset inclusion and exclusion criteria. The results were compiled using a narrative synthesis method after data were retrieved and synthesized with the aid of suitable quality assessment tools. After satisfying the inclusion criteria, seven papers were added to the systematic review. The results emphasized the complexity of the digital divide in health education and provided a range of approaches to mitigate disparities in access to digital health technologies and resources. The importance of digital equality and universal design, continuous intervention evaluation and monitoring, and enduring obstacles to Internet access and healthcare technology availability were among the major themes. This systematic study emphasizes how critical it is to put evidence-based tactics into practice in order to close the digital divide in health education. Through the promotion of universal design principles, ongoing evaluation of treatments, and digital equity, stakeholders can mitigate health disparities, improve population health, and guarantee equitable access to telehealth and digital health services.

The promise of artificial intelligence in health: Portrayals of emerging healthcare technologies

AbstractEmerging technologies of artificial intelligence (AI) and automated decision‐making (ADM) promise to advance many industries. Healthcare is a key locus for new developments, where operational improvements are magnified by the bigger‐picture promise of improved care and outcomes for patients. Forming the zeitgeist of contemporary sociotechnical innovation in healthcare, media portrayals of these technologies can shape how they are implemented, experienced and understood across healthcare systems. This article identifies current applications of AI and ADM within Australian healthcare contexts and analyses how these technologies are being portrayed within news and industry media. It offers a categorisation of leading applications of AI and ADM: monitoring and tracking, data management and analysis, cloud computing, and robotics. Discussing how AI and ADM are depicted in relation to health and care practices, it examines the sense of promise that is enlivened in these representations. The article concludes by considering the implications of promissory discourses for how technologies are understood and integrated into practices and sites of healthcare.

Организационно-финансовые аспекты выбора интраокулярных линз при хирургии катаракты в условиях ценностно-ориентированной системы здравоохранения.

Relevance. In modern conditions, value-based healthcare provides for the provision by medical organizations of medical care of appropriate quality with mandatory control of its cost. Treatment outcomes that are important to the patient become no less significant than clinical performance indicators and payment for medical services, regardless of the profile of medical care. The gradual change in paradigms of healthcare systems from a paternalistic to a person-centric model required changes in organizational, legislative and financial approaches when introducing health-saving healthcare technologies. Purpose of the study: analysis of the current organizational, legal, financial and clinical models of surgical treatment of patients with age-related cataracts, astigmatism and development of proposals for the implementation of the patient’s right to choose intraocular lenses, taking into account his modern expectations from the results of treatment. Materials and methods. Statistical and analytical methods were used (content analysis methods, expert assessment methods, analysis and synthesis methods). Results. An analysis of the legislation in organizing the provision and financing of medical care to the adult population in the field of ophthalmology is presented, and state guarantees for the use of medical products – types of intraocular lenses implanted into the human body for the surgical treatment of senile cataracts and astigmatism – are considered. Clinical models of the disease are systematized, taking into account the frequency and frequency of provision of medical services and the list of medical devices implanted into the human body, professional, medico-economic, medico-legal and reputational risks are described when exercising the patient’s right to choose intraocular lenses in the treatment of ophthalmological pathology. Proposals have been developed to change clinical recommendations, standards of medical care for senile cataracts and astigmatism, and improve payment for ophthalmological care. Conclusions. The transition to value-based healthcare requires the consolidation of the efforts of all structures of the healthcare industry to achieve the best results in the treatment of ophthalmic diseases and increase the clinical effectiveness associated with the choice of intraocular lenses in the surgical treatment of adults in the field of ophthalmology.