Medical Sector Research Articles

Additive manufacturing in the medical sector: from an empirical investigation of challenges and opportunities toward the design of an ecosystem model

PurposeThis works provides a thorough understanding of the challenges and opportunities associated with Additive Manufacturing (AM) adoption in the medical sector. Through this analysis, we aim to better understand when to adopt AM, how to do so, and how such adoption might change in the future.Design/methodology/approachThis research first conducted a systematic literature review (SLR) to identify AM challenges and opportunities in the medical sector, which were then validated through a Delphi study. The 18 Delphi study participants were also asked to suggest countermeasures for the challenges and help identify future AM adoption scenarios. Finally, these findings were analyzed according to the ecosystem pie model to design an ecosystem model for AM in the medical sector.FindingsAmong the 13 challenges and 13 opportunities identified, the lack of a skilled workforce and the responsiveness achievable via AM were by far the most relevant challenge and opportunity. Moreover, the participants identified countermeasures for 10 challenges, as well as three future AM adoption scenarios. Finally, leveraging these findings, an ecosystem model was developed.Originality/valueThis work contributes to the limited understanding of the AM challenges and opportunities in the medical sector. It helps medical practitioners to better understand the challenges and opportunities associated with AM and AM manufacturers to better identify where to focus their R&D efforts and how this would impact future AM adoption levels. Furthermore, this work extends current theory supporting the design of an ecosystem model for AM in the medical sector following the ecosystem pie model.

Revolutionizing diabetes care with innovative decision-making using cubic intuitionistic fuzzy Schweizer and Sklar power aggregation operators

The cubic intuitionistic fuzzy set is an expansion of the cubic fuzzy set that displays massive information to demonstrate interval-valued intuitionistic fuzzy sets and intuitionistic fuzzy sets. This increment informs limitations essential in existing frameworks, primarily focusing on the significance of embracing our access for more accurate decisions in compound and unresolved structures. The Schweizer and Sklar (SS) operations are engaged in promoting strong aggregation operators for cubic intuitionistic fuzzy sets through this research. Operators such as cubic intuitionistic fuzzy Schweizer and Sklar power weighted average (CIFSSPWA) and cubic intuitionistic fuzzy Schweizer and Sklar power weighted geometric (CIFSSPWG) are offered that enhance the workability of data aggregation within the cubic intuitionistic fuzzy (CIF) environment when compared to surviving methods. The proposed operators may assist in patient treatment and handling by upgrading decision-making in medical sectors like diabetes care. Moreover, to determine the stability and reliance of the outcomes, sensitivity and comparison studies are richly absorbed by this approach.

Decision-Making in Healthcare. A Bibliometric Exploration

Abstract The dynamic environment in which healthcare systems operate highlights the importance of the decision-making process, especially considering the fact that it influences the quality of services and the well-being of patients. The aim of this research is to examine the conceptual link between the concepts “decision”, “decision making process”, “healthcare”, and “management” and how they interact in the literature. Thus, the research aims to identify common trends and patterns in these areas. The research carries out scientific mapping using a three-stage methodological approach: bibliographic data collection through refined searches in the Web of Science database, data analysis using Biblioshiny, and subsequent bibliometric analysis of the 259 scientific documents collected. The main results of the analyses carried out illustrated an increase in scientific publications over time, especially in 2023, when most academic materials were published. International collaboration is in its infancy, with the US and China leading such collaborations. The theoretical implications are centered around the methodological approach to research in the healthcare field. Regarding the practical implications, the research provides information to decision-makers in the medical sector by identifying current contexts in the lives of organizations, but also to academics, with the research having the role of revealing publication sources and other information of interest to them.

Determinants of medical student's perception of general practice.

Enhancing the attractiveness of a career in general practice is a significant public health concern, given the current shortage in Belgium. The objective of this study is to determine the factors that influence students' decisions to pursue a career in general practice and their perceptions of it. This prospective study was conducted in May 2023. A questionnaire, based on existing literature, was distributed to final-year medical students. Quantitative data were analysed using descriptive and bivariate tests (chi2 test). Out of the 142 participating students (response rate: 45%), 29.3% expressed a preference for general practice, while 70.7% opted for other specialties. These groups exhibited significant differences (p < 0.05) not only in terms of socio-demographic factors (nationality, parent's education) but also in their perceptions of general practice. The latter group associated it with routine work (89.2%) and feelings of loneliness (58.5%), in contrast to the former (36.1% and 24.3%, respectively). Moreover, 76.6% of students reported a shift in their perception of general practice during their medical education. General practice internships (71.9%), GPs (78.2%), and interactions with patients (68.9%) had a positive influence on their perception, while instructors in hospital settings (72.9%) and peers (37%) often portrayed general practice as a less favourable career option. Perceptions of general practice are significantly shaped by interactions with role models from the medical sector who convey either a positive or negative image of general practice. To address the shortage issue, it is imperative to increase positive interactions with GPs within the curriculum and work towards changing the institutional culture at this level.

Analysis of GDPR Compliance in Utilizing Personal Data for Public Health Purposes in Romania.

The General Data Protection Regulation (GDPR), which became effective on May 25, 2016, underscored the significance of confidentiality across various economic and social domains. Within the medical sector, confidentiality of patient health information is meticulously governed by laws, e.g., no. 95/2006 and no. 46/2003. While these laws address numerous privacy aspects within the doctor-patient relationship, it becomes necessary to update them to align with the latest advancements in emerging technologies, particularly in the context of telemedicine. Upon reviewing the overview of rules pertaining to health data processing in Romania, as published by the European Data Protection Board (EDPB) in 2021, and comparing it with the current public health and research laws in Romania, it becomes apparent that there is a regulatory gap concerning the secondary use of health data. This gap is particularly notable in terms of planning, managing and enhancing the healthcare system, as well as utilizing such data for scientific and historical research purposes, leading to the necessity of developing and regulating the European Health Data Space. Although steps have been taken to align the GDPR legislation in Romania, there is still a disproportionality in the regulation of privacy and cyber security with the implementation of new technologies that will collect, process and store sensitive medical data.

Innovations in Starch Based Bioplastics: A Comprehensive Review of Methods and Applications

The depletion of fossil fuel resources and their associated effects, such as greenhouse gas emissions, global warming, and environmental pollution, have prompted researchers to explore the bioeconomy in innovative ways. The concepts of sustainability and the circular economy have shifted the focus of research from mere development to development that also prioritizes environmental protection, known as sustainable development. Conventional plastics, derived from petroleum, can take hundreds of years to degrade naturally, thus harming the local environment. Bioplastics, which are biopolymers sourced from biological materials, offer the advantage of biodegradability in natural environments and are considered a virtuous replacement for conventional plastics. Starch, an abundant and low-cost biopolymer, is a promising material for bioplastics. However, starch-based bioplastics often fall short in practical applications due to limited mechanical and barrier properties. Effective solutions include copolymerization, the addition of fillers, plasticization, and chemical modification. This review focuses on starch-based bioplastics, enhanced with cellulose, polylactic acid, and polyhydroxyalkanoates, and their applications in food packaging, medical, and electronic sectors.

Digital Technologies in the Healthcare Industry: Literature review

Digital transformation is much needed in the healthcare sector to maximize the efficiency and productivity of human health. Adding technologies is an important addition to the continuity of growing the industry, and it is a promising opportunity for human care growth; This research conjectured that while healthcare is a vibrant and continuously evolving field, it becomes essential to present a detailed categorization for the main recent technologies used in the healthcare. To do this conceptual research, we reviewed a deal of existing research, about 31 research papers, that share common characteristics. The papers chosen for review are all written in English language, published in the last three years; From 2021 to 2023. And shown by Google Scholar, IEEE, PubMed and Emerald Insight. Our study presented a more unambiguous categorization. Existing literature showed three main categories, namely Preoperative; Intraoperative; And Post-operative. This research suggests adding a new one category related to Inorganic illness to address psychological and relaxation aspects, which need access to devices in each psychological state and relaxation; Preoperative; Intraoperative; And Post-operative. To conclude, technology has a great role in increasing efficiency and accuracy and reducing material and human losses. The medical sector seeks for more extended, and effective use of technology leverage the quality of life without suffering pain. This is certainly besides the economic prosperity of the medical infrastructure.

Phytofabrication of biocompatible chitosan-based ZnO nanocomposite aided by Cissus quadrangularis extract enriched with antimicrobial and antioxidant potential

A dynamic chitosan-based ZnO nanocomposite (NC) was fabricated via a cost-effective formulation and an eco-friendly procedure utilizing Cissus quadrangularis (CQ) plant extract. This study investigates the antimicrobial and antioxidant properties, together with the cytocompatibility aspects of chitosan-incorporated ZnO nanocomposite (CS-ZnO/CQE). The formation and structural morphology of the nanocomposites were examined using FTIR, UV–Vis, XRD, XPS, BET, TGA, SEM, and TEM techniques. The antibacterial test results demonstrated the greatest inhibitory zone diameter against S. aureus (19 ± 1.00 mm) and E. coli (17 ± 1.05 mm), assessed through agar well diffusion method. Also, the composite exhibited a DPPH inhibition rate of 78.7 ± 0.34 %, indicating its high effectiveness in neutralizing free radicals. In addition, the nanocomposite exhibited less toxicity towards human erythrocytes, HDF and HEK-293 cells as a result of the biocompatibility exhibited by CS, ZnO, and CQ plant extract. Likewise, it has exceptional cell migratory capacity and possesses biodegradability factors. These observations strongly suggest the potential of CS-ZnO/CQE as a cutting-edge antibacterial and antioxidant agent to be implemented in the medical sector.

Inversion of Glycyrrhiza Chlorophyll Content Based on Hyperspectral Imagery

Glycyrrhiza is an important medicinal crop that has been extensively utilized in the food and medical sectors, yet studies on hyperspectral remote sensing monitoring of glycyrrhiza are currently scarce. This study analyzes glycyrrhiza hyperspectral images, extracts characteristic bands and vegetation indices, and constructs inversion models using different input features. The study obtained ground and unmanned aerial vehicle (UAV) hyperspectral images and chlorophyll content (called Soil and Plant Analyzer Development (SPAD) values) from sampling sites at three growth stages of glycyrrhiza (regreening, flowering, and maturity). Hyperspectral data were smoothed using the Savitzky–Golay filter, and the feature vegetation index was selected using the Pearson Correlation Coefficient (PCC) and Recursive Feature Elimination (RFE). Feature extraction was performed using Competitive Adaptive Reweighted Sampling (CARS), Genetic Algorithm (GA), and Successive Projections Algorithm (SPA). The SPAD values were then inverted using Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost), and the results were analyzed visually. The results indicate that in the ground glycyrrhiza inversion model, the GA-XGBoost model combination performed best during the regreening period, with R2, RMSE, and MAE values of 0.95, 0.967, and 0.825, respectively, showing improved model accuracy compared to full-spectrum methods. In the UAV glycyrrhiza inversion model, the CARS-PLSR combination algorithm yielded the best results during the maturity stage, with R2, RMSE, and MAE values of 0.83, 1.279, and 1.215, respectively. This study proposes a method combining feature selection techniques and machine learning algorithms that can provide a reference for rapid, nondestructive inversion of glycyrrhiza SPAD at different growth stages using hyperspectral sensors. This is significant for monitoring the growth of glycyrrhiza, managing fertilization, and advancing precision agriculture.

A new extended Pham distribution for modelling cancer data

In this study, a novel extended Pham model is offered and examined. This model can fit positive, negative, and approximately symmetric data. In addition, it features four alternative hazard rate shapes, namely increasing, decreasing, bathtub, and upside-down bathtub shapes. The new model's main properties, such as quantiles, moments, and mean deviations, are derived. To evaluate the estimation of distribution parameters, two approaches are studied. From a classical standpoint, the maximum likelihood technique is used to determine the point and approximate confidence intervals of all unknown parameters. The Bayesian estimation method, on the other hand, is considered via the Markov chain Monte Carlo technique for obtaining Bayes point estimates alongside Bayes credible intervals. We present various Monte Carlo simulation studies to investigate the efficacy of maximum likelihood and Bayes estimation findings for the model's parameters. Numerical investigations showed that the offered Bayes estimates outperformed those derived using the conventional method. We also analyzed two sets of real data taken from the medical sector; the first represents survival rates for cancer patients who received a combination of radiotherapy and chemotherapy, and the second consists of remission times for bladder cancer patients. These clinical scenarios reveal that the new model outperforms other important competing models based on specific statistical criteria. Ultimately, for both clinical data applications, the proposed extended Pham distribution is the best choice compared to the traditional Pham and the other well-known eight lifetime models.

Deep Learning-Based Nystagmus Detection for BPPV Diagnosis.

In this study, we propose a deep learning-based nystagmus detection algorithm using video oculography (VOG) data to diagnose benign paroxysmal positional vertigo (BPPV). Various deep learning architectures were utilized to develop and evaluate nystagmus detection models. Among the four deep learning architectures used in this study, the CNN1D model proposed as a nystagmus detection model demonstrated the best performance, exhibiting a sensitivity of 94.06 ± 0.78%, specificity of 86.39 ± 1.31%, precision of 91.34 ± 0.84%, accuracy of 91.02 ± 0.66%, and an F1-score of 92.68 ± 0.55%. These results indicate the high accuracy and generalizability of the proposed nystagmus diagnosis algorithm. In conclusion, this study validates the practicality of deep learning in diagnosing BPPV and offers avenues for numerous potential applications of deep learning in the medical diagnostic sector. The findings of this research underscore its importance in enhancing diagnostic accuracy and efficiency in healthcare.

Adaptive Critic Design for Event-Triggered Tracking Control in Discrete-Time on Linear Systems with Observers

This research pioneers an innovative methodology for the bespoke manufacturing of SquidSkin safety products, meticulously tailored to the distinct requisites of diverse industries encompassing medical, military, and defense sectors. The triple DES algorithm is a three instance of DES on same plain text in blocks of 64 bits and converts them to cipher text using keys of 168 bits. The triple DES encryption process creates cipher text, which is an unreadable, effectively indecipherable conversion of plaintext data, the version of information that humans can read and understand. The output of the encryption process, the triple DES cipher text, cannot be read until a secret triple DES key is used to decrypt it. The delineation of industry- specific manufacturing parameters mandates a nuanced approach to material composition and production processes, culminating in a paradigmatic advancement in safety product customization. Integral to this methodology is a rigorous analysis of industry-specific requirements, guiding the selection of optimal material compositions. This selection is underpinned by a meticulously designed material testing phase, ensuring adherence to exacting industry standards and performance benchmarks. Augmenting this paradigm is an imperative emphasis on fortifying the security framework underpinning the handling of proprietary manufacturing data. The proposal introduces an advanced cryptographic architecture, featuring encryption and decryption protocols. Approach assures heightened efficiency, unwavering reliability, and the preservation of proprietary information across varied industry sectors.

The Challenges of Blood Sample Delivery via Drones in Urban Environment: A Feasibility Study through Specific Operation Risk Assessment Methodology

In recent years, Unmanned Aircraft System (UAS) usage in the medical sector as an alternative to traditional means of goods transport has grown significantly. Even though the reduced response time achieved with UASs can be lifesaving in critical situations, their usage must comply with technological constraints such as range, speed and capacity, while minimizing potential risks. In this paper, the feasibility of a drone operation dedicated to the transport of blood samples in an urban area is studied through a safety risk analysis. The assessment utilizes the Specific Operation Risk Assessment (SORA) framework, in line with current European regulations, and extends it to define flight trajectories with minimal risk. A case study in the Helsinki urban area is used as a reference, with an exemplary case of commercial drone transportation of blood samples between the Töölö and Malmi Hospitals. By leveraging the drone performance capabilities and minimizing the risk for people on the ground, this approach demonstrates that medical delivery using drones in densely populated urban environments remains challenging. Nonetheless, it argues that the proposed method can enhance risk awareness and support the planning of feasible operations.

Искусственный интеллект в медицине: этические проблемы коммуникации между врачом и пациентом

A serious challenge to the patient-oriented model of interaction between doctor and patient is posed by objective circum-stances: the constantly increasing number of patients and rising healthcare costs and, therefore, the inability to provide the attention and care necessary for the patient. A likely response to the emerging "crisis of care" in medicine appears to be the use of digital technolo-gies in clinical practice and healthcare organization. In particular, one of the possible options for using the potential of digital technolo-gies in medicine is artificial intelligence systems (hereinafter referred to as AI).The article is devoted to the peculiarities of communica-tion between a doctor and patients in the conditions of using artificial intelligence in clinical practice. The author identifies both the prospects for the introduction of artificial intelligence and difficulties in the process of communication between a doctor and a patient. The purpose of the article is to analyze the mechanism of influence of artificial intelligence on the process of communication between a doctor and a patient, to assess the impact of digital technologies on the basic values that develop in the process of clinical communica-tion – respect for patient autonomy, trust, confidentiality. The main expected positive consequence of the use of AI in medicine is the freeing up of time for direct communication with the patient. However, there is a risk that it will be used to increase patient flow, espe-cially in the commercial medical sector. It is concluded that, despite the widespread use of digital technologies, society remains wary of such innovations, especially when it concerns personal data. As a result, the level of trust and willingness to use AI systems is low. When introducing AI, it is necessary to take into account the feasibility and appropriateness of using these technologies in the provision of medical care, since, along with the effectiveness and accuracy of diagnostic results and treatment, the process of communication with a doctor remains extremely important for the patient, the opportunity to share their medical history and, during this communica-tion, form trust necessary for joint decision-making.

IMPLEMENTING ADVANCED X-RAY TECHNOLOGIES AT THE THIRD CHERKASY CITY EMERGENCY HOSPITAL

Introduction: In recent decades, the world has seen a transition from traditional to digital radiography. Ukraine has also joined this process, replacing outdated equipment with modern digital systems that have become especially important during the Covid-19 epidemic and the Russian-Ukrainian war. The Third Cherkasy City Emergency Hospital implements modern digital technologies of X-ray diagnostics in order to improve the provision of medical care and diagnostics. This study aims at investigating and evaluating the experience of implementing digital X-ray technologies in The Third Cherkasy City Emergency Hospital. Materials and methods: This study involved analyzing medical records from The Third Cherkasy City Emergency Hospital and conducting individual interviews with eight healthcare professionals, including radiologists, other physicians, and a nurse from the hospital. Results. Beginning in 2017, due to the decentralization policy and changes in the Cherkassy city administration, there has been a gradual transition of the medical sector, particularly The Third Cherkassy City Emergency Hospital, to modern digital equipment. This transition has significantly enhanced the quality of medical services and streamlined the process of diagnosing and treating patients. The integration of digital X-ray technologies into the hospital's medical practice has expedited the acquisition and processing of X-ray images, reducing labor and time previously spent on film installation and processing. This reduction in personnel and equipment needs has led to cost savings and process optimization. The implementation of modern X-ray technology has notably improved the efficiency and workflow of hospital staff, facilitating faster and more accurate processing and analysis of X-ray images. The absence of chemical reagents and the utilization of high-quality, clear images have reduced physical and emotional stress among medical personnel. Furthermore, the technology has enabled more effective detection of various diseases at early stages and streamlined emergency care for injured patients. Additionally, the impact of X-ray radiation on both patients and medical staff has decreased. However, the adoption of digital technologies has brought forth new challenges, such as organizing storage and access to digital archives. Addressing these challenges necessitates additional investments in server equipment and the development of specialized programs. Ensuring the reliability and security of medical information storage is crucial to prevent potential data loss or corruption.

Comparative Analysis of Machine Learning Algorithms for Diabetic Disease Identification

This article introduces a comparative analysis of different types of machine learning algorithms (MLAs) used for diabetic disease identification. Today machine learning algorithms are a major role in solving and identifying the different type of diseases in the medical sector. In the early prediction of diabetic to easily treat physicians and protect from other diseases in patients seven types of MLAs such as support vector machine (SVM), decision tree (DT), logistic regression (LGR), Gradient boost method (GDBM), k-nearest neighbour (KNN), XG boost (XGBM) and random forest (RF) are used for diabetic identification. PIMA Indian diabetic dataset (PIMAIDD) is used to train and test the MLAs. Confusion matrix, accuracy (ACR), precision (PCN), recall (RCL), f1-score (FSC), receiver operating curve (ROC) and K-fold cross-validation are the metrics used for performance evaluation of MLAs and experiments are implemented by Jupyter notebook and python sci-kit libraries. Six types of test cases were conducted whereas test case 4 (70%-30%) was well performed in which RF reported better results in diabetic identification that differentiates from other machine learning metric scores.

Bioethical challenges in postwar development aid: The Rwandan case study.

This article considers aspects of a development aid that provides medical support to strengthen pediatric orthopedics in Rwanda. We present part of the Afriquia foundation work, a nonprofit foundation from Poland involved in supporting the medical sector in Rwanda as a sign of global solidarity and the human right to health. The main foundation's activity is the treatment of orthopedic problems among Rwandan citizens. We present a case study of two children under the care of the Afiquia foundation. 11-year-old Seraphine treated due to the consequences of right tibia osteomyelitis and 11-year-old Lavi suffering from osteogenesis imperfecta. Both children were treated surgically in Poland due to Rwanda's lack of treatment possibilities. After the applied treatment, Seraphine walks correctly without crutches and can attend school and thrive among her peers. Lavi has not sustained any fragility fracture since the surgery in Poland. He is healthy and constantly ongoing his rehabilitation including gait training. The described cases initiated development aid in Rwanda, supplying hospitals with orthopedic implants and training medical staff. The growing number of humanitarian crises across the globe and the people affected requires increasing organizations involved in providing relief. The emphasis should be on global education, aiming to make the recipients reflect and prepare them to face humanitarian crises.

Eco-friendly based stability-indicating HPTLC technique for the determination of Escitalopram and Etizolam by employing quality by design approach

AbstractThe two chemical components Escitalopram (ESC) and Etizolam (ETZ) are beneficial for the health of individuals because it helps to treat anxiety. The study mainly illustrated that a green approach is essential in the medical sector with the help of “Green Analytical quality by design”. According to AQbD, the techniques of HPTLC have become eco-friendly, and decided to use “ESC” and “ETZ”. Hence, ethanol and phosphate buffer pH 3.5 adjusted with 1% “orthophosphoric acid”. After the retardation factor, the product ESC was found at 0.34 min and ETZ was found at 0.53 min. The linearity of ETZ the range varies from 300 to 1800 μg mL−1 and for ESC it varies from 100 to 600 μg mL−1. The validation parameter of R2 Values ranged from 0.9997 to 0.9994 for both ESC and ETZ. The study also demonstrated that different other methods were also useful for the medical sector to make it more convenient and eco-friendly. Some of those approaches are “GAPI”, “AGMS”, “NEMI”, and “AGREE”. The outcome of the study helped to find that the technique “HPTLC” is a green analytic design that helps to maintain the stability of the medicine and it was also approved as a quality design and also a novel approach in the pharmaceutical sector.

Pricing in the medical sector: Theoretical foundations and practical aspects

Pricing is one of the components of ensuring competitive conditions in the market: it has its own specificity in the healthcare market, which is characterized by various forms of operation of enterprises, access to goods and services. The purpose of the study was to investigate the methods and approaches to this process in Ukraine, taking into account the peculiarities of the country’s development related to the full-scale invasion of the Russian Federation. The methods used in the study were formal and legal (to form logical links between individual regulatory documents) and comparison (to assess the specifics of pricing in the medical sector in different countries). The study assessed the pricing process as such and described the factors that influence it in practice in general and in the medical sector. It was also concluded that understanding the role of this process for both the state and potential clients is important, as it affects economic stability, quality of medical services, social justice. Particular attention was paid to such approaches as “cost+markup” and “market-based”. Based on the models built in this paper, it was concluded that these approaches should be used in symbiosis (to focus on both market conditions and the company’s goals for forming a margin). Attention was also paid to the Value-Based Pricing approach: it was concluded that it is inherently different from the other two approaches, as it requires much more complex estimates, since it is based on the expected utility of the created product. The study also highlighted the problem of non-transparent pricing as one of the main ones that prevents the formation of effective market conditions. The results obtained in the study can be used both to formulate public policy and to create strategies for individual companies in the Ukrainian market of medical goods and services

A Comprehensive Review on Weight Gain following Discontinuation of Glucagon-Like Peptide-1 Receptor Agonists for Obesity.

Obesity is considered the leading public health problem in the medical sector. The phenotype includes overweight conditions that lead to several other comorbidities that drastically decrease health. Glucagon-like receptor agonists (GLP-1RAs) initially designed for treating type 2 diabetes mellitus (T2DM) had demonstrated weight loss benefits in several clinical trials. In vivo studies showed that GLP-1RA encourages reduced food consumption and consequent weight reduction by stimulating brown fat and enhancing energy outlay through the action of the sympathetic nervous system (SNS) pathways. Additionally, GLP-1RAs were found to regulate food intake through stimulation of sensory neurons in the vagus, interaction with the hypothalamus and hindbrain, and through inflammation and intestinal microbiota. However, the main concern with the use of GLP-1RA treatment was weight gain after withdrawal or discontinuation. We could identify three different ways that could lead to weight gain. Potential factors might include temporary hormonal adjustment in response to weight reduction, the central nervous system's (CNS) incompetence in regulating weight augmentation owing to the lack of GLP-1RA, and β-cell malfunction due to sustained exposure to GLP-1RA. Here, we also review the data from clinical studies that reported withdrawal symptoms. Although the use of GLP-1RA could be beneficial in multiple ways, withdrawal after years has the symptoms reversed. Clinical studies should emphasize the downside of these views we highlighted, and mechanistic studies must be carried out for a better outcome with GLP-1RA from the laboratory to the bedside.