The heterogeneity of progression of abdominal aortic aneurysms (AAAs) is not well understood. This study investigates which geometrical and mechanical factors, determined using time-resolved 3D ultrasound (3D + t US), correlate with increased growth of the aneurysm. The AAA diameter, volume, wall curvature, distensibility, and compliance in the maximal diameter region were determined automatically from 3D + t echograms of 167 patients. Due to limitations in the field-of-view and visibility of aortic pulsation, measurements of the volume, compliance of a 60 mm long region and the distensibility were possible for 78, 67, and 122 patients, respectively. Validation of the geometrical parameters with CT showed high similarity, with a median similarity index of 0.92 and root-mean-square error (RMSE) of diameters of 3.5 mm. Investigation of Spearman correlation between parameters showed that the elasticity of the aneurysms decreases slightly with diameter (p = 0.034) and decreases significantly with mean arterial pressure (p < 0.0001). The growth of a AAA is significantly related to its diameter, volume, compliance, and surface curvature (p < 0.002). Investigation of a linear growth model showed that compliance is the best predictor for upcoming AAA growth (RMSE 1.70 mm/year). To conclude, mechanical and geometrical parameters of the maximally dilated region of AAAs can automatically and accurately be determined from 3D + t echograms. With this, a prediction can be made about the upcoming AAA growth. This is a step towards more patient-specific characterization of AAAs, leading to better predictability of the progression of the disease and, eventually, improved clinical decision making about the treatment of AAAs.

Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7-11%; intraclass correlation coefficient (ICC2,1) 0.60-0.86], and SVM models were best for spatiotemporal variability (% error: 18-22%; ICC2,1 = 0.47-0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

This study examines ChatGPT's role in clinical decision support, by analyzing its scope, application, and limitations. By analyzing patient data and providing evidence-based recommendations, ChatGPT, an AI language model, can help healthcare professionals make well-informed decisions. This study examines ChatGPT's use in clinical decision support, including diagnosis and treatment planning. However, it acknowledges limitations like biases, lack of contextual understanding, and human oversight and also proposes a framework for the future clinical decision support system. Understanding these factors will allow healthcare professionals to utilize ChatGPT effectively and make accurate clinical decisions. Further research is needed to understand the implications of using ChatGPT in healthcare settings and to develop safeguards for responsible use.

In this study, we are investigating the academic usability of Chat GPT, which has been gaining increasing popularity recently, as well as its potential for use in undergraduate study. In this context, based on our experiments, we believe that Chat GPT needs further development in certain aspects. While it shows promising potential for academic research in the future, currently, it can be stated that there is a need for improvement in this regard. But we foresee that Chat GPT can be used more effectively in undergraduate study. In our experience, Chat GPT has provided very reasonable results in this regard.

Artificial intelligence (AI) has been driving the continuous development of the Physical Medicine and Rehabilitation (PM&R) fields. The latest release of ChatGPT/GPT-4 has shown us that AI can potentially transform the healthcare industry. In this study, we propose various ways in which ChatGPT/GPT-4 can display its talents in the field of PM&R in future. ChatGPT/GPT-4 is an essential tool for Physiatrists in the new era.

PURPOSE: The study aims to assess ChatGPT performance on the Residency Admission National Exam to evaluate ChatGPT's level of medical knowledge compared to graduate medical doctors in Italy. METHODS: ChatGPT3 was used in June 2023 to undertake the 2022 Italian Residency Admission National Exam-a 140 multiple choice questions computer-based exam taken by all Italian medical graduates yearly, used to assess basic science and applied medical knowledge. The exam was scored using the same criteria defined by the national educational governing body. The performance of ChatGPT was compared to the performance of the 15,869 medical graduates who took the exam in July 2022. Lastly, the integrity and quality of ChatGPT's responses were evaluated. RESULTS: ChatGPT answered correctly 122 out of 140 questions. The score ranked in the top 98.8th percentile among 15,869 medical graduates. Among the 18 incorrect answers, 10 were evaluating direct questions on basic science medical knowledge, while 8 were evaluating candidates' applied clinical knowledge and reasoning under the form of case presentation. Errors were logical (2 incorrect answers) and informational in nature (16 incorrect answers). Explanations to the correct answers were all evaluated as "appropriate." Comparison to national statistics related to the minimal score needed to match into each specialty, demonstrated that the performance of ChatGPT would have granted the candidate a match into any specialty. CONCLUSION: ChatGPT proved to be proficient in basic science medical knowledge and applied clinical knowledge. Future research should assess the impact and reliability of ChatGPT in clinical practice.

The advent of artificial intelligence (AI) and machine learning (ML) has revolutionized the field of medicine. Although highly effective, the rapid expansion of this technology has created some anticipated and unanticipated bioethical considerations. With these powerful applications, there is a necessity for framework regulations to ensure equitable and safe deployment of technology. Generative Adversarial Networks (GANs) are emerging ML techniques that have immense applications in medical imaging due to their ability to produce synthetic medical images and aid in medical AI training. Producing accurate synthetic images with GANs can address current limitations in AI development for medical imaging and overcome current dataset type and size constraints. Offsetting these constraints can dramatically improve the development and implementation of AI medical imaging and restructure the practice of medicine. As observed with its other AI predecessors, considerations must be taken into place to help regulate its development for clinical use. In this paper, we discuss the legal, ethical, and technical challenges for future safe integration of this technology in the healthcare sector.

The Code Interpreter feature in ChatGPT has the potential to democratize data analysis for non-specialists. As bioinformaticians, we are impressed by its performance in data manipulation and visualization. However, bioinformatics tasks often require execution of third-party packages, access to annotation knowledgebase, and handling large datasets. Code Interpreter's exclusive support for Python, no installation option for additional packages, inability to utilize external resources, and limited storage capacity could pose obstacles to its wide adoption in bioinformatics applications. To address these limitations, we advocated for the necessity of locally deployable, API-based systems for chatbot-aided bioinformatics applications.

We aimed to evaluate current trends and future directions in the field of AI research since ChatGPT was launched. We performed a bibliometric analysis of the literature published during the first 7 months of the life of ChatGPT since its introduction, updated to July 1st, 2023. Seven hundred and twenty-four (724) articles were retrieved. This analysis highlights a significant increase in publications exploring ChatGPT use across various medical disciplines, indicating its expanding relevance in healthcare. A decline proportion of studies focusing on ethical considerations was observed. Simultaneously, there was a steady increase in studies focused on the exploration of possible applications of ChatGPT. As ChatGPT applications continue to expand, ongoing vigilance and collaborative efforts to optimize ChatGPT performance are essential in harnessing the benefits while mitigating the risks of AI use in healthcare.

Regeneration of ruptured Achilles tendon remains a clinical challenge owing to its limited regenerative capacity. Dynamic tensile stress plays a positive role in the regeneration of tendon, although the specific underlying mechanisms remain unclear. In this study, the Achilles tendon defect-regeneration model was created in male C57BL/6 mice aged 8 weeks. The animals were randomly assigned to four groups-repair, non-repair, repair with fixation, and non-repair with fixation. The repair group and repair with fixation group adopted the panda rope bridge technique (PRBT) repair method. Our results demonstrated the presence of more densely aligned and mature collagen fibers, as well as more tendon-related makers, in the repair group at both 2- and 4-week post-surgery. Furthermore, the biomechanical strength of the regenerated tendon in the repair group was highly improved. Most importantly, the expressions of integrin αv and its downstream and the phosphorylation levels of FAK and ERK were remarkably higher in the repair group than in the other groups. Furthermore, blocking FAK or ERK with selective inhibitors PF573228 and U0126 resulted in obvious adverse effects on the histological structure of the regenerated Achilles tendon. In summary, this study demonstrated that dynamic tensile stress based on the PRBT could effectively promote the regeneration of the Achilles tendon, suggesting that dynamic tensile stress enhances the cell proliferation and tenogenic differentiation via the activation of the integrin/FAK/ERK signaling pathway.

Most cases of unilateral knee osteoarthritis (OA) progress to bilateral OA within 10 years. Biomechanical asymmetries have been implicated in contralateral OA development; however, gait analysis alone does not consistently detect asymmetries in OA patient gait. Stair ambulation is a more demanding activity that may be more suited to reveal between-leg asymmetries in OA patients. The objective of this study was to investigate the between-leg biomechanical differences in patients with unilateral mild-to-moderate knee OA. Sixteen unilateral mild-to-moderate medial knee OA patients and 16 healthy individuals underwent kinematic and kinetic analysis of stair ascent and descent. Stair ascent produced higher loading and muscle forces in the unaffected limb compared to the OA limb, and stair descent produced lower loading on the OA limb compared to healthy subjects. These biomechanical differences were apparent in the ankle, knee, and hip joints. The implications of these findings are that OA patients rely more heavily on their unaffected sides than the affected side in stair ascent, a strategy that may be detrimental to the unaffected joint health. The reduction in affected limb loading in stair descent is thought to be related to minimizing pain.

The launch of Open AI's chatbot, ChatGPT, has generated a lot of attention and discussion among professionals in several fields. Many concerns and challenges have been brought up by researchers from various fields, particularly in relation to the harm that using these tools for medical diagnosis and treatment recommendations can cause. In addition, it has been debated if ChatGPT is dependable, efficient, and helpful for clinicians and medical professionals. Therefore, in this study, we assess ChatGPT's effectiveness in providing mental health support, particularly for issues related to anxiety and depression, based on the chatbot's responses and cross-questioning. The findings indicate that there are significant inconsistencies and that ChatGPT's reliability is low in this specific domain. As a result, care must be used when using ChatGPT as a complementary mental health resource.

Cuff-based pulse waveform acquisition (CBPWA) devices are reliable solutions for non-invasive cardiovascular diagnostics. However, poor signal resolution has limited clinical applications. This study aims to demonstrate the improved signal quality of CBPWA devices by implementing passive pneumatic low-pass filters (pLPF). Conventionally, pressure sensor output resolution is a percentage of the operating range. Therefore, measurement of small pressure changes in a large range must sacrifice signal resolution to accommodate for the large mean pressures. We design a pLPF to obtain the running mean pressure and combine it with a high-resolution differential pressure sensor for isolating the signal's pulsatile component. Thirty-one volunteers participated in a device proof-of-concept study at Caltech. Volunteers were measured at rest in the supine position on the left arm. The filtering behavior is mathematically modeled and experimentally verified, showing good agreement between measured and predicted cutoff frequencies. In the human study, the device successfully captured high-fidelity pulse waveform measurements for all volunteers: a blood pressure (BP) reading was followed by inflate-and-hold acquisition in diastolic BP (DBP), mean arterial pressure (MAP), and supra systolic BP (sSBP). The study demonstrated the reliability and high signal resolution of pLPF for CBPWA. Considering the widespread use of the brachial cuff, a system for high-resolution CBPWA motivates the clinical implementation of non-invasive pulse waveform analysis (PWA).

Additive Manufacturing is noted for ease of product customization and short production run cost-effectiveness. As our global population approaches 8 billion, additive manufacturing has a future in maintaining and improving average human life expectancy for the same reasons that it has advantaged general manufacturing. In recent years, additive manufacturing has been applied to tissue engineering, regenerative medicine, and drug delivery. Additive Manufacturing combined with tissue engineering and biocompatibility studies offers future opportunities for various complex cardiovascular implants and surgeries. This paper is a comprehensive overview of current technological advancements in additive manufacturing with potential for cardiovascular application. The current limitations and prospects of the technology for cardiovascular applications are explored and evaluated.

Acquired disorders and congenital defects of the male and female reproductive systems can have profound impacts on patients, causing sexual and endocrine dysfunction and infertility, as well as psychosocial consequences that affect their self-esteem, identity, sexuality, and relationships. Reproductive tissue engineering (REPROTEN) is a promising approach to restore fertility and improve the quality of life of patients with reproductive disorders by developing, replacing, or regenerating cells, tissues, and organs from the reproductive and urinary systems. In this review, we explore the latest advancements in REPROTEN techniques and their applications for addressing degenerative conditions in male and female reproductive organs. We discuss current research and clinical outcomes and highlight the potential of 3D constructs utilizing biomaterials such as scaffolds, cells, and biologically active molecules. Our review offers a comprehensive guide for researchers and clinicians, providing insights into how to reestablish reproductive tissue structure and function using innovative surgical approaches and biomaterials. We highlight the benefits of REPROTEN for patients, including preservation of fertility and hormonal production, reconstruction of uterine and cervical structures, and restoration of sexual and urinary functions. Despite significant progress, REPROTEN still faces ethical and technical challenges that need to be addressed. Our review underscores the importance of continued research in this field to advance the development of effective and safe REPROTEN approaches for patients with reproductive disorders.

In this paper, a new application of Piezoelectric Transformer (PT)-based power converters to generate high-voltage (HV) bipolar pulses for medical electroporation therapy is proposed. In particular, PT-based power conversion is investigated as an alternative to magnetics-based approaches of generating HV from a relatively low-voltage (LV) input source for application in electroporation therapy. The detailed PT-based system design and selection of wide bandgap semiconductor switches such as GaN FETs, high-voltage SiC diodes and SiC MOSFETs, as well as simulation results to demonstrate proof-of-concept using LTSpice are presented. Preliminary experimental results of the PT-based capacitor charger are shown, and work is ongoing to develop a complete hardware prototype of the proposed HV pulse generator.

ChatGPT is revolutionizing hospital workflows by enhancing the precision and efficiency of tasks that were formerly the exclusive domain of healthcare professionals. Additionally, ChatGPT can aid in administrative duties, including appointment scheduling and billing, which enables healthcare professionals to allocate more time towards patient care. By shouldering some of these responsibilities, ChatGPT has the potential to advance the quality of patient care, streamline departmental efficiency, and lower healthcare costs. Nevertheless, it is crucial to strike a balance between the advantages of ChatGPT and the necessity of human interaction in healthcare to guarantee optimal patient care. While ChatGPT may assume some of the duties of physicians in particular medical domains, it cannot replace human doctors. Tackling the challenges and constraints associated with the integration of ChatGPT into the healthcare system is critical for its successful implementation.

Pedicle screw fixation is a spinal fusion technique that involves the implantation of screws into vertebral pedicles to restrict movement between those vertebrae. The objective of this research is to measure pedicle screw placement accuracy using a novel automated measurement system that directly compares the implanted screw location to the planned target in all three anatomical views. Preoperative CT scans were used to plan the screw trajectories in 122 patients across four surgical centers. Postoperative scans were fused to the preoperative plan to quantify placement accuracy using an automated measurement algorithm. The mean medial-lateral and superior-inferior deviations in the pedicle region for 500 screws were 1.75 ± 1.36 mm and 1.52 ± 1.26 mm, respectively. These deviations were measured using an automated system and were statistically different from manually determined values. The uncertainty associated with the fusion of preoperative to postoperative images was also quantified to better understand the screw-to-plan accuracy results. This study uses a novel automated measurement system to quantify screw placement accuracy as it relates directly to the planned target location, instead of analyzing for breaches of the pedicle, to quantify the validity of using of a robotic-guidance system for accurate pedicle screw placement.

Humanoid doctor is an AI-based robot that featured remote bi-directional communication and is embedded with disruptive technologies. Accurate and real-time responses are the main characteristics of a humanoid doctor which diagnoses disease in a patient. The patient details are obtained by Internet of Things devices, edge devices, and text formats. The inputs from the patient are processed by the humanoid doctor, and it provides its opinion to the patient. The historical patient data are trained using cloud artificial intelligence platform and the model is tested against the patient sample data acquired using medical IoT and edge devices. Disease is identified at three different stages and analyzed. The humanoid doctor is expected to identify the diseases well in comparison with human healthcare professionals. The humanoid doctor is under-trusted because of the lack of a multi-featured accurate model, accessibility, availability, and standardization. In this letter, patient input, artificial intelligence, and response zones are encapsulated and the humanoid doctor is realized.

Mesenchymal stem cells (MSCs) are used widely in tissue engineering and regenerative medicine because of their ease of isolation and their pluripotency. The low survival and retention rate of MSCs at the target site upon implantation can be addressed via encapsulation within hydrogels capable of directing their fate. In this study, the interplay between matrix mechanics and the passage number of MSCs on their chondrogenic differentiation was assessed. Human bone marrow-derived MSCs between passages 4 and 6 were encapsulated within alginate-gelatin hybrid gels. The stiffness of the gels was varied by varying alginate concentrations while maintaining the concentration of gelatin and consequently, the cell adhesion sites, constant. The study revealed that within 4.8 kPa gels, GAG deposition was higher by P4 MSCs compared to P6 MSCs. However, an opposite trend was observed with collagen type 2 deposition. Further, we observed enhanced chondrogenic differentiation upon encapsulation of MSCs within 6.7 kPa hydrogel irrespective of passaging history. However, the effect of matrix compliance was more prominent in the case of higher passaged MSCs suggesting that matrix stiffness can help rescue the reduced differentiation capability of these cells.

Advancements in AI have resulted in the development of sophisticated language models like ChatGPT, which can generate human-like text. While ChatGPT is useful for clarifying concepts and providing basic guidance, it has limitations. It lacks the ability to provide the latest scientific information and access original medical databases. Studies have shown that ChatGPT's text can be robotic, shallow, and lacking a human touch. It has also been found to provide misleading or inaccurate information. Researchers and medical professionals should be aware of these limitations and not solely rely on ChatGPT for complex tasks. The human element and real-world experiences are indispensable in science, and consulting experts is advisable for reliable insights.

This critique explores the implications of integrating artificial intelligence (AI) technology, specifically OpenAI's advanced language model GPT-4 and its interface, ChatGPT, into the field of spinal surgery. It examines the potential effects of algorithmic bias, unique challenges in surgical domains, access and equity issues, cost implications, global disparities in technology adoption, and the concept of technological determinism. It posits that biases present in AI training data may impact the quality and equity of healthcare outcomes. Challenges related to the unique nature of surgical procedures, including real-time decision-making, are also addressed. Concerns over access, equity, and cost implications underscore the potential for exacerbated healthcare disparities. Global disparities in technology adoption highlight the importance of global collaboration, technology transfer, and capacity building. Finally, the critique challenges the notion of technological determinism, emphasizing the continued importance of human judgement and patient-care provider relationship in healthcare. The critique calls for a comprehensive evaluation of AI technology integration in healthcare to ensure equitable and quality care.

Slowing the decline in walking mobility in the elderly is critical for maintaining the quality of life. Wearable assistive devices may 1 day facilitate mobility in older adults; however, we need to ensure that such devices do not impair stability in this population that is predisposed to fall-related injuries. This study sought to quantify the effects of untethered ankle exoskeleton assistance on measures of stability, whole-body dynamics, and strategies to maintain balance during normal and perturbed walking in older adults. Eight healthy participants (69-84 years) completed a treadmill-based walking protocol that included perturbations from unexpected belt accelerations while participants walked with and without ankle exoskeleton assistance. Exoskeleton assistance increased frontal plane range of angular momentum (8-14%, p ≤ 0.007), step width (18-34%, p ≤ 0.006), and ankle co-contraction (21-29%, p ≤ 0.039), and decreased biological ankle moment (16-27%, p ≤ 0.001) during unperturbed and perturbed walking; it did not affect the anteroposterior margin-of-stability, step length, trunk variability, or soleus activity during unperturbed and perturbed walking. Our finding that ankle exoskeleton assistance did not affect the anteroposterior margin-of-stability supports additional investigation of assistive exoskeletons for walking assistance in the elderly.

This paper introduces a simple strategy for diagnosing disease, which is called improved gray wolf optimization (IGWO) and ensemble classification. The proposed strategy consists of two sequential phases, which are; (i) Feature Selection Phase (FSP) and (ii) Ensemble Classification Phase (ECP). During the former, the most effective features for diagnosing disease are selected, while during the latter, the actual diagnosis takes place depending on voting of five different classifiers. The main contribution of this paper is a suggested modification for the traditional Gray Wolf Optimization (GWO), which is called Improved Gray Wolf Optimization (IGWO). As an optimization technique, the proposed IGWO is employed in the FSP for selecting the effective features. For evaluating, IGWO has been implemented using recent feature selection techniques as well as the proposed method. To accomplish the classification phase; ensemble classification has been used which uses several classification techniques such as; Naïve Bayes (NB), Support Vector Machines (SVM), Deep Neural Network (DNN), Decision Tree (DT), and K-Nearest Neighbors (KNN). Ensemble classification integrate several classifiers for improving prediction performance. Experimental results have shown that employing IGWO promotes the performance of the diagnosing strategy of different diseases in terms of precision, recall, and accuracy.

The interaction of the three-point seat belt with the occupant, particularly the lap belt with the pelvis, is affected by a multitude of intrinsic and extrinsic factors, including the torso recline angle, lap belt angle, and occupant body mass index (BMI). While field data analyses have shown the strong safety benefit for seat belt use regardless of occupant size or crash direction, the term "submarining" historically has been used to describe a scenario in which the lap belt loads the abdominal soft tissue and organs, superior and posterior to the pelvic bone. While contemporary restraint systems work to effectively address the risk of submarining in occupants properly seated and properly belted, scenarios in which the lap belt may not properly engage the load-bearing pelvis remain. These scenarios, including a reclined torso angle or shallow lap belt angle, require further study. In this research study, eight non-injurious seated belt pull tests were conducted on two constrained whole-body cadavers of above-normal BMI (≥ 25 kg/m2) with controlled variation of torso and lap belt-pelvis angles. Test factors affecting belt engagement with the pelvis were identified for each subject. Belt engagement was largely affected by the initial placement of the lap belt. The initial belt placement was affected by the torso angle which influenced the distribution of the abdominal soft tissue. The belt disengagement thresholds differed between subjects due to the inter-subject differences in soft tissue distribution, which affected the lap belt kinematics relative to the pelvis. In addition to improving the understanding of this particular submarining mechanism, this study provides a dataset for future validation of human body model soft tissue deformation response from lap belt loading.

Tissue-engineering and cell-based strategies provide an intriguing approach to treat complex conditions such as those of the endocrine system. We have previously developed a cell-based hormone therapy (cHT) to address hormonal insufficiency associated with the loss of ovarian function. To assess how the cHT strategy may achieve its efficacy, we developed a mathematical model to determine if known autocrine, paracrine, and endocrine effects of the native hypothalamus-pituitary-ovary (HPO) axis could explain our previously observed effects in ovariectomized rats following treatment with cHT. Our model suggests that cHT constructs participate in the complex machinery of the HPO axis. We were able to describe the in vivo behaviors of estrogen, progesterone, follicle-stimulating hormone (FSH), luteinizing hormone (LH), inhibin, and androgen with good accuracy. A sensitivity analysis indicated that some parameters impact the broader HPO system more than others, but that most changes in model parameters led to proportional changes in the system. We also conducted a predictive analysis on the effect of cHT dose on HPO axis hormones and found that, with the exception of estrogen, the other HPO hormones analyzed reach a saturation level within the physically possible number of constructs.

The study evaluates the performance of OpenAI's GPT-3 model on answering medical exam questions from Staged Senior Professional and Technical Examinations Regulations for Medical Doctors in the field of internal medicine. The study used the official API to connect the questionnaire with the ChatGPT model, and the results showed that the AI model performed reasonably well, with the highest score of 8/13 in chest medicine. However, the overall performance of the AI model was limited, with only chest medicine scoring more than 60. ChatGPT scored relatively high in Chest medicine, Gastroenterology, and general medicine. One of the limitations of the study is the use of non-English text, which may affect the model's performance as the model is primarily trained on English text.

The global COVID-19 pandemic has affected all spheres of human life, resulting in millions of deaths and overwhelming medical facilities. Moreover, the world has witnessed great financial hardship because of job losses resulting in economic havoc. Many sections of society have contributed in different ways to slow the spread of the virus and protect public health. For example, medical scientists are praised for their efforts to develop COVID-19 vaccines. Clinical trials have shown that the COVID-19 vaccines are highly effective in preventing symptomatic COVID-19 infections. However, many people around the world have been hesitant to get vaccinated. Vaccine misconceptions have emerged and increased due to a combination of factors, including the availability of information on the Internet and the influence of celebrities and opinion leaders. In this context, we have analyzed ChatGPT responses to relevant queries on vaccine misconceptions. The positive responses and supportive opinions provided by the AI chatbot could be instrumental in shaping people's perceptions of vaccines and in encouraging users to get vaccinated and reduce misconceptions.

Large language models or ChatGPT have recently gained extensive media coverage. At the same time, the use of ChatGPT has increased deistically. Biomedical researchers, engineers, and clinicians have shown significant interest and started using it due to its diverse applications, especially in the biomedical field. However, it has been found that ChatGPT sometimes provided incorrect or partly correct information. It is unable to give the most recent information. Therefore, we urgently advocate a domain-specific next-generation, ChatBot for biomedical engineering and research, providing error-free, more accurate, and updated information. The domain-specific ChatBot can perform diversified functions in biomedical engineering, such as performing innovation in biomedical engineering, designing a medical device, etc. The domain-specific artificial intelligence enabled device will revolutionize biomedical engineering and research if a biomedical domain-specific ChatBot is produced.