Healthcare Tools Research Articles

Artificial Intelligence in public health: A case study

Artificial Intelligence technology has become an innovative tool in global healthcare that helps to manage various problems of scale and complexity. Its use in public health is multifaceted and multivariate extending across early outbreak detection, risk modeling and prediction, efficient vaccine logistics and administration, reducing health disparities, aiding assist in diagnosis, everyday monitoring of chronic diseases, telemedicine and virtual chatbots especially for elderly care, mental health and oncology patients, and community engagement. Despite these huge generational advantages, multiple challenges exist in data quality, security and misuse, individual privacy, contextual application, accountability and oversight and remain primary concerns for universal adoption of artificial intelligence. There is a need for safeguards and a global regulatory framework to responsibly harvest the benefits of artificial intelligence for the advancement of public and community health.

Evaluating the reliability of the responses of large language models to keratoconus-related questions

ABSTRACT Clinical relevance Artificial intelligence has undergone a rapid evolution and large language models (LLMs) have become promising tools for healthcare, with the ability of providing human-like responses to questions. The capabilities of these tools in addressing questions related to keratoconus (KCN) have not been previously explored. Background In this study, the responses were evaluated from three LLMs – ChatGPT-4, Copilot, and Gemini – to common patient questions regarding KCN. Methods Fifty real-life patient inquiries regarding general information, aetiology, symptoms and diagnosis, progression, and treatment of KCN were presented to the LLMs. Evaluations of the answers were conducted by three ophthalmologists with a 5-point Likert scale ranging from ‘strongly disagreed’ to ‘strongly agreed’. The reliability of the responses provided by LLMs was evaluated using the DISCERN and the Ensuring Quality Information for Patients (EQIP) scales. Readability metrics (Flesch Reading Ease Score, Flesch-Kincaid Grade Level, and Coleman-Liau Index) were calculated to evaluate the complexity of responses. Results ChatGPT-4 consistently scored 3 points or higher for all (100%) its responses, while Copilot had five (10%) and Gemini had two (4%) responses scoring 2 points or below. ChatGPT-4 achieved a ‘strongly agree’ rate of 74% across all questions, markedly superior to Copilot at 34% and Gemini at 42% (p < 0.001); and recorded the highest ‘strongly agree’ rates in general information and symptoms & diagnosis categories (90% for both). The median Likert scores differed among LLMs (p < 0.001), with ChatGPT-4 scoring highest and Copilot scoring lowest. Although ChatGPT-4 exhibited more reliability based on the DISCERN scale, it was characterised by lower readability and higher complexity. While all LLMs provided responses categorised as ‘extremely difficult to read’, the responses provided by Copilot showed higher readability. Conclusions Despite the responses provided by ChatGPT-4 exhibiting lower readability and greater complexity, it emerged as the most proficient in answering KCN-related questions.

Artificial intelligence in healthcare: a scoping review of perceived threats to patient rights and safety

BackgroundThe global health system remains determined to leverage on every workable opportunity, including artificial intelligence (AI) to provide care that is consistent with patients’ needs. Unfortunately, while AI models generally return high accuracy within the trials in which they are trained, their ability to predict and recommend the best course of care for prospective patients is left to chance.PurposeThis review maps evidence between January 1, 2010 to December 31, 2023, on the perceived threats posed by the usage of AI tools in healthcare on patients’ rights and safety.MethodsWe deployed the guidelines of Tricco et al. to conduct a comprehensive search of current literature from Nature, PubMed, Scopus, ScienceDirect, Dimensions AI, Web of Science, Ebsco Host, ProQuest, JStore, Semantic Scholar, Taylor & Francis, Emeralds, World Health Organisation, and Google Scholar. In all, 80 peer reviewed articles qualified and were included in this study.ResultsWe report that there is a real chance of unpredictable errors, inadequate policy and regulatory regime in the use of AI technologies in healthcare. Moreover, medical paternalism, increased healthcare cost and disparities in insurance coverage, data security and privacy concerns, and bias and discriminatory services are imminent in the use of AI tools in healthcare.ConclusionsOur findings have some critical implications for achieving the Sustainable Development Goals (SDGs) 3.8, 11.7, and 16. We recommend that national governments should lead in the roll-out of AI tools in their healthcare systems. Also, other key actors in the healthcare industry should contribute to developing policies on the use of AI in healthcare systems.

Brokering responsible research and innovation in in silico medicine

ABSTRACT The implementation of Responsible Research and Innovation (RRI) in research projects has increased the need for interdisciplinary collaboration. This article presents our RRI approach within the Horizon 2020 ‘In Silico World' project, which aims to accelerate the adoption of in silico medicine through computer modeling and simulation tools in healthcare. To address the shortcomings of the ‘checklist approach' for integrating ethics and the risk of becoming checkboxes ourselves, we introduce the term ‘RRI brokers.’ It serves as a lens for evaluating the project's RRI activities and the dynamics that can be faced by Social Science and Humanities scholars (SSH), and as a means to acquire agency in our own positioning. We suggest that to strengthen RRI, more consideration is needed on how we present our expertise in these collaborations, and awareness of how we, as RRI brokers, move between translating, balancing, and shaping worlds, affecting what we broker and ourselves.

Optimising co-design processes in telemedicine innovation-developing a telemedical solution for emergency medical services.

Stakeholder engagement plays a vital role in driving advancements in product development. This imperative now extends to the healthcare domain, driven by the scarcity of healthcare professionals and the pressing demand for effective solutions. Through the application of design thinking and co-design methodologies, this study endeavours to promote comprehensive stakeholder involvement, creating streamlined processes and adaptable templates geared towards fostering innovative solutions for tele-emergency medicine. In this study design thinking and co-design methods are developed, adapted, and tested, to create effective tools and demonstrate their application. This is part of a process involving stakeholders and lead users to develop a telemedicine solution for emergency medical services. This research is descriptive in kind, offering a transparent and holistic portrayal of the co-design process. The rural region of Burgenland in Austria was chosen for this study, with the challenges of its weak infrastructure offering valuable insights. The tools were tested in co-design workshops, with the participants continuously observed by the research team. Seventeen healthcare professionals, emergency medical technicians and academics participated in a co-design process to develop a telemedicine solution for emergency medical services. The results section presents practical co-creative healthcare innovation tools and templates that have been shown to facilitate the co-design process. The study developed and applied co-design elements for the creation of a prototype concept for telemedicine in emergency medicine and offers valuable insights for similar projects involving diverse stakeholders. It shows that structured co-design activities help all stakeholders to jointly create solutions that meet the overall needs.

Prenatal Telepsychological Intervention for Preventing Anxiety: A Study Protocol.

Background: Anxiety is one of the most frequent disorders during the perinatal stage that is associated with adverse health effects in women and their babies. In spite of this, preventive interventions during this stage are scarce. A long-distance intervention carried out during pregnancy can be an effective and accessible resource to help improve women's emotional state. The objective of this study is to present and assess the effectiveness of a telepsychology cognitive-behavioral preventive intervention during pregnancy to manage anxiety. Methods: A random clinical trial will be carried out among pregnant women in Spain. The psychological intervention will take place via videoconference in seven weekly sessions, lasting one hour each, with groups of 6-8 pregnant women. Study outcomes will be collected via online questionnaires at five points in time: pre-intervention (baseline: t0), post-intervention (baseline: t1), follow-up at 1 month (t2), follow-up at 3 months (t3) and follow-up at 6 months (t4). The control group will receive usual pregnancy care (attendance at pregnancy follow-up consultations and information and answers to frequently asked questions provided by the midwife). Our primary hypothesis is that the intervention will decrease the frequency of women who present anxious symptomatology during pregnancy. The second objective is to analyze the effectiveness of this intervention to prevent depressive symptomatology during pregnancy, as well as postpartum anxiety and depressive symptomatology. The primary outcome measure is the difference in the mean anxiety score between the intervention and control groups assessed by the Edinburgh Depression Scale-Anxiety Subscale (EDS-3A), the State-Trait Anxiety Inventory (STAI) and Generalized Anxiety Disorder Screener (GAD-7) at the end of the intervention and at 1, 3 and 6 months postpartum. Generalized anxiety disorder (GAD) will be evaluated with the SCID clinical interview at the same time points. The secondary outcome will be determined by using the EPDS at the same time points. Conclusions: The results will determine whether a cognitive behavioral therapy applied via videoconference is well accepted by pregnant women, and if it is effective in preventing anxiety and emotional symptoms during the perinatal stage. If this intervention is an effective and useful resource among pregnant women, it can be implemented as a tool in Spanish healthcare.

Technology readiness assessment: Case of clinical decision support systems in healthcare

Clinical Decision Support Systems (CDSS) play a critical role in modern healthcare by supporting healthcare providers in making well-informed decisions, improving patient safety and outcomes, enhancing efficiency, and promoting evidence-based practices. Their integration into clinical workflows can lead to more effective and patient-centered care. CDSS is essential tools for healthcare organizations as well as for healthcare providers to improve clinical care. However, successful implementation of CDSS can be challenging. Therefore, before implementing CDSS, it is crucial to assess the readiness of healthcare organizations to implement these tools. CDSS is essential tools in healthcare for several compelling reasons. For instance, enhanced patient safety, improved diagnostic accuracy, optimized treatment plans, consistency in care, and support for complex decisions. This study's aim is to develop a model that will help healthcare organizations identify the challenges of implementing CDSS, and to assess their readiness for such an implementation in a comprehensive and multi-dimensional manner. Through a literature review, the first step of this research explores the concept of clinical decision support and CDSS, discussing their features, characteristics, and organizational hurdles to implementation. It also provides perspectives on CDSS adoption in the context of information systems and health technology. The review helped to identify research gaps, objectives, and questions. To address these gaps and to attempt to answer the research questions, a Hierarchical Decision Model (HDM) is proposed. The model allows us to assess the readiness of healthcare organizations for CDSS implementation. It presents four perspectives and sixteen criteria for a multi-dimensional assessment. The methodology involves expert panels for the HDM model's refinement, validation, and quantification. Two case studies are then presented to demonstrate the HDM model's application to identify real-world CDSS implementation challenges and to provide insights and recommendations. The research contributions are evaluated against the identified gaps in the literature review, with limitations and future research presented. In conclusion, this research provides valuable insights into CDSS implementation readiness assessment and highlights the need for careful consideration and planning. The proposed HDM model offers a valuable framework for healthcare organizations to evaluate their readiness for CDSS implementation.

Navigating ChatGPT's Alignment with Expert Consensus on Pediatric OSA Management

ObjectiveThis study aimed to evaluate the potential integration of artificial intelligence (AI), specifically ChatGPT, into healthcare decision-making, focusing on its alignment with expert consensus statements regarding the management of persistent pediatric obstructive sleep apnea. MethodsWe analyzed ChatGPT's responses to 52 statements from the 2024 expert consensus statement (ECS) on the management of pediatric persistent OSA after adenotonsillectomy. Each statement was input into ChatGPT using a 9-point Likert scale format, with each statement entered three times to calculate mean scores and standard deviations. Statistical analysis was performed using Excel. ResultsChatGPT's responses were within 1.0 of the consensus statement mean score for 63% (33/52) of the statements. 13% (7/52) were statements in which the ChatGPT mean response was different from the ECS mean by 2.0 or greater, the majority of which were in the categories of surgical and medical management. Statements with ChatGPT mean scores differing by more than 2.0 from the consensus mean highlighted the risk of disseminating incorrect information on established medical topics, with a notable variation in responses suggesting inconsistencies in ChatGPT's reliability. ConclusionWhile ChatGPT demonstrated a promising ability to align with expert medical opinions in many cases, its inconsistencies and potential to propagate inaccuracies in contested areas raise important considerations for its application in clinical settings. The findings underscore the need for ongoing evaluation and refinement of AI tools in healthcare, emphasizing collaboration between AI developers, healthcare professionals, and regulatory bodies to ensure AI's safe and effective integration into medical decision-making processes.

Exploration of digital health literacy in the deep south.

385 Background: As technological advances are implemented in healthcare, cancer patients with low digital health literacy are at risk for inequities. Understanding the community’s comfort with using and teaching technology skills can be foundational for the application of digital healthcare tools. Methods: A cross-sectional survey was administered prior to community conversations about digital health literacy to understand community comfort with technology. Surveys measured self-reported personal comfort performing, personal comfort teaching, and perceived county-comfort completing technology-based tasks. Participants rated their comfort level on a 5-point Likert scale (1= not at all, 2 = slightly, 3 = somewhat, 4 = moderately, 5 = completely). Mean scores were calculated and mean comfort levels were compared across healthcare providers, lay navigators, community health advisors (CHAs), and community members using one-way ANOVA tests. Tukey's HSD post-hoc tests were used to explore group differences. Results: Between 8/2023-4/2024, 164 participants across Alabama and Mississippi completed the pre-survey, including 32 healthcare providers, 17 lay navigators, 31 community health advisors (CHAs), and 84 community members. The sample was primarily female (90%), Black (74%), and from Alabama (84%). ANOVA tests revealed significant group differences between personal comfort levels (p&lt;0.001) comfort teaching (p&lt;0.001), and perception of county-level comfort in technology-based tasks (p=0.043) with lay navigators and healthcare providers reporting higher personal comfort and comfort teaching others technology-based tasks compared to community members (post-hoc Tukey’s HSD). Conclusions: Digital health literacy gaps remain, with lay and clinical providers potentially overestimating comfort with technology. Baseline technology skills must be addressed as healthcare increases reliance on technology. Personal Comfort p-value Comfort Teaching Others p-value Perception of County Comfort p-value Using Search Engines 3.61 4.72 4.76 4.23 &lt;.0001 2.95 4.19 4.88 3.48 &lt;.0001 2.52 3.16 3.82 3.13 0.043 Searching Online for Health-Related Information 3.49 4.66 4.71 4.06 2.90 4.03 4.71 3.35 2.46 2.69 3.29 2.97 Scanning QR codes 2.80 4.47 4.65 3.77 2.55 3.84 4.76 3.13 2.38 2.44 2.71 2.81 Logging into their Patient Portal 3.27 4.44 4.59 3.87 2.75 3.94 4.76 3.03 2.44 2.59 3.00 2.65 Sending their Care-Team a Message 3.05 4.38 4.65 3.74 2.63 3.91 4.82 3.03 2.37 2.44 2.71 2.58 Personal comfort, comfort teaching, perception of community comfort with technology; 7/17 tasks shown; C = community, H = hospital, L = lay navigator, CHAs = community health advisors.

Arduino-Based Real-Time ECG Monitoring System for Heart Signal Visualization

Electrocardiogram (ECG) monitoring is an essential tool in healthcare for diagnosing and tracking heart conditions. However, existing ECG devices are often expensive, limiting accessibility for personal or educational use. This project aims to develop a low-cost ECG monitoring system using an Arduino UNO, AD8232 sensor, and TFT SPI display. The system collects ECG signals via electrodes and displays the real-time PQRST waveform on the TFT screen and the Arduino IDE serial monitor. Resistors are utilized to ensure signal clarity by reducing electrical noise. The project successfully demonstrated the ability to visualize clear ECG signals in real time, offering a practical and cost-effective solution for continuous cardiac monitoring

The Challenges for EU User Testing Policies for Patient Information Leaflets.

Patient information leaflets (PILs) are essential tools in healthcare, providing crucial information about medication use. In the European Union, the European Medicines Agency (EMA) oversees the regulation and standardisation of PILs to ensure their readability and accessibility. However, challenges persist in ensuring these documents are comprehensible and user-friendly. This study employs a qualitative analytical approach, reviewing existing literature and regulatory documents to identify gaps in the EU user testing policies for PILs. It focuses on the diversity of participant samples, the independence of the testing process, and the robustness of user testing protocols. Findings indicate that current user testing practices often lack diversity and may be biased when pharmaceutical companies conduct their own tests. Additionally, there is a lack of user testing protocols for translated PILs, potentially compromising their accuracy and cultural relevance. To improve the efficacy of PILs, it is essential to include diverse and representative samples in user testing, mandate independent third-party evaluations, implement protocols for user testing on translated PILs, and ensure continuous updates to guidelines based on the latest best practices in health communication. These measures will enhance patient safety and understanding of medication information.

Mapping World: A Comparative Analysis of Functionality and Usability between QGIS and ArcGIS

The current review conducts a comprehensive comparison of Quantum GIS (QGIS) and ArcGIS in the context of healthcare applications, aiming to assist users in selecting the most appropriate GIS platform for their specific needs. Geographic Information Systems (GIS) are essential tools in healthcare, providing capabilities for spatial analysis, disease mapping, and resource allocation. Driven by the need to improve healthcare delivery and public health strategies, this study compares the functionalities, costs, and support systems of QGIS and ArcGIS. The major objective is to determine which GIS platform offers the best balance of cost-efficiency and performance across various healthcare scenarios. The methodology encompasses a detailed comparative analysis, incorporating quantitative data from user satisfaction surveys, performance evaluations, and real-world case studies. Findings reveal that QGIS, as an open-source GIS software, is highly cost-effective and customizable, making it particularly advantageous for budget-limited projects and smaller organizations requiring flexibility. Its extensibility via plugins and robust user community fosters continuous innovation and improvement. QGIS proves exceptionally effective in healthcare applications such as disease mapping and accessibility analysis, where customization and cost-efficiency are critical. Conversely, ArcGIS is distinguished by its advanced analytical capabilities and strong performance in handling complex, large-scale studies. Its sophisticated tools and comprehensive spatial analysis functionalities are essential for thorough healthcare research and planning. Although ArcGIS incurs higher costs, its extensive feature set, professional support, and exhaustive documentation justify the investment for large organizations and research institutions engaged in detailed GIS projects. In conclusion, both QGIS and ArcGIS exhibit significant strengths in key healthcare applications. QGIS excels in affordability and flexibility, while ArcGIS provides superior analytical power and support for extensive projects. Future research should focus on integrating real-time data and enhancing user experience to further optimize GIS applications in healthcare.

Θ-Net: A Deep Neural Network Architecture for the Resolution Enhancement of Phase-Modulated Optical Micrographs In Silico.

Optical microscopy is widely regarded to be an indispensable tool in healthcare and manufacturing quality control processes, although its inability to resolve structures separated by a lateral distance under ~200 nm has culminated in the emergence of a new field named fluorescence nanoscopy, while this too is prone to several caveats (namely phototoxicity, interference caused by exogenous probes and cost). In this regard, we present a triplet string of concatenated O-Net ('bead') architectures (termed 'Θ-Net' in the present study) as a cost-efficient and non-invasive approach to enhancing the resolution of non-fluorescent phase-modulated optical microscopical images in silico. The quality of the afore-mentioned enhanced resolution (ER) images was compared with that obtained via other popular frameworks (such as ANNA-PALM, BSRGAN and 3D RCAN), with the Θ-Net-generated ER images depicting an increased level of detail (unlike previous DNNs). In addition, the use of cross-domain (transfer) learning to enhance the capabilities of models trained on differential interference contrast (DIC) datasets [where phasic variations are not as prominently manifested as amplitude/intensity differences in the individual pixels unlike phase-contrast microscopy (PCM)] has resulted in the Θ-Net-generated images closely approximating that of the expected (ground truth) images for both the DIC and PCM datasets. This thus demonstrates the viability of our current Θ-Net architecture in attaining highly resolved images under poor signal-to-noise ratios while eliminating the need for a priori PSF and OTF information, thereby potentially impacting several engineering fronts (particularly biomedical imaging and sensing, precision engineering and optical metrology).

Artificial Intelligence in Dry Eye Disease: A Narrative Review.

Dry eye disease (DED) is a multifactorial condition affecting millions worldwide, characterized by discomfort, visual disturbance, and potential damage to the ocular surface. The complexity of its diagnosis and management, driven by the diversity of symptoms and underlying causes, presents significant challenges to clinicians. Artificial intelligence (AI) has emerged as a transformative tool in healthcare, offering potential solutions to these challenges through its data analysis, pattern recognition, and predictive modeling capabilities. This narrative review explores the role of AI in diagnosing, treating, and managing dry eye disease. AI-driven tools such as machine learning algorithms, imaging technologies, and diagnostic platforms are examined for their ability to enhance diagnostic accuracy, personalize treatment approaches, and optimize patient outcomes. Furthermore, the review addresses the limitations of AI technologies in ophthalmology, including the need for robust clinical validation, data privacy concerns, and the ethical considerations of integrating AI into clinical practice. The findings suggest that while AI holds promise for improving the care of patients with DED, ongoing research and development are crucial to realizing its full potential.

Review of emerging trends and projection of future developments in large language models research in ophthalmology

BackgroundLarge language models (LLMs) are fast emerging as potent tools in healthcare, including ophthalmology. This systematic review offers a twofold contribution: it summarises current trends in ophthalmology-related LLM research and...

Advancing rheumatology with natural language processing: insights and prospects from a systematic review.

Natural language processing (NLP) and large language models (LLMs) have emerged as powerful tools in healthcare, offering advanced methods for analysing unstructured clinical texts. This systematic review aims to evaluate the current applications of NLP and LLMs in rheumatology, focusing on their potential to improve disease detection, diagnosis and patient management. We screened seven databases. We included original research articles that evaluated the performance of NLP models in rheumatology. Data extraction and risk of bias assessment were performed independently by two reviewers, following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. The Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies was used to evaluate the risk of bias. Of 1491 articles initially identified, 35 studies met the inclusion criteria. These studies utilized various data types, including electronic medical records and clinical notes, and employed models like Bidirectional Encoder Representations from Transformers and Generative Pre-trained Transformers. High accuracy was observed in detecting conditions such as RA, SpAs and gout. The use of NLP also showed promise in managing diseases and predicting flares. NLP showed significant potential in enhancing rheumatology by improving diagnostic accuracy and personalizing patient care. While applications in detecting diseases like RA and gout are well developed, further research is needed to extend these technologies to rarer and more complex clinical conditions. Overcoming current limitations through targeted research is essential for fully realizing NLP's potential in clinical practice.

Exploiting Speech Tremors: Machine Learning for Early Diagnosis of Amyotrophic Lateral Sclerosis

Abstract Neurodegenerative diseases pose significant challenges in healthcare, with Amyotrophic Lateral Sclerosis (ALS) being one such rare yet debilitating condition affecting motor neurons. Machine learning (ML) and artificial intelligence (AI) have emerged as powerful tools in healthcare, offering insights and solutions for various medical conditions. This study focuses on utilizing ML and AI to establish an early diagnosis of ALS through tremor detection in sustained speech. The study aims to establish an early diagnosis of ALS by analyzing tremors detected in sustained speech. The dataset comprises 54 patients from the Republican Research and Clinical Centre of Neurology and Neurosurgery in Belarus, Minsk. The study adopts a two-faceted approach: (1) Exploratory voice analysis to identify tremors associated with ALS in speech samples. (2) Development of ML algorithms to construct predictive models for early ALS diagnosis based on the identified tremors. The ML models exhibit promising results in distinguishing ALS patients from healthy controls based on speech analysis. Tremor detection in sustained speech proves to be an effective marker for early ALS diagnosis. While initial findings are encouraging, larger-scale studies are required to validate the clinical applicability of this approach. The successful application of ML and AI in early ALS diagnosis by leveraging innovative approaches, such as tremor detection in sustained speech, we can enhance early diagnosis and improve patient outcomes in neurodegenerative diseases like ALS on a broader scale.&amp;#xD;

Evaluating the Knowledge of Radiation Exposure Parameters Among CT Technologists in Saudi Arabia: A Survey-Based Study on Adherence to ALARA Principles and Dose Optimization

Computed tomography is a crucial tool in modern healthcare, offering non-invasive imaging for diagnosis and monitoring. However, its widespread use raises concerns about radiation exposure. Adhering to the ALARA principle is vital to minimize risks while maintaining diagnostic quality. This study evaluates the knowledge of CT technologists in Saudi Arabia regarding radiation exposure parameters. A questionnaire-based survey was conducted, covering demographics, perceptions, and understanding of CT parameters. Statistical analysis revealed gaps in knowledge, particularly regarding dose optimization strategies and diagnostic reference levels. While most participants recognized the importance of adjusting parameters based on patient characteristics and clinical indications, deficiencies were observed in understanding specific parameters' effects on dose and image quality. Furthermore, a notable proportion of participants were unfamiliar with DRLs, indicating a need for improved education in this area. The study emphasizes the importance of interdisciplinary collaboration between radiologists, physicists, and technologists in protocol development and optimization.

Application of Linear Algebra in Image Processing for Medical Electronics

A very important part of medical electronics is the use of linear algebra in picture processing to improve the accuracy of diagnosis, treatment plans, and medical study. This abstract talks about the basic ideas and real-world uses of linear algebra in this area, showing how important it is for improving healthcare tools. Linear algebra gives us a strong way to change and understand pictures, which is very important in many types of medical imaging, like MRI, CT scans, ultrasound, and digital pathology. Images and how they are represented and changed are two of the most basic uses of linear algebra. A lot of the time, images are shown as matrices or tensors, where each part is a pixel strength or color value. Linear changes, like translations, rotations, and scaling, are needed to line up pictures, fix errors, and make sure that image data is the same across all modes. In medical image analysis, linear algebra methods like eigenanalysis and matrix decomposition (for example, Singular Value Decomposition) are also used to get information from images and make them simpler. These techniques help doctors and researchers find important patterns, oddities, and structures in pictures, which makes automatic analysis and disease classification easier. Medical image registration is a very important step for lining up pictures from different sources or places in time. Linear algebra makes it easier to figure out transformation matrices that get the best spatial alignment. This is very important for continuous studies and tracking of treatment, where exact comparison and analysis depend on perfectly aligned images. Linear algebra is also very important in methods for improving and fixing images. For example, filtering processes based on convolution matrices are used to get rid of noise, boost contrast, and bring out features in medical pictures, which makes them easier for doctors to see and understand. In computer imaging and tomography, linear algebra makes it possible to rebuild three-dimensional structures from two-dimensional picture slices. This makes it possible to see internal details and diseases more clearly. Overall, combining linear algebra with image processing in medical electronics not only makes medical pictures better and easier to understand, but it also leads to new diagnosis tools and treatment plans. Employing mathematical methods to pull useful data from large sets of images helps healthcare professionals make better choices, which ultimately leads to better patient results and progress in medical study.

Pregnancy Planning and Genetic Testing: Exploring Advantages, and Challenges.

Pregnancy planning and genetic testing (PPGT) has emerged as a tool in reproductive healthcare, offering parents-to-be insight in their risks of having a child with a genetic disorder. This paper reviews the advantages, drawbacks and challenges associated with PPGT, providing some practical guidance for health care professionals. Advantages include identification of genetic risks, a possibility to informed reproductive decision-making, and the potential to reduce the parents-to-be risk for an affected child. Challenges and drawbacks include provision of service, ethical considerations, genetic counselling complexities, and the need to increase public and professional awareness by comprehensive education and accessibility. Practical guidance involves considerations for selecting appropriate candidates, counselling strategies, and how to integrate PPGT into existing healthcare frameworks. By addressing these factors, PPGT can offer an increased reproductive informed choice for the individual and the couple reducing the burden of disease in the family.