ChatGPT, an advanced natural language processing model, holds significant promise in diabetes self-management and education. ChatGPT excels in providing personalized educational experiences by tailoring information to meet individual patient needs and preferences. It aids patients in developing self-management skills and strategies, fostering proactive disease management. Additionally, ChatGPT addresses healthcare access disparities by enabling patients to access educational resources irrespective of their geographic location or physical limitations. However, it is important to acknowledge and address the deficiencies of ChatGPT, such as its limited medical expertise, contextual understanding, and emotional support capabilities. Strategies for optimizing ChatGPT include regular training and updating, integration of healthcare professionals' expertise, improvement in contextual comprehension, and enhancing emotional support. By addressing these limitations and striking a balance between the benefits and limitations, ChatGPT can play a significant role in empowering patients to better understand and manage diabetes. Further research and development are needed to refine ChatGPT's capabilities and address ethical considerations, but its integration in patient education holds the potential to transform healthcare delivery and create a more informed and engaged patient population.

PURPOSE: The use of AI-powered technology, particularly OpenAI's ChatGPT, holds significant potential to reshape healthcare and medical education. Despite existing studies on the performance of ChatGPT in medical licensing examinations across different nations, a comprehensive, multinational analysis using rigorous methodology is currently lacking. Our study sought to address this gap by evaluating the performance of ChatGPT on six different national medical licensing exams and investigating the relationship between test question length and ChatGPT's accuracy. METHODS: We manually inputted a total of 1,800 test questions (300 each from US, Italian, French, Spanish, UK, and Indian medical licensing examination) into ChatGPT, and recorded the accuracy of its responses. RESULTS: We found significant variance in ChatGPT's test accuracy across different countries, with the highest accuracy seen in the Italian examination (73% correct answers) and the lowest in the French examination (22% correct answers). Interestingly, question length correlated with ChatGPT's performance in the Italian and French state examinations only. In addition, the study revealed that questions requiring multiple correct answers, as seen in the French examination, posed a greater challenge to ChatGPT. CONCLUSION: Our findings underscore the need for future research to further delineate ChatGPT's strengths and limitations in medical test-taking across additional countries and to develop guidelines to prevent AI-assisted cheating in medical examinations.

The application of artificial intelligence (AI) in the field of medicine has revolutionised various sectors of the health care system, including robotics surgery, biotechnology, pharmaceutical, evidence-based medicine and advanced research and transplantation techniques. By offering improved 3D imagery of the various organs involved in surgery and perfectly minimising the chances of error, AI aid made complicated surgical procedures more efficient and highly effective, requiring less hands-on. Further, the AI tool helps plastic surgery and aesthetic surgeons in anticipating prognostic surgical markers and post-operative consequences. In addition to enhancing accurate and rapid diagnosis, AI has played a pivotal role in the development and discovery of new drugs. Nevertheless, the application of AI in health care also raises significant challenges and concerns. Incorrect drug recommendations, failure to identify tumours and lesions on imaging modalities and potential bias in data entry and its automatic can risk the life of patients on a large scale. Additionally, breaching patient data privacy may raise concerns about cybersecurity issues, further compromised by growing dependency on AI which can result in massive unemployment. In short, AI has played a pivotal role in health care; however, addressing the in-transparencies is critical to ensure safe, ethical and more effective implementation in the dynamic field of medicine.

A common feature of congenital heart disease is the presence of right ventricular outflow tract (RVOT) obstruction that can range from mild to severe and can lead to atresia of the pulmonary valve, in extreme conditions. RVOT abnormalities can frequently be corrected surgically or via interventional means. However, most of these patients will ultimately develop pulmonary valve insufficiency and eventual right ventricular dilation, which will require a pulmonary valve replacement at some point in their life to mitigate the detrimental effects of pulmonary valve regurgitation (PVR) on the right ventricle (RV). The evolution from the studies done by Philip Bonhoeffer to implant a pulmonary valve via transcatheter means, have provided a bedrock for transcatheter pulmonary valve replacement (TPVR). Yet, several areas of unmet need for a demographic of patients still exist. Here, we discuss the clinical unmet needs in children under 20 Kg and expand the use of hybrid and other TPVR approaches along with the current indications and contraindications for pulmonary valve replacement. The constraints and limitations from commercially available pulmonary valves will be discussed from a clinical standpoint. Finally, we explore the use of hybrid and periventricular delivery of transcatheter pulmonary valves in younger patients.

Emotions play a pivotal role in human cognition, exerting influence across diverse domains of individuals' lives. The widespread adoption of artificial intelligence and machine learning has spurred interest in systems capable of automatically recognizing and classifying emotions and affective states. However, the accurate identification of human emotions remains a formidable challenge, as they are influenced by various factors and accompanied by physiological changes. Numerous solutions have emerged to enable emotion recognition, leveraging the characterization of biological signals, including the utilization of cardiac signals acquired from low-cost and wearable sensors. The objective of this work was to comprehensively investigate the current trends in the field by conducting a Systematic Literature Review (SLR) that focuses specifically on the detection, recognition, and classification of emotions based on cardiac signals, to gain insights into the prevailing techniques employed for signal acquisition, the extracted features, the elicitation process, and the classification methods employed in these studies. A SLR was conducted using four research databases, and articles were assessed concerning the proposed research questions. Twenty seven articles met the selection criteria and were assessed for the feasibility of using cardiac signals, acquired from low-cost and wearable devices, for emotion recognition. Several emotional elicitation methods were found in the literature, including the algorithms applied for automatic classification, as well as the key challenges associated with emotion recognition relying solely on cardiac signals. This study extends the current body of knowledge and enables future research by providing insights into suitable techniques for designing automatic emotion recognition applications. It emphasizes the importance of utilizing low-cost, wearable, and unobtrusive devices to acquire cardiac signals for accurate and accessible emotion recognition.

Dummy headforms used for impact testing have changed little over the years, and frictional characteristics are thought not to represent the human head accurately. The frictional interface between the helmet and head is an essential factor affecting impact response. However, few studies have evaluated the coefficient of friction (COF) between the human head and helmet surface. This study's objectives were to quantify the human head's static and dynamic COF and evaluate the effect of biological sex and hair properties. Seventy-four participants slid their heads along a piece of helmet foam backed by a fixed load cell at varying normal force levels. As normal force increased, static and dynamic human head COF decreased following power-law curves. At 80 N, the static COF is 0.32 (95% CI 0.30-0.34), and the dynamic friction coefficient is 0.27 (95% CI 0.26-0.28). Biological sex and hair properties were determined not to affect human head COF. The COFs between the head and helmet surface should be used to develop more biofidelic head impact testing methods, define boundary conditions for computer simulations, and aid decision-making for helmet designs.

The advent of artificial intelligence (AI) has catalyzed a revolutionary transformation across various industries, including healthcare. Medical applications of ChatGPT, a powerful language model based on the generative pre-trained transformer (GPT) architecture, encompass the creation of conversational agents capable of accessing and generating medical information from multiple sources and formats. This study investigates the research trends of large language models such as ChatGPT, GPT 4, and Google Bard, comparing their publication trends with early COVID-19 research. The findings underscore the current prominence of AI research and its potential implications in biomedical engineering. A search of the Scopus database on July 23, 2023, yielded 1,096 articles related to ChatGPT, with approximately 26% being medical science-related. Keywords related to artificial intelligence, natural language processing (NLP), LLM, and generative AI dominate ChatGPT research, while a focused representation of medical science research emerges, with emphasis on biomedical research and engineering. This analysis serves as a call to action for researchers, healthcare professionals, and policymakers to recognize and harness AI's potential in healthcare, particularly in the realm of biomedical research.

Escherichia coli bacterium is a rod-shaped organism composed of a complex double membrane structure. Knowledge of electric field driven ion transport through both membranes and the evolution of their induced permeabilization has important applications in biomedical engineering, delivery of genes and antibacterial agents. However, few studies have been conducted on Gram-negative bacteria in this regard considering the contribution of all ion types. To address this gap in knowledge, we have developed a deterministic and stochastic Brownian dynamics model to simulate in 3D space the motion of ions through pores formed in the plasma membranes of E. coli cells during electroporation. The diffusion coefficient, mobility, and translation time of Ca2+, Mg2+, Na+, K+, and Cl- ions within the pore region are estimated from the numerical model. Calculations of pore's conductance have been validated with experiments conducted at Gustave Roussy. From the simulations, it was found that the main driving force of ionic uptake during the pulse is the one due to the externally applied electric field. The results from this work provide a better understanding of ion transport during electroporation, aiding in the design of electrical pulses for maximizing ion throughput, primarily for application in cancer treatment.

Diabetes is a common chronic illness that requires continual patient education and support to be effectively managed. The lack of diabetes educators and the limitations of conventional education approaches make it difficult to meet the specific needs of each patient. The artificial intelligence (AI) tool ChatGPT provides a tailored and interactive approach to support and instruction. ChatGPT has the potential to increase diabetes patients' access to information and assistance, but more research is required to fully comprehend its benefits and drawbacks. To maximize ChatGPT's potential benefits and reduce its possible hazards, care must be taken to ensure that it is developed and integrated in an ethical and equitable manner. In this post, we talk about how crucial ChatGPT is for educating people about diabetes.

The large amount of training samples required to develop a deep learning brain injury model demands enormous computational resources. Here, we study how a transformer neural network (TNN) of high accuracy can be used to efficiently generate pretraining samples for a convolutional neural network (CNN) brain injury model to reduce computational cost. The samples use synthetic impacts emulating real-world events or augmented impacts generated from limited measured impacts. First, we verify that the TNN remains highly accurate for the two impact types (N = 100 each; [Formula: see text] of 0.948-0.967 with root mean squared error, RMSE, ~ 0.01, for voxelized peak strains). The TNN-estimated samples (1000-5000 for each data type) are then used to pretrain a CNN, which is further finetuned using directly simulated training samples (250-5000). An independent measured impact dataset considered of complete capture of impact event is used to assess estimation accuracy (N = 191). We find that pretraining can significantly improve CNN accuracy via transfer learning compared to a baseline CNN without pretraining. It is most effective when the finetuning dataset is relatively small (e.g., 2000-4000 pretraining synthetic or augmented samples improves success rate from 0.72 to 0.81 with 500 finetuning samples). When finetuning samples reach 3000 or more, no obvious improvement occurs from pretraining. These results support using the TNN to rapidly generate pretraining samples to facilitate a more efficient training strategy for future deep learning brain models, by limiting the number of costly direct simulations from an alternative baseline model. This study could contribute to a wider adoption of deep learning brain injury models for large-scale predictive modeling and ultimately, enhancing safety protocols and protective equipment.

The investigation of magnetohydrodynamic (MHD) blood flow within configurations that are pertinent to the human anatomy holds significant importance in the realm of scientific inquiry because of its practical implications within the medical field. This article presents an exhaustive appraisal of the diverse applications of magnetohydrodynamics and their computational modeling in biological contexts. These applications are classified into two categories: simple flow and pulsatile flow. An alternative approach of traditional CFD methods called Lattice Boltzmann Method (LBM), a mesoscopic method based on kinetic theory, is introduced to solve complex problems, such as hemodynamics. The results show that the flow velocity reduces considerably by increasing the magnetic field intensity, and the flow separation area is minimized by the increase of magnetic field strength. The LBM with BGK collision model has shown good results in terms of precision. Finally, this literature review has revealed a number of potential avenues for further research. Suggestions for future works are proposed accordingly.

End-stage Fontan patients with single-ventricle (SV) circulation are often bridged-to-heart transplantation via mechanical circulatory support (MCS). Donor shortage and complexity of the SV physiology demand innovative MCS. In this paper, an out-of-the-box circulation concept, in which the left and right ventricles are switched with each other is introduced as a novel bi-ventricle MCS configuration for the "failing" Fontan patients. In the proposed configuration, the systemic circulation is maintained through a conventional mechanical ventricle assist device (VAD) while the venous circulation is delegated to the native SV. This approach spares the SV and puts it to a new use at the right-side providing the most-needed venous flow pulsatility to the failed Fontan circulation. To analyze its feasibility and performance, eight SV failure modes have been studied via an established multi-compartmental lumped parameter cardiovascular model (LPM). Here the LPM model is experimentally validated against the corresponding pulsatile mock-up flow loop measurements of a representative 15-year-old Fontan patient employing a clinically-approved VAD (Medtronic-HeartWare). The proposed surgical configuration maintained the healthy cardiac index (3-3.5 l/min/m2) and the normal mean systemic arterial pressure levels. For a failed SV with low ejection fraction (EF = 26%), representing a typical systemic Fontan failure, the proposed configuration enabled a ~ 28 mmHg amplitude in the venous/pulmonary waveforms and a 2 mmHg decrease in the central venous pressure (CVP) together with acceptable mean pulmonary artery pressures (17.5 mmHg). The pulmonary vascular resistance (PVR)-SV failure case provided a ~ 5 mmHg drop in the CVP, with venous/pulmonary pulsatility reaching to ~ 22 mmHg. For the high PVR failure case with a healthy SV (EF = 44%) pulmonary hypertension is likely to occur as expected. While this condition is routinely encountered during the heart transplantation and managed through pulmonary vasodilators a need for precise functional assessment of the spared failed-ventricle is recommended if utilized in the PVR failure mode. Comprehensive in vitro and in silico results encourage this novel concept as a low-cost, more physiological alternative to the conventional bi-ventricle MCS pending animal experiments.

Robotic surgery and telemedicine have revolutionized the healthcare industry, offering improved patient outcomes and access to medical knowledge. However, Pakistan lags behind in adopting these cutting-edge technologies due to several challenges, including limited infrastructure, budget constraints, and low awareness among patients and healthcare professionals. This article provides an in-depth analysis of the current state of the healthcare system in Pakistan, highlighting the need for investment and policy reforms to integrate robotic surgery and telemedicine effectively. The potential benefits, including medical tourism, enhanced facilities, and skilled workforce retention, are discussed as incentives for the government to embrace digital medicine and bridge the healthcare gap. Through increased awareness, training programs, and collaborations with other nations, Pakistan can pave the way for a digitally empowered healthcare future.

Titanium-based implants utilized in total joint arthroplasties could restore primary musculoskeletal function to patients suffering from osteoarthritis and other conditions. Implants are susceptible to failure stemming from aseptic loosening and infection at the joint site, eventually requiring revision surgery. We hypothesized that there might be a feedback loop by which metal degradation particles and ions released from the implant decrease cell viability and increase immune response, thereby creating biochemical conditions that increase the corrosion rate and release more metal ions. This study focused on the synergistic process through cell viability assays and electrochemical tests. From the results, inflammatory conditions from ion release resulting in cell death would further increase the corrosion rate at the metal implant site. The synergistic interaction in the implant surroundings in which infectious conditions produce Ti ions that contribute to more infection, creating a potential cycle of accelerating corrosion.

Advancements in artificial intelligence (AI) provide many helpful tools for healthcare, one of which includes AI chatbots that use natural language processing to create humanlike, conversational dialog. These chatbots have general cognitive skills and are able to engage with clinicians and patients to discuss patients' health conditions and what they may be at risk for. While chatbot engines have access to a wide range of medical texts and research papers, they currently provide high-level, generic responses and are limited in their ability to provide diagnostic guidance and clinical advice to patients on an individual level. The essay discusses the use of retrieval-augmented generation (RAG), which can be used to improve the specificity of user-entered prompts and thereby enhance the detail in AI chatbot responses. By embedding more recent clinical data and trusted medical sources, such as clinical guidelines, into the chatbot models, AI chatbots can provide more patient-specific guidance, faster diagnoses and treatment recommendations, and greater improvement of patient outcomes.

Behind armor blunt trauma (BABT) is a non-penetrating injury caused by the rapid deformation of body armor, by a projectile, which may in extreme circumstances cause death. The understanding of the mechanisms is still low, in relation to what is needed for safety threshold levels. Few models of graded kinetic energy transfer to the body exist. We established an experimental model for graded BABT. The cold gas cannon was air-driven, consisted of a pressure vessel, a barrel, and a pressure actuator. It required short training to operate and was constructed by standard components. It produced standardized expulsion of plastic projectiles with 65 mm and weight 58 g. Velocity correlated linearly to pressure (R 0.9602, p < 0.0001), equation Y = 6.558*X + 46.50. Maximum tested pressure was 10 bar, velocity 110 m/s and kinetic energy (Ek) 351 J. Crossbred male swine (n = 10) mean weight (SD) 56 ± 3 kg, were subjected to BABT, mean Ek (SD) 318 (61) J, to a fix point on the right lateral thorax. Pulmonary contusion was confirmed by physiological parameters pO2 (p < 0.05), SaO2 (p < 0.01), pCO2 (p < 0.01), etCO2 (p < 0.01), MPAP (p < 0.01), Cstat (p < 0.01), intrapulmonary shunt (Q's/Q't) (p < 0.05), and qualified trans-thoracic ultrasound (p < 0.0001). The consistent injury profile enabled for the addition of future experimental interventions.

Neuromuscular electrical stimulation (NMES) of the quadriceps (Q) may increase venous blood flow to reduce the risk of venous thromboembolism. This study assessed whether Q-NMES pants could increase peak venous velocity (PVV) in the femoral vein using Doppler ultrasound and minimize discomfort. On 15 healthy subjects, Q-NMES using textile electrodes integrated in pants was applied with increasing intensity (mA) until the first visible muscle contraction [measurement level (ML)-I] and with an additional increase of six NMES levels (ML II). Discomfort using a numeric rating scale (NRS, 0-10) and PVV were used to assess different NMES parameters: frequency (1, 36, 66 Hz), ramp-up/-down time (RUD) (0, 1 s), plateau time (1.5, 4, and 6 s), and on:off duty cycle (1:1, 1:2, 1:3, 1:4). Q-NMES pants significantly increased PVV from baseline with 93% at ML I and 173% at ML II. Frequencies 36 Hz and 66 Hz and no RUD resulted in significantly higher PVV at both MLs compared to 1 Hz and 1 s RUD, respectively. Plateau time, and duty cycle did not significantly change PVV. Discomfort was only significantly higher with increasing intensity and frequency. Q-NMES pants produces intensity-dependent 2-3-fold increases of venous blood flow with minimal discomfort. The superior NMES parameters were a frequency of 36 Hz, 0 s RUD, and intensity at ML II. Textile-based NMES wearables are promising for non-episodic venous thromboembolism prevention.

Modeling blood flow in aneurysms treated with coils could be used to understand the complete embolization of the aneurysm, through thrombus formation that fills the entire sac. Modeling of the endovascular coil mass as a porous medium is a technique that allows for study of aneurysm hemodynamics, efficiently for patient-specific treatment outcome predictions. Models in the literature use mean porosity of coils in the aneurysmal volume, proving inadequate for outcome prediction. However, models that consider heterogeneous porosity distribution have shown more accurate hemodynamics. We recently published the porous crown model, considering the heterogeneous coil mass distribution, validated on two patients. This study aims (i) to validate the porous crown model for a larger cohort (eight patients), and (ii) to propose a porous medium model translatable to clinical practice in treatment planning. We analyzed the porosity distribution of the endovascular coils deployed inside the cerebral aneurysm phantoms of eight patients using 3D x-ray synchrotron images. The permeability and inertial factor of the porous crown model are calculated using previously published methodology. We propose a new "bilinear" porous model, that uses the same hypothesis, but the permeability and inertial factor can be defined from just basic information available in the neuro-suite, i.e., the aneurysmal sac volume and the coil volume fraction targeted by the neurosurgeon. These two models are compared to the coil-resolved simulations, considered as the gold standard. The results show that both the porous crown model and the bilinear model produce similarly accurate hemodynamics in the aneurysm. The error in the standard (mean porosity) porous model is 66%, whereas the error of the bilinear model is 26%, compared to the coil-resolved. The bilinear model is promising as a means of treatment outcome prediction at time of intervention.

Acute respiratory distress syndrome (ARDS) and ventilator-induced lung injury (VILI) are heterogeneous conditions. The spatiotemporal evolution of these heterogeneities is complex, and it is difficult to elucidate the mechanisms driving its progression. Through previous quantitative analyses, we explored the distributions of cellular injury and neutrophil infiltration in experimental VILI and discovered that VILI progression is characterized by both the formation of new injury in quasi-random locations and the expansion of existing injury clusters. Distributions of neutrophil infiltration do not correlate with cell injury progression and suggest a systemic response. To further examine the dynamics of VILI, we have developed a novel computational model that simulates damage (cellular injury progression and neutrophil infiltration) using a stochastic approach. Optimization of the model parameters to fit experimental data reveals that the range and strength of interdependence between existing and new damaged regions both increase as mechanical ventilation patterns become more injurious. The interdependence of cellular injury can be attributed to mechanical tethering forces, while the interdependence of neutrophils is likely due to longer-range cell signaling pathways.

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonance imaging (MRI) using wearable seismocardiography (SCG) devices. 4D flow MRI provides a comprehensive assessment of cardiovascular hemodynamics, but it is costly and time-consuming. We hypothesized that deep learning could be used to identify pathological changes in blood flow, such as elevated peak systolic velocity ([Formula: see text]) in patients with heart valve diseases, from SCG signals. We also investigated the ability of this deep learning technique to differentiate between patients diagnosed with aortic valve stenosis (AS), non-AS patients with a bicuspid aortic valve (BAV), non-AS patients with a mechanical aortic valve (MAV), and healthy subjects with a normal tricuspid aortic valve (TAV). In a study of 77 subjects who underwent same-day 4D flow MRI and SCG, we found that the [Formula: see text] values obtained using deep learning and SCGs were in good agreement with those obtained by 4D flow MRI. Additionally, subjects with non-AS TAV, non-AS BAV, non-AS MAV, and AS could be classified with ROC-AUC (area under the receiver operating characteristic curves) values of 92%, 95%, 81%, and 83%, respectively. This suggests that SCG obtained using low-cost wearable electronics may be used as a supplement to 4D flow MRI exams or as a screening tool for aortic valve disease.

Peripheral arterial diseases are commonly managed with endovascular procedures, which often face limitations in device control and visualization under X-ray fluoroscopy guidance. In response, we developed the CathCam, an angioscope integrated into an expandable cable-driven parallel mechanism to enhance real-time visualization, precise device positioning and catheter support for successful plaque crossing. The primary objective of this study was to assess and compare the performance of the novel CathCam with respect to conventional catheters and the CathPilot (i.e., CathCam without the angioscope), for applications in crossing chronic total occlusions (CTO). We first assessed the system in 3D-printed phantom models, followed by an ex vivo evaluation with CTO samples from a patient's superficial femoral artery. We measured and compared success rates, crossing times, and fluoroscopy times in both experiments. The CathCam demonstrated a 100% success rate in phantom experiments and a 75% success rate in ex vivo experiments with CTO samples, compared to conventional catheters, with 35% and 25% success rates, respectively. The average crossing times for the CathCam and the conventional catheter were 31 s and 502 s for the phantom experiments and 210 s and 511 s for the actual CTO lesions. The Cathcam also showed to be a reliable endovascular imaging approach in an in vivo experiment. Compared to conventional catheters, the CathCam significantly increased the success rate and reduced crossing and fluoroscopy times in both phantom and ex vivo setups. CathCam can potentially improve clinical outcomes for minimally invasive endovascular interventions by offering high-resolution real-time imaging alongside accurate device control.

Wave separation analysis (WSA) reveals the impact of forward- and backward-running waves on the arterial pressure pulse, but the calculations require a flow waveform. This study investigated (1) the variability of the ascending aortic flow waveform in children and adolescents with/without a childhood heart disease history (CHD); (2) the accuracy of WSA obtained with a representative flow waveform (RepFlow), compared with the triangulation method and published ultrasound-derived adult representative flow; (3) the impact of limitations in Doppler ultrasound on WSA; and (4) generalizability of results to adults with a history of CHD. Phase contrast MRI was performed in youth without (n = 45, Group 1, 10-19 years) and with CHD (n = 79, Group 2, 7-18 years), and adults with CHD history (n = 29, Group 3, 19-59 years). Segmented aortic cross-sectional area was used as a surrogate for the central pressure waveform in WSA. A subject-specific virtual Doppler ultrasound was performed on MRI data by extracting velocities from a sample volume. Time/amplitude-normalized ascending aortic flow waveforms were highly consistent amongst all groups. WSA with RepFlow therefore yielded errors < 10% in all groups for reflected wave magnitude and return time. Absolute errors were typically 1.5-3 times greater with other methods, including subject-specific (best-case/virtual) Doppler ultrasound, for which velocity profile skewing introduced waveform errors. Our data suggest that RepFlow is the optimal approach for pressure-only WSA in children and adolescents with/without CHD, as well as adults with CHD history, and may even be more accurate than subject-specific Doppler ultrasound in the ascending aorta.

The robustness of artificial intelligence (AI) in medical research has gotten a lot of attention in the current era. In particular, ChatGPT-4 is emerging as a notable AI language model. Basically, ChatGPT-4 is a state-of-the-art language model that mimics human responses to natural language inputs using AI-based deep learning methods and was created by OpenAI. ChatGPT-4 allows medical researchers to instantly share and receive up-to-date information on best practices and guidelines. Moreover, practitioners can explore this technology in various fields such as recognizing patterns, predicting outcomes, and providing important insights by entering patient data, medical reports, and other text-based information. No doubt, ChatGPT-4 is in the trending era but still need to know more about this tool. Therefore, this letter highlights the contributions, benefits, and challenges of the ChatGPT-4 tool in the medical field that may help medical professionals or researchers in their future work.