Clinical Environment Research Articles

The educational effects of telemedicine training using role‐playing for general practice/family medicine residents: A qualitative study

AbstractBackgroundDespite the increasing demand for telemedicine, there have been few reports on telemedicine training for general practice/family medicine residents. This study aimed to qualitatively examine the educational effects of remote medical training for residents using role‐play.MethodsThis study targeted first‐ and second‐year residents enrolled in the General Medicine/Family Medicine Residency Program at Fukushima Medical University in 2023. The residents watched educational videos on telemedicine and engaged in role‐playing training based on multiple scenarios. Subsequently, interviews were conducted with the residents, and the verbatim transcripts of the audio data were thematically analyzed using open coding.ResultsEight residents participated in the study, with each undergoing 3–4 interviews. The identified codes were inductively summarized, and nine categories were generated: verbal cues to enhance the quality of history taking, nonverbal communication to connect with patients, addressing risks inherent in the convenience of telemedicine, co‐creating a clinical environment with patients, anticipating issues different from those encountered in face‐to‐face consultation rooms, considering and supporting the elderly and those who are digitally disadvantaged, improving access restrictions due to busyness and resistance to telemedicine, understanding the living environment in connection with patient families and home care nurses, and awareness of the wide‐ranging applications of telemedicine.ConclusionThe results of telemedicine training via role‐play suggest various educational effects. This study provides crucial findings for considering educational methods for GM/FM residents to respond to the increasing demand for telemedicine in primary health care.

Sub-Second Optical Coherence Tomography Angiography Protocol for Intraoral Imaging Using an Efficient Super-Resolution Network.

This study introduces a 200 kHz swept-source optical coherence tomography system-based fast optical coherence tomographyangiography (OCTA) protocol for intraoral imaging by integrating an efficient Intraoral Micro-Angiography Super-Resolution Transformer (IMAST) model. This protocol reduces acquisition time to ~0.3 s by reducing the spatial sampling resolution, thereby minimizing motion artifacts while maintaining a field of view and image quality. The IMAST modelutilizes a transformer-based architecture combined with convolutional operations to reconstruct high-resolution intraoral OCTA images from reduced-resolution scans. Experimental results from various intraoral sites and conditions show the model's robustness and high performance in enhancing image quality compared to existing deep-learning methods.Besides, IMASTshows advantages in model complexity, inference time, and computational cost, underscoring its suitability for clinical environments. These findings support the potential of our approach for noninvasive oral disease diagnosis, reducing patient discomfort and facilitating early detection of malignancies, thus serving as a valuable tool for oral assessment.

Toward accessible MRI: SDR4MR, a simple RF pulse monitoring technique using an inexpensive software-defined radio.

This study evaluated the applicability and performance of the SDR4MR method at 1.5 T and 3T across different acquisition scenarios in a clinical environment. The SDR4MR hardware consists of a broadband receiver coil connected to a software-defined radio (SDR) via optional RF attenuators. The SDR stick is plugged into the computer's USB port, which runs the SDR software and a Mathematica script to decode the RF pulse sequence. Several MRI pulse sequences were recorded: (i) a multi-echo multi-slice spin echosequence to check the SDR4MR configuration on a well-known simple sequence; (ii) 2D and 3D sequences for which detailed information is not available in the user interface. The measured RF pulse sequences have been drawn in the style of illustrations found in MRI textbooks. Sequence times and amplitudes were estimated, and sequence details not described in the MRI user interface were retrieved. The present study demonstrated the implementation of SDR4MR on clinical scanners. This easy-to-use configuration enables precise monitoring of RF pulse sequences. This method could be further improved by taking advantage of advances in SDR hardware and software.

Optimizing prediction of metastasis among colorectal cancer patients using machine learning technology

Background and aimColorectal cancer is among the most prevalent and deadliest cancers. Early prediction of metastasis in patients with colorectal cancer is crucial in preventing it from the advanced stages and enhancing the prognosis among these patients. So far, previous studies have been conducted to predict metastasis in colorectal cancer patients using clinical data. The current research attempts to leverage a combination of demographic, lifestyle, nutritional, and clinical factors, such as diagnostic and therapeutical factors, to construct an ML model with more predictive insights and generalizability than previous ones.Materials and methodsIn this retrospective study, we used 1156 CRC patients referred to the Masoud internal clinic in Tehran City from January 2017 to December 2023. The chosen machine learning algorithms, including LightGBM, XG-Boost, random forest, artificial neural network, support vector machine, decision tree, K-Nearest Neighbor and logistic regression, were utilized to establish prediction models for predicting metastasis among colorectal cancer patients. We also assessed features based on the best-performing model to improve clinical usability. To show the generalizability of the established prediction model for predicting CRC metastasis, we leveraged the data of 115 CRC patients from Imam Khomeini Hospital in Sari City. We assessed the predictive ability of LightGBM as the best-performing model based on external data.ResultsThe LightGBM model with a PPV of 97.32%, NPV of 84.67%, sensitivity of 83.14%, specificity of 93.14%, accuracy of 88.14%, F1-score of 87.51%, and an AU-ROC of 0.9 0.01 obtained satisfactory performance for prediction purposes on this topic. Factors including the history of IBD, family history of CRC, number of lymph nodes involved, fruit intake, and tumor size were considered as more strengthful predictors for metastasis in colorectal cancer and clinical usability. The external validation cohort showed a PPV of 0.8, NPV of 0.85, sensitivity of 0.78, specificity of 0.86, accuracy of 0.834, F1-score of 0.795, and AU-ROC of 0.770.03, demonstrating satisfactory generalizability when leveraging external data from other clinical settings.ConclusionThe current empirical results indicated that LighGBM has predictive competency that can be leveraged by physicians in clinical environments for early prediction of metastasis and enhanced prognosis in patients with colorectal cancer.Clinical trial numberNot applicable.

Stability of monkeypox virus on commonly contacted surfaces in clinical settings

Abstract Monkeypox virus (MPXV), the etiologic agent of mpox, is an enveloped DNA virus that may persist on various surfaces, contributing to fomite-mediated transmission. The ongoing global outbreak of mpox has highlighted an urgent need to understand the environmental stability of MPXV. This study investigates the stability of MPXV on surfaces encountered in clinical settings. The persistence of viable MPXV and viral DNA was evaluated using porous (gauze, cotton, and scrubs), and non-porous (stainless steel, polypropylene plastic, IV tubing, N95 masks, and nitrile gloves) materials. Surfaces were inoculated with 105, 106, and 107 TCID50 MPXV and incubated at room temperature (22°C) and 4°C for up to 21 days to determine the effect of temperature and inoculum titre on virus viability. We show that MPXV stability is influenced by both surface type and temperature, with non-porous surfaces and lower temperatures supporting longer virus viability. Infectious MPXV was detected for up to 21 days on IV tubing and nitrile gloves at 4°C, while porous materials like cotton showed rapid loss of infectivity, especially at room temperature. Notably, we found that viral DNA did not correlate with the presence of infectious virus, suggesting that molecular assays may overestimate fomite-mediated transmission risks. Our findings provide novel insights into MPXV persistence in clinical environments, extending prior knowledge by systematically quantifying virus viability across multiple surface types and temperature conditions. These findings underscore the importance of stringent decontamination protocols in clinical settings, and highlight the need for comprehensive methods for risk assessment to evaluate the potential for MPXV transmission from contaminated surfaces.

“they evaluate us based on the questions meant for registrar”: Exploration of Transitional Opportunities Experienced by Intern Doctors

Introduction: The transition between being an intern doctor before moving into the medical work force is a crucial phase in the life of an intern doctor. The transitional experience is a phase of learning, exploration and adaptation for intern doctors. This study aimed to build insight into the opportunities experienced by intern doctors in Vanuatu during the internship transition. Methods: A qualitative study was carried out in two referral Hospitals in Vanuatu, Vila Central Hospital and Northern Provincial Hospital. A convenient sample of 27 intern doctors participated through face to face semi-structured in-depth interviews. The participants comprised of current intern doctors or those that had completed internship within the past two years and were currently working in the two referral hospitals in Vanuatu. To help direct in-depth interviews, a semi-structured open-ended questionnaire was employed. Thematic analysis was used to interpret the verbatim transcription of the interview material. Results: Six themes were described as opportunities which are; confidence gain in the work place, clinical competency, impact of supporting learning tools, impact of medical consultants, clinical learning environment, and developing interaction relationship. These opportunities have allowed the interns to become more efficient clinicians to make right decisions and when dealing with stressful and non- ideal situations. Conclusions: The findings of this study indicate that the transitional opportunities experienced by medical intern doctors will enable them to improve their proficiency level thus improving the clinical outcomes.

Beyond Workarounds: Enhancing Education, Care, and Wellness on Inpatient Medicine Rotations -A Multicenter Qualitative Study.

Inpatient medicine rotations (IMRs) aim to deliver exceptional clinical education and high-quality patient care. However, increasing workloads and the fast pace of inpatient wards are undermining this dual objective. To explore tensions and challenges between balancing education and clinical practice on IMRs and how physician-leaders are addressing them. Constructivist grounded theory. Inpatient medicine rotation physician-leaders from academic medical centers in the United States and Canada. Data collection involved semi-structured individual and group interviews, collected and analyzed iteratively to develop an explanatory conceptual model. Rigor was enhanced through constant comparison, investigator triangulation, and return-of-findings sessions. Twenty interviews involving 27 participants from 20 distinct training programs were conducted. Participants endorsed IMRs unique clinical and educational value. However, they flagged how increasing workloads and resource challenges produce tensions that can undermine the quality of both which, as a consequence, negatively impact attending and trainee wellness. While reactionary "workarounds" were the norm, they often created unanticipated problems. Key IMR features and strategies for success were identified and organized into six categories: (1) patient mix/census; (2) leadership collaboration; (3) collaborative care models; (4) rotation scheduling; (5) clinical workflow; (6) educational workflow. How physician-leaders configured their IMR structures and processes within these categories had the potential to support or undermine the delivery of high-quality care, education, and wellness. Inpatient medicine rotations, which is essential for clinical care and education, are currently facing serious challenges from a changing clinical and educational landscape. Our findings present a conceptual model highlighting key modifiable variables, giving physician-leaders a framework to assess and enhance their IMR's clinical learning environment, thus fostering quality care, education, and clinician wellness.

Restoring the Clinical Learning Environment at Teaching Hospitals in Post-Assad Syria: A PHEEM Analysis and Recommendations

Introduction After 54 years of Assad’s family dictatorship, Syria was finally free but, nonetheless, was in ruins. The health profession education training system is no exception, and there have been many indicators of this in the literature, but none of them have provided a systematic evaluation of the clinical learning environment (CLE) using validated approaches. Therefore, this study aimed to evaluate CLE at teaching hospitals in Damascus, Syria. Methods This cross-sectional study was conducted in Damascus, Syria, during mid-2023, before Assad’s regime collapsed. The study population included all resident doctors working and training at any teaching hospital in Damascus. We used the recently validated 36-item PHEEM inventory as a data collection tool and added 10 extra items related to Syria’s specific context. There was also one open-ended question. We recruited participants from 14 different teaching hospitals using nonprobability sampling techniques. Results A total of 1490 residents from 31 medical specialties participated in the study, which was approximately 37% of the total population at that time. The female participants comprised 50.7% (n=754) of the total sample. The Cronbach’s alpha was 0.925. The total PHEEM mean score was 72.4±21.4 (Max. 144). All the PHEEM domains showed significant shortcomings, except for learner engagement and social participation. The worst-scoring domains were external regulation, work culture, and living conditions, with scores of 48.5%, 40.8%, and 31.2%, respectively. The results of the additional 10 war-related items were important. One item showed that only 14% of respondents did not have plans to migrate. The written comments re-echo some of the PHEEM findings in more context. Discussion This study draws a roadmap for clinical educators, lawmakers, and new leaders to make targeted reforms and investments to restore the clinical learning environment. There are major issues that not only render training residents suboptimal but also compromise residents' and patients’ safety.

Quantitative sensory testing in dogs with spontaneous osteoarthritis

ObjectiveTo investigate changes in somatosensory sensitivity in dogs with spontaneous osteoarthritis (OA) and pain of the stifle or hip, compared to a group of healthy control dogs.Study designA non-randomised, non-blinded, prospective research study.Animals30 control, 51 OA-pain, and 31 OA-pain dogs receiving NSAIDsMethodsA range of noxious and non-noxious quantitative sensory testing (QST) modalities were applied. Dogs were tested twice, one month apart. Two sites were tested at each visit: a distal site located on the cranial aspect of the mid metatarsus and a primary site, lateral to the patella (in dogs with stifle OA) or craniodorsally to the greater trochanter (in dogs with coxofemoral OA). Control dogs were tested at appropriate primary sites to produce the same proportion of animals being tested at stifle or hip as those in the OA group. The order in which non-nociceptive and nociceptive tests were performed was randomized for each test site for each animal, although nociceptive tests were always performed after non-nociceptive tests. Feasibility for performing the tests was assessed for the final 45 dogs recruited to the study. The hierarchical structure of the QST testing data was accounted for within the statistical analysis by employing general linear modelling within a multilevel modelling framework using the MLwiN statistics package.ResultsOsteoarthritis category was not a major determinant of QST outcome measures for the majority of modalities evaluated. In the few modalities in which OA category was determined to be a significant predictor variable, the results were not consistent with previously reported data. The novel, non-nociceptive tests employed overall suggested non-noxious hypoesthesia in association with OA pain. The feasibility of performing QST assessments was relatively low compared to previous studies.Conclusionsand clinical relevance: In a clinical environment, the variability in feasibility of performing QST between dogs may be sufficient to confound changes in QST outcome measures associated with spontaneous OA.

Pneumonia Detection Using Deep Learning

Pneumonia is a major global health challenge requiring timely and accurate diagnosis to enhance patient outcomes. This study introduces a Convolutional Neural Network (CNN)-based model trained on labeled chest X-ray datasets to classify images as either pneumonia-positive or normal. By employing advanced techniques such as transfer learning and fine-tuning of pre-trained models, the system achieves robust feature extraction, ensuring exceptional diagnostic accuracy. Preprocessing steps, including image normalization, data augmentation, and contrast enhancement, are applied to improve the model’s performance and generalizability across diverse datasets. The evaluation metrics, including accuracy, precision, recall, and F1-score, affirm the model’s reliability, often matching or surpassing the diagnostic efficiency of human radiologists. This AI-driven diagnostic tool offers transformative potential for healthcare by reducing diagnostic time, assisting medical professionals, and addressing expertise shortages in resource-constrained areas. Future objectives include extending the model’s capabilities to detect multiple lung diseases and implementing it in clinical environments for continuous assessment and improvement. Index Terms - Pneumonia Detection, Convolutional Neural Network, Chest X-rays, Transfer Learning, Image Preprocessing, Deep Learning, Diagnostic Tool, Healthcare AI, Data Augmentation, Lung Disease Detection.

Support needed by nursing students to develop professional dignity.

BackgroundNursing students' professional dignity development during work-integrated learning is dependent on support from professional nurses. If they are left unsupported, such development is jeopardised.AimThe aim of the study was to explore and describe the support that nursing students need from professional nurses, including their lecturers, during work-integrated learning to develop professional dignity.Research designA qualitative, exploratory-descriptive research design applied. Through volunteer sampling, participants were invited for face-to-face in-depth individual interviews to discuss the question: 'What support did you need from others during work-integrated learning to feel dignified as nursing students?' Saturation of data determined the number of participants. Recording of the interviews and the writing of field notes were carried out with the permission of the participants. Manual coding in a thematic analysis was done to analyse the intricate data content with intuition and insight.Participants and research contextThe study was undertaken in South Africa at a designated nursing education institution and the hospital where the students performed the bulk of work-integrated learning. Fourteen third-year students were interviewed. They had sufficient experience with work-integrated learning and could provide rich data concerning support needed to develop professional dignity.Ethical considerationsThe Faculty of Health Sciences Research Ethics Committee at the University of Pretoria approved the proposal (Reference number 73/2023) and the applicable authorities gave written permission for the research to be conducted. Since the participants were students, the researchers made sure that they did not feel obliged to participate.FindingsFour categories were identified, namely, (a) improving work-integrated learning experience, (b) value students' professional development, (c) cooperate to benefit students' professional growth and (d) manage resources optimally.ConclusionStudents needed to be respected and their input to quality nursing care acknowledged. Positive role models and learning conducive clinical environments contributed to their development.

Non-Invasive Hemoglobin Monitoring Using Optical Sesnors

Non-invasive hemoglobin monitoring through spectroscopy has emerged as a groundbreaking method in medical diagnostics, facilitating real-time, painless, and continuous evaluation of hemoglobin levels without the necessity for invasive blood draws. This approach primarily utilizes optical techniques, including Near-Infrared Spectroscopy (NIRS), Pulse CO-Oximetry, and the combination of optical and plethysmography sensors to measure hemoglobin concentration based on tissue absorption and scattering characteristics. These techniques offer significant benefits in clinical environments, especially for patients who require regular monitoring, such as those suffering from anemia, chronic kidney disease, or those undergoing surgical procedures. This paper examines the underlying principles, technologies, clinical applications, and future potential of non-invasive hemoglobin monitoring through spectroscopy. Keywords: Non-invasive hemoglobin monitoring, optical sensors, real-time monitoring, anemia, medical diagnostics.

A Comparative Study of Nursing Students’ Perceptions of Clinical Learning Environments in Urban and Semi-Urban Regions of Pakistan

Background: Clinical learning environments (CLEs) are pivotal in shaping nursing competencies. In Pakistan, disparities in mentorship, infrastructure, and resource allocation across regions influence student perceptions significantly.Objective: This study compares nursing students’ perceptions of CLEs between urban (Karachi) and semi-urban/rural hospitals (Hyderabad, Khairpur, Larkana) and explores the factors underpinning these differences.Methods: A cross-sectional survey using the Clinical Learning Environment Inventory (CLEI) was administered to 120 BScN students. Both quantitative (independent t-tests, ANOVA via SPSS v26) and qualitative methods (thematic analysis) were employed.Results: Karachi students reported notably higher satisfaction regarding mentorship (mean = 3.5 vs. 2.9, p = 0.02), resource availability (3.8 vs. 2.6, p < 0.01), and peer collaboration (4.0 vs. 3.2, p = 0.03). Graphical representations reveal distinct clusters for urban and semi-urban responses. Qualitative findings highlighted themes such as the need for better supervision and reduced non-academic workload in semi-urban areas.Conclusion: Urban CLEs currently outperform semi-urban settings, largely due to superior infrastructure and structured mentorship. Policy reforms focusing on faculty training, resource allocation, and workload management are recommended to standardize educational outcomes across regions.

Medical MLLM Is Vulnerable: Cross-Modality Jailbreak and Mismatched Attacks on Medical Multimodal Large Language Models

Security concerns related to Large Language Models (LLMs) have been extensively explored; however, the safety implications for Multimodal Large Language Models (MLLMs), particularly in medical contexts (MedMLLMs), remain inadequately addressed. This paper investigates the security vulnerabilities of MedMLLMs, focusing on their deployment in clinical environments where the accuracy and relevance of question-and-answer interactions are crucial for addressing complex medical challenges. We introduce and redefine two attack types: mismatched malicious attack (2M-attack) and optimized mismatched malicious attack (O2M-attack), by integrating existing clinical data with atypical natural phenomena. Using the comprehensive 3MAD dataset that we developed, which spans a diverse range of medical imaging modalities and adverse medical scenarios, we performed an in-depth analysis and proposed the MCM optimization method. This approach significantly improves the attack success rate against MedMLLMs. Our evaluations, which include white-box attacks on LLaVA-Med and transfer (black-box) attacks on four other SOTA models, reveal that even MedMLLMs designed with advanced security mechanisms remain vulnerable to breaches. This study highlights the critical need for robust security measures to enhance the safety and reliability of open-source MedMLLMs, especially in light of the potential impact of jailbreak attacks and other malicious exploits in clinical applications. Warning: Medical jailbreaking may generate content that includes unverified diagnoses and treatment recommendations. Always consult professional medical advice.

Implementing DPYD genotyping to predict chemotherapy toxicity in Australia: a feasibility study.

Implementing pharmacogenomic-guided management in cancer patients equitably and effectively in a large population presents challenges. DPYD genotyping determines clinically significant variants of patients at increased risk of developing grade3-5 fluoropyrimidine (FP) toxicity. FP chemotherapies are prescribed for ~16,000 Australians with a 10%-40% grade3-4 toxicity incidence and 1% mortality. Variant carriers can have FP dosing adjusted to improve treatment tolerance without compromising anticancer effect. This strategy has not been formally adopted within Australia, despite widespread international standardisation. This pilot study determined genotyping turnaround-times (TAT) for 4 DPYD variants (c.1905+1G>A, c.1679T>G, c.2846A>T and c.1236G>A/Haplotype B3) in Australian patients. Secondary objectives were identification of FP toxicities of DPYD variant carriers, and analysis of healthcare stakeholder perspectives, including enablers/barriers to implementation. Genotyping was determined by Real-Time Polymerase Chain Reaction. Qualitative data were determined through semi-structured questionnaire. 104 patients recruited over 24 months had a mean TAT of 7.2 days, 5.2 business days (range 1-30). Grade3-4 toxicity occurred in 9/16 DPYD variant carriers, including 2 ICU admissions and 1 death. Themes from 30 questionnaire respondents suggest that clinical environment and resources were fundamental barriers, and motivation to improve patient care was the predominant enabler of change. DPYD genotyping is feasible for improving precision-oncology for patients requiring FP chemotherapies. A TAT of 7 days is acceptable by both stakeholder respondents and national oncology clinician groups. This pilot study, although small, informs a large national project evaluating prospective DPYD genotyping and its impact on FP tolerability, patient safety and cost-effectiveness in Australia.

Comparison of Bioelectric Signals and Their Applications in Artificial Intelligence: A Review

This review examines the role of various bioelectrical signals in conjunction with artificial intelligence (AI) and analyzes how these signals are utilized in AI applications. The applications of electroencephalography (EEG), electroretinography (ERG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG) in diagnostic and therapeutic systems are focused on. Signal processing techniques are discussed, and relevant studies that have utilized these signals in various clinical and research settings are highlighted. Advances in signal processing and classification methodologies powered by AI have significantly improved accuracy and efficiency in medical analysis. The integration of AI algorithms with bioelectrical signal processing for real-time monitoring and diagnosis, particularly in personalized medicine, is emphasized. AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies.

TAME Pain data release: using audio signals to characterize pain

Accurately assessing pain through speech remains a challenge in medical practice, with profound implications for patient care and patient health outcomes. The TAME Pain dataset addresses this challenge by providing a comprehensive dataset that captures the relationship between induced acute pain and speech in adults. Utilizing the Cold Pressor Task (CPT) method to induce pain, we recorded over 7,000 utterances from 51 participants, correlating their self-reported pain levels with vocal cues. This dataset stands as the largest of its kind to date and includes comprehensive annotations detailing background noise, speech errors, and non-speech vocal features, maximizing its utility for in-depth audio analysis. Our dataset is designed to support the development of reliable, non-invasive pain assessment technologies, particularly in telemedicine and remote healthcare settings. By releasing these data, we aim to facilitate interdisciplinary research in psychology, medical science, and AI, fostering innovations that can enhance pain management practices and improve patient outcomes across diverse clinical environments.

Optimizing Healthcare Scheduling Through Machine Learning: A Role-Based Doctor Appointment System

Improving access to healthcare services requires efficient digital solutions, particularly for managing doctor appointments. This study presents an advanced Healthcare Management System (HMS), a full-stack web application aimed at streamlining the appointment scheduling process in clinical environments. Built on the MERN stack—MongoDB, Express.js, React (with Vite), and Node.js—paired with Tailwind CSS for responsive design, the platform offers an intuitive interface for seamless user interaction. The system incorporates dedicated dashboards tailored to three user roles: Administrator, Doctor, and Patient, each providing secure, role-specific functionalities. Security is fortified through JWT-based authentication and Bcrypt.js for password encryption. The appointment module enables dynamic scheduling, real-time updates, and automated confirmations, with doctors managing their availability directly. Admins are equipped with tools for managing users, professionals, and appointments, alongside integrated analytics. Patients benefit from easy booking, flexible time selection, and secure transactions via Stripe and Razor pay gateways. The system’s development process emphasized maintainability and robustness through tools such as Visual Studio Code, GitHub, and Thunder Client. This HMS offers a scalable, secure, and intelligent approach to healthcare appointment management, with the potential for future integration of machine learning to enhance predictive scheduling and resource optimization. INDEX TERMS Healthcare Management System (HMS), Doctor Appointment Scheduling, MERN Stack, Machine Learning, Web Application, Role-Based Access Control, JWT Authentication, Stripe, Razor pay, MongoDB, React.js, Vite, Node.js, Express.js, Tailwind CSS, Secure Transactions, Real-Time Scheduling, Patient Portal, Admin Dashboard, Scalable Web Architecture.

Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures

Thyroid diseases, including hypothyroidism, hyperthyroidism, thyroid nodules, thyroiditis, and thyroid cancer, are among the most prevalent endocrine disorders, posing significant health risks, which need to be diagnosed and treated promptly. Traditional diagnostic approaches, reliant on manual interpretation of medical images, are time-consuming and prone to errors. This study introduces a novel deep learning framework utilizing advanced Convolutional Neural Networks (CNNs), specifically modified ResNet and InceptionV3 architectures, to improve the accuracy and efficiency of thyroid disease diagnosis. We present Dual-OptNet, a new hybrid deep learning architecture that effectively merges skip connections of ResNet with multi-scale feature extraction based on InceptionV3 for lung classification tasks. Dual-OptNet shows the most accurate and generalizability results in classifying the thyroid disease with an average and best classification accuracy of 97% from a dual-step optimized using Adam and SGD. Future work will focus on developing a real-time classification tool to demonstrate the potential utility of this model in a clinical context. Future work will also focus on enhancing the dataset to cover a wider range of uncommon thyroid cases, and incorporating explainable AI methods, so that the model decisions are more interpretable. Further research will also explore real-time ultrasound analysis and multi-modal data integration, such as combining medical images with patient history, to enhance diagnostic accuracy. Deploying the system in clinical environments will be key to validating its impact and scalability, ultimately contributing to more efficient and accurate healthcare solutions

Can large language models facilitate the effective implementation of nursing processes in clinical settings?

BackgroundThe quality of generative nursing diagnoses and plans reported in existing research remains a topic of debate, and previous studies have primarily utilized ChatGPT as the sole large language mode.PurposeTo explore the quality of nursing diagnoses and plans generated by a prompt framework across different large language models (LLMs) and assess the potential applicability of LLMs in clinical settings.MethodsWe designed a structured nursing assessment template and iteratively developed a prompt framework incorporating various prompting techniques. We then evaluated the quality of nursing diagnoses and care plans generated by this framework across two distinct LLMs(ERNIE Bot 4.0 and Moonshot AI), while also assessing their clinical utility.ResultsThe scope and nature of the nursing diagnoses generated by ERNIE Bot 4.0 and Moonshot AI were similar to the “gold standard” nursing diagnoses and care plans.The structured assessment template effectively and comprehensively captures the key characteristics of neurosurgical patients, while the strategic use of prompting techniques has enhanced the generalization capabilities of the LLMs.ConclusionOur research further confirms the potential of LLMs in clinical nursing practice.However, significant challenges remain in the effective integration of LLM-assisted nursing processes into clinical environments.