ChatGPT in travel medicine: A friend or foe?

Cachexia in tuberculosis in South-East Asian and African regions: knowledge gaps and untapped opportunities

Tuberculosis (TB) and cachexia are clinical entities that have a defined relationship, making them often found together. TB can lead to cachexia, while cachexia is a risk factor for TB. This article reviews cachexia in Tuberculosis patients in Southeast Asian and African regions by conducting a comprehensive literature search across electronic databases such as PubMed, Google Scholar, and Research Gate between 2013 and 2024 using keywords including 'Africa', 'cachexia', 'prevalence', 'implications', 'tuberculosis', and 'Southeast Asia. This article utilized only studies that satisfied the inclusion criteria, revealing knowledge gaps and untapped opportunities for cachexia in TB across Southeast Asian and African regions. Many Southeast Asian and Western Pacific patients initially receive a tuberculosis diagnosis. Sub-Saharan African countries are among the 30 high TB burden nations, according to the WHO. Food inadequacy and heightened energy expenditure can impair the immune system, leading to latent TB and subsequently, active infection. Symptoms needing attention: shortness of breath, productive cough, hyponatremia at 131 mmol/l, hypoalbuminemia at 2.1 g/dl, elevated aspartate transaminase at 75 U/l, increased lactate dehydrogenase at 654, and normocytic anemia. Comorbidities, such as kidney disease, cardiovascular disease, and asthma, can influence the nutritional status of individuals with TB. While efforts like screening, contact tracing, and utilizing gene Xpert to detect TB cases were implemented, only a few proved effective. It is essential to conduct further studies, including RCTs, in Southeast Asia and Africa to evaluate and manage cachexia in TB patients.

Brain-computer interfaces: the innovative key to unlocking neurological conditions

Neurological disorders such as Parkinson's disease, stroke, and spinal cord injury can pose significant threats to human mortality, morbidity, and functional independence. Brain-Computer Interface (BCI) technology, which facilitates direct communication between the brain and external devices, emerges as an innovative key to unlocking neurological conditions, demonstrating significant promise in this context. This comprehensive review uniquely synthesizes the latest advancements in BCI research across multiple neurological disorders, offering an interdisciplinary perspective on both clinical applications and emerging technologies. We explore the progress in BCI research and its applications in addressing various neurological conditions, with a particular focus on recent clinical studies and prospective developments. Initially, the review provides an up-to-date overview of BCI technology, encompassing its classification, operational principles, and prevalent paradigms. It then critically examines specific BCI applications in movement disorders, disorders of consciousness, cognitive and mental disorders, as well as sensory disorders, highlighting novel approaches and their potential impact on patient care. This review reveals emerging trends in BCI applications, such as the integration of artificial intelligence and the development of closed-loop systems, which represent significant advancements over previous technologies. The review concludes by discussing the prospects and directions of BCI technology, underscoring the need for interdisciplinary collaboration and ethical considerations. It emphasizes the importance of prioritizing bidirectional and high-performance BCIs, areas that have been underexplored in previous reviews. Additionally, we identify crucial gaps in current research, particularly in long-term clinical efficacy and the need for standardized protocols. The role of neurosurgery in spearheading the clinical translation of BCI research is highlighted. Our comprehensive analysis presents BCI technology as an innovative key to unlocking neurological disorders, offering a transformative approach to diagnosing, treating, and rehabilitating neurological conditions, with substantial potential to enhance patients' quality of life and advance the field of neurotechnology.

New augmented reality remote for virtual guidance and education of fracture surgery: a retrospective, non-inferiority, multi-center cohort study

BACKGROUND

The demand for telesurgery is rapidly increasing. Augmented reality (AR) remote surgery is a promising alternative, fulfilling a worldwide need in fracture surgery. However, previous AR endoscopic and Google Glass remotes remain unsuitable for fracture surgery, and the application of remote fracture surgery has not been reported. The authors aimed to evaluate the safety and clinical effectiveness of a new AR remote in fracture surgery.

MATERIALS AND METHODS

This retrospective non-inferiority cohort study was conducted at three centres. Between 1 January 2018 and 31 March 2022, 800 patients who underwent fracture surgery were eligible for participation. The study enroled 551 patients with fractures (132 patellae, 128 elbows, 126 tibial plateaus, and 165 ankles) divided into an AR group (specialists used AR to remotely guide junior doctors to perform surgeries) and a traditional non-remote group (specialists performed the surgery themselves).

RESULTS

Among 364 patients (182 per group) matched by propensity score, seven (3.8%) in the AR group and six (3%) in the non-remote group developed complications. The 0.005 risk difference (95% CI: -0.033 to 0.044) was below the pre-defined non-inferiority margin of a 10% absolute increase. A similar distribution in the individual components of all complications was found between the groups. Hierarchical analysis following propensity score matching revealed no statistical difference between the two groups regarding functional results at 1-year follow-up, operative time, amount of bleeding, number of fluoroscopies, and injury surgery interval. A Likert scale questionnaire showed positive results (median scores: 4-5) for safety, efficiency, and education.

CONCLUSION

This study is the first to report that AR remote surgery can be as safe and effective as that performed by a specialist in person for fracture surgery, even without the physical presence of a specialist, and is associated with improving the skills and increasing the confidence of junior surgeons. This technique is promising for remote fracture surgery and other open surgeries, offering a new strategy to address inadequate medical care in remote areas.

Diagnostic accuracy of artificial intelligence assisted clinical imaging in the detection of oral potentially malignant disorders and oral cancer: a systematic review and meta-analysis

BACKGROUND

The objective of this study is to examine the application of artificial intelligence (AI) algorithms in detecting oral potentially malignant disorders (OPMD) and oral cancerous lesions, and to evaluate the accuracy variations among different imaging tools employed in these diagnostic processes.

MATERIALS AND METHODS

A systematic search was conducted in four databases: Embase, Web of Science, PubMed, and Scopus. The inclusion criteria included studies using machine learning algorithms to provide diagnostic information on specific oral lesions, prospective or retrospective design, and inclusion of OPMD. Sensitivity and specificity analyses were also required. Forest plots were generated to display overall diagnostic odds ratio (DOR), sensitivity, specificity, negative predictive values, and summary receiver operating characteristic (SROC) curves. Meta-regression analysis was conducted to examine potential differences among different imaging tools.

RESULTS

The overall DOR for AI-based screening of OPMD and oral mucosal cancerous lesions from normal mucosa was 68.438 (95% CI= [39.484-118.623], I2 =86%). The area under the SROC curve was 0.938, indicating excellent diagnostic performance. AI-assisted screening showed a sensitivity of 89.9% (95% CI= [0.866-0.925]; I2 =81%), specificity of 89.2% (95% CI= [0.851-0.922], I2 =79%), and a high negative predictive value of 89.5% (95% CI= [0.851-0.927], I2 =96%). Meta-regression analysis revealed no significant difference among the three image tools. After generating a GOSH plot, the DOR was calculated to be 49.30, and the area under the SROC curve was 0.877. Additionally, sensitivity, specificity, and negative predictive value were 90.5% (95% CI [0.873-0.929], I2 =4%), 87.0% (95% CI [0.813-0.912], I2 =49%) and 90.1% (95% CI [0.860-0.931], I2 =57%), respectively. Subgroup analysis showed that clinical photography had the highest diagnostic accuracy.

CONCLUSIONS

AI-based detection using clinical photography shows a high DOR and is easily accessible in the current era with billions of phone subscribers globally. This indicates that there is significant potential for AI to enhance the diagnostic capabilities of general practitioners to the level of specialists by utilizing clinical photographs, without the need for expensive specialized imaging equipment.

Amplifying Chinese physicians' emphasis on patients' psychological states beyond urologic diagnoses with ChatGPT-A multi-center cross-sectional study

BACKGROUND

Artificial intelligence (AI) technologies, particularly large language models (LLMs), have been widely employed by the medical community. In addressing the intricacies of urology, ChatGPT offers a novel possibility to aid in clinical decision-making. This study aimed to investigate the decision-making ability of LLMs in solving complex urology-related problems and assess its effectiveness in providing psychological support to patients with urological disorders.

MATERIALS AND METHODS

This study evaluated the clinical and psychological support capabilities of ChatGPT 3.5 and 4.0 in the field of urology. A total of 69 clinical and 30 psychological questions were posed to the AI models, and their responses were evaluated by both urologists and psychologists. As a control, clinicians from Chinese medical institutions provided responses under closed-book conditions. Statistical analyses were conducted separately for each subgroup.

RESULTS

In multiple-choice tests covering diverse urological topics, ChatGPT 4.0, performed comparably to the physician group, with no significant overall score difference. Subgroup analyses revealed variable performance, based on disease type and physician experience, with ChatGPT 4.0 generally outperforming ChatGPT 3.5 and exhibiting competitive results against physicians. When assessing the psychological support capabilities of AI, it is evident that ChatGPT4.0 outperforms ChatGPT3.5 across all urology-related psychological problems.

CONCLUSIONS

The performance of LLMs in dealing with standardized clinical problems and providing psychological support has certain advantages over clinicians. AI stands out as a promising tool for potential clinical aid.

Step into the era of large multimodal models: a pilot study on ChatGPT-4V(ision)'s ability to interpret radiological images

BACKGROUND

The introduction of ChatGPT-4V's 'Chat with images' feature represents the beginning of the era of large multimodal models (LMMs), which allows ChatGPT to process and answer questions based on uploaded images. This advancement has the potential to transform how surgical teams utilize radiographic data, as radiological interpretation is crucial for surgical planning and postoperative care. However, a comprehensive evaluation of ChatGPT-4V's capabilities in interpret radiological images and formulating treatment plans remains to be explored.

PATIENTS AND METHODS

Three types of questions were collected: (1) 87 USMLE-style questions, submitting only the question stems and images without providing options to assess ChatGPT's diagnostic capability. For questions involving treatment plan formulations, a five-point Likert scale was used to assess ChatGPT's proposed treatment plan. The 87 questions were then adapted by removing detailed patient history to assess its contribution to diagnosis. The diagnostic performance of ChatGPT-4V was also tested when only medical history was provided. (2) We randomly selected 100 chest radiography from the ChestX-ray8 database to test the ability of ChatGPT-4V to identify abnormal chest radiography. (3) Cases from the 'Diagnose Please' section in the Radiology journal were collected to evaluate the performance of ChatGPT-4V in diagnosing complex cases. Three responses were collected for each question.

RESULTS

ChatGPT-4V achieved a diagnostic accuracy of 77.01% for USMLE-style questions. The average score of ChatGPT-4V's treatment plans was 3.97 (Interquartile Range: 3.33-4.67). Removing detailed patient history dropped the diagnostic accuracy to 19.54% (P<0.0001). ChatGPT-4V achieved an AUC of 0.768 (95% CI: 0.684-0.851) in detecting abnormalities in chest radiography, but could not specify the exact disease due to the lack of detailed patient history. For cases from 'Diagnose Please' ChatGPT provided diagnoses consistent with or very similar to the reference answers.

CONCLUSION

ChatGPT-4V demonstrated an impressive ability to combine patient history with radiological images to make diagnoses and directly design treatment plans based on images, suggesting its potential for future application in clinical practice.

The performance of large language model powered chatbots compared to oncology physicians on colorectal cancer queries

BACKGROUND

Large language model (LLM)-powered chatbots have become increasingly prevalent in healthcare, while their capacity in oncology remains largely unknown. To evaluate the performance of LLM-powered chatbots compared to oncology physicians in addressing to colorectal cancer queries.

METHODS

This study was conducted between August 13, 2023, and January 5, 2024. A total of 150 questions were designed, and each question was submitted three times to eight chatbots: ChatGPT-3.5, ChatGPT-4, ChatGPT-4 Turbo, Doctor GPT, Llama-2-70B, Mixtral-8x7B, Bard, and Claude 2.1. No feedback was provided to these chatbots. The questions were also answered by nine oncology physicians, including three residents, three fellows, and three attendings. Each answer was scored based on its consistency with guidelines, with a score of 1 for consistent answers and 0 for inconsistent answers. The total score for each question was based on the number of corrected answers, ranging from 0 to 3. The accuracy and scores of the chatbots were compared to those of the physicians.

RESULTS

Claude 2.1 demonstrated the highest accuracy, with an average accuracy of 82.67%, followed by Doctor GPT at 80.45%, ChatGPT-4 Turbo at 78.44%, ChatGPT-4 at 78%, Mixtral-8x7B at 73.33%, Bard at 70%, ChatGPT-3.5 at 64.89%, and Llama-2-70B at 61.78%. Claude 2.1 outperformed residents, fellows, and attendings. Doctor GPT outperformed residents and fellows. Additionally, Mixtral-8x7B outperformed residents. In terms of scores, Claude 2.1 outperformed residents and fellows. Doctor GPT, ChatGPT-4 Turbo and ChatGPT-4 outperformed residents.

CONCLUSIONS

This study shows that LLM-powered chatbots can provide more accurate medical information compared to oncology physicians.

Unveiling advanced strategies for therapeutic stem cell interventions in severe burn injuries: a comprehensive review

This comprehensive review explores the complex terrain of stem cell therapies as a potential therapeutic frontier in the healing of complicated burn wounds. Serious tissue damage, impaired healing processes, and possible long-term consequences make burn wounds a complex problem. An in-depth review is required since, despite medical progress, existing methods for treating severe burn wounds have significant limitations. Burn wounds are difficult to heal because they cause extensive tissue damage. The challenges of burn injury-induced tissue regeneration and functional recovery are also the subject of this review. Although there is a lot of promise in current stem cell treatments, there are also some limitations with scalability, finding the best way to transport the cells, and finding consistent results across different types of patients. To shed light on how to improve stem cell interventions to heal severe burn wounds, this review covers various stem cell applications in burn wounds and examines these obstacles. To overcome these obstacles, one solution is to enhance methods of stem cell distribution, modify therapies according to the severity of the burn, and conduct more studies on how stem cell therapy affects individual patients. Novel solutions may also be possible through the combination of cutting-edge technologies like nanotechnology and biotechnology. This review seeks to increase stem cell interventions by analyzing present challenges and suggesting strategic improvements. The goal is to provide a more effective and tailored way to repair serious burn wounds.

ChatGPT in medicine: prospects and challenges: a review article

It has been a year since the launch of Chat Generator Pre-Trained Transformer (ChatGPT), a generative artificial intelligence (AI) program. The introduction of this cross-generational product initially brought a huge shock to people with its incredible potential and then aroused increasing concerns among people. In the field of medicine, researchers have extensively explored the possible applications of ChatGPT and achieved numerous satisfactory results. However, opportunities and issues always come together. Problems have also been exposed during the applications of ChatGPT, requiring cautious handling, thorough consideration, and further guidelines for safe use. Here, the authors summarized the potential applications of ChatGPT in the medical field, including revolutionizing healthcare consultation, assisting patient management and treatment, transforming medical education, and facilitating clinical research. Meanwhile, the authors also enumerated researchers' concerns arising along with its broad and satisfactory applications. As it is irreversible that AI will gradually permeate every aspect of modern life, the authors hope that this review can not only promote people's understanding of the potential applications of ChatGPT in the future but also remind them to be more cautious about this "Pandora's Box" in the medical field. It is necessary to establish normative guidelines for its safe use in the medical field as soon as possible.

Bioprinting in cardiovascular medicine: Possibilities, challenges, and future perspectives for low and middle-income countries

Cardiovascular diseases (CVD) stemming from various factors significantly impact the quality of life (QoL) and are prevalent with high mortality rates in both developed and developing countries. In cases where pharmacotherapy proves insufficient and end-stage disease ensues, a heart transplant/surgical repair becomes the only feasible treatment option. However, challenges such as a limited supply of heart donors, complications associated with rejection, and issues related to medication compliance introduce an additional burden to healthcare services and adversely affect patient outcomes. The emergence of bioprinting has facilitated advancements in creating structures, including ventricles, valves, and blood vessels. Notably, the development of myocardial/cardiac patches through bioprinting has offered a promising avenue for revascularizing, strengthening, and regenerating ventricles. Employment loss in developing countries as a circumstance of disability or death can severely impact a family's well-being and means for sustainable living. Innovations by means of life sustaining treatment options can provide hope for the impoverished and help reduce disability burden on the economy of low- to middle-income countries (LMICs). Such developments can have a significant impact that can last for generations, especially in developing countries. In this review, the authors delve into various types of bioprinting techniques, exploring their possibilities, challenges, and potential future applications in treating various end-stage cardiovascular conditions in LMICs.

Glioblastoma patients' survival and its relevant risk factors during the pre-COVID-19 and post-COVID-19 pandemic: real-world cohort study in the USA and China

BACKGROUND

Although the COVID-19 pandemic has exerted potential impact on patients with glioblastomas (GBMs), it remains unclear whether the survival and its related risk factors of GBM patients would be altered or not during the period spanning from pre-COVID-19 to post-COVID-19 pandemic era. This study aimed to clarify the important issues above.

METHODS

Two observational cohorts were utilized, including the nationwide American cohort from the Surveillance, Epidemiology, and End-Results (SEER) and the Chinese glioblastoma cohort (CGC) at our institution during 2018-2020. Demographics, tumour features, treatment regimens and clinical outcomes were collected. Cox regression model, competing risk model, and subgroup and sensitivity analysis were used to dynamically estimate the survival and its relevant risk factors over different diagnosis years from the pre-COVID-19 (2018 and 2019) to post-COVID-19 (2020) pandemic. Causal mediation analysis was further adopted to explore the potential relationship between risk factors and mortality.

RESULTS

This study included 11321 GBM cases in SEER and 226 GBM patients in CGC, respectively. Instead of the diagnostic years of 2018-2020, the prognostic risk factors, such as advanced age, bilateral tumour and absence of comprehensive therapy (surgery combined with chemoradiotherapy), were identified to persistently affect GBM survival independently during the period from 2018 to 2020 in the SEER cohort (all P < 0.05). In CGC, lack of comprehensive therapy for GBM patients were restated as survival risk factors during the same timeframe. Causal mediation analysis showed that the effect of comprehensive therapy on all-cause mortality played a determinant role (direct effect value -0.227, 95% CI -0.248 to -0.207), which was partially mediated by age (9.11%) rather than tumour laterality.

CONCLUSIONS

As the timeframe shifted from pre-COVID-19 to post-COVID-19 pandemic, survival of GBM patients remained stable, yet advanced age, bilateral tumours, and passive treatment continuingly impacted GBM survival. It is necessary to optimize the comprehensive treatment for GBM patients even in the post-pandemic era.

Healthcare workers' knowledge and risk perception regarding the first wave of COVID-19 in Khyber Pakhtunkhwa, Pakistan: an online cross-sectional survey

Background

Increased COVID-19 transmission among the populace may be caused by healthcare workers (HCWs) who lack knowledge, awareness, and good preventive practices. Additionally, it may cause elevated stress levels, anxiety, poor medical judgement, and situational overestimation.

Objectives

The present survey aimed to assess knowledge and risk perception regarding COVID-19 among HCWs in Khyber Pakhtunkhwa (KP), Pakistan.

Methodology

A web-based online, pre-tested questionnaire comprising 26 items was circulated via social media in April 2020 amongst HCWs in major tertiary care facilities in KP.

Results

The study's results, revealing both the commendable knowledge levels among HCWs about COVID-19 and their heightened risk perception, highlight the critical need for targeted interventions to address the potential impact on self-protective behaviour and mental health within this vital workforce. This insight is important for designing strategies that not only enhance HCWs' well-being but also ensure the continued effectiveness of healthcare delivery during pandemics. The percentage mean score (PMS) of COVID-19 knowledge was 85.14±10.82. Male HCWs and those with an age older than or equal to 32 years demonstrated a higher knowledge score (85.62±11.08; P=0.032 and 87.59±7.33, P=0.021, respectively). About 76% of HCWs feared contracting COVID-19. Nearly 82% of respondents were mentally preoccupied with the pandemic and also terrified of it. 'Of these, 81% were nurses, 87% had a job experience of 6-8 years and 54.45% were frontline workers. Feelings of panic and concern about the pandemic were found to be more in HCWs who were physicians above the age of 32, and who had 3-5 years of work experience. HCWs' overall risk perception was found to be significantly different between males (7.04±2.26) and females (8.01±1.97), job experience of 6-10 years (8.04±177) with 3-5 years and younger than or equal to 2 years job experience (7.18±2.43,6.93±2.22), respectively, and between frontline HCWs (7.50±2.10) and non-frontline HCWs (6.84±2.40).

Conclusion

HCWs demonstrated good knowledge about COVID-19. As the risk perception of COVID-19 among HCWs is high, it can raise concerns about their self-protective behaviour, and mental health. These issues need to be addressed.

Human olfactory neurosphere-derived cells: A unified tool for neurological disease modelling and neurotherapeutic applications

As one of the leading causes of global mortality and morbidity, various neurological diseases cause social and economic burdens. Despite significant advances in the treatment of neurological diseases, establishing a proper disease model, especially for degenerative and infectious diseases, remains a major challenging issue. For long, mice were the model of choice but suffered from serious drawbacks of differences in anatomical and functional aspects of the nervous system. Furthermore, the collection of post-mortem brain tissues limits their usage in cultured cell lines. Overcoming such limitations has prompted the usage of stem cells derived from the peripheral nervous system, such as the cells of the olfactory mucosa as a preferred choice. These cells can be easily cultured in vitro and retain the receptors of neuronal cells life-long. Such cells have various advantages over embryonic or induced stem cells, including homology, and ease of culture and can be conveniently obtained from diseased individuals through either biopsies or exfoliation. They have continuously helped in understanding the genetic and developmental mechanisms of degenerative diseases like Alzheimer's and Parkinson's disease. Moreover, the mode of infection of various viruses that can lead to post-viral olfactory dysfunction, such as the Zika virus can be monitored through these cells in vitro and their therapeutic development can be fastened.

Impact of COVID-19 Pandemic on Routine Childhood Immunization Programs in Indonesia: Taking Rural and Urban Area into Account

Background

To date, the primary global concern has revolved around addressing the COVID-19 pandemic. However, there is a growing awareness of the pandemic's secondary impacts on critical aspects of healthcare, such as childhood immunization programs.

Objective

This study aims to assess the impact of the COVID-19 pandemic on childhood immunization programs in Indonesia, with a specific focus on performance disparities between rural and urban areas. It considers factors like access, utilization, and program workload.

Methods

Data were collected from primary health cares (PHCs) in two regions in West Java Province, Indonesia, representing rural and urban areas. A descriptive analysis was conducted to compare vaccination coverage, drop-out rates, and the ratio of vaccinators per dose from 2019 to 2021 in 40 and 22 PHCs for rural and urban areas, respectively. A general linear model was employed to evaluate the differences in these parameters over the three consecutive years.

Results

The results indicate fluctuations in vaccine coverage over the three years, with the most significant impact observed in 2020, particularly in rural areas. Statistical analysis revealed a significant difference in routine immunization coverage, drop-out rates, and vaccinator ratios between rural and urban areas from 2019 to 2021 (p<0.05). In 2021, both rural and urban areas displayed significant differences in performance parameters for routine immunization and COVID-19 vaccination (p<0.05), except in terms of coverage for IPV and COVID-19 vaccination.

Conclusion

The study highlights a reduction in routine immunization coverage during the pandemic, a concerning issue that increases the risk of vaccine-preventable diseases, particularly in rural areas.

A Systematic Review and Meta-Analysis of Artificial Intelligence Tools in Medicine and Healthcare: Applications, Considerations, Limitations, Motivation and Challenges

Artificial intelligence (AI) has emerged as a transformative force in various sectors, including medicine and healthcare. Large language models like ChatGPT showcase AI's potential by generating human-like text through prompts. ChatGPT's adaptability holds promise for reshaping medical practices, improving patient care, and enhancing interactions among healthcare professionals, patients, and data. In pandemic management, ChatGPT rapidly disseminates vital information. It serves as a virtual assistant in surgical consultations, aids dental practices, simplifies medical education, and aids in disease diagnosis. A total of 82 papers were categorised into eight major areas, which are G1: treatment and medicine, G2: buildings and equipment, G3: parts of the human body and areas of the disease, G4: patients, G5: citizens, G6: cellular imaging, radiology, pulse and medical images, G7: doctors and nurses, and G8: tools, devices and administration. Balancing AI's role with human judgment remains a challenge. A systematic literature review using the PRISMA approach explored AI's transformative potential in healthcare, highlighting ChatGPT's versatile applications, limitations, motivation, and challenges. In conclusion, ChatGPT's diverse medical applications demonstrate its potential for innovation, serving as a valuable resource for students, academics, and researchers in healthcare. Additionally, this study serves as a guide, assisting students, academics, and researchers in the field of medicine and healthcare alike.

Immersive virtual reality simulation training for cesarean section: a randomized controlled trial

BACKGROUND

Caesarean section (CS) is a complex surgical procedure that involves many steps and requires careful precision. Virtual reality (VR) simulation has emerged as a promising tool for medical education and training, providing a realistic and immersive environment for learners to practice clinical skills and decision-making. This study aimed to evaluate the educational effectiveness of a VR simulation program in training the management of patients with premature rupture of membranes (PROM) and CS.

MATERIALS AND METHODS

A two-arm parallel randomized controlled trial was conducted with 105 eligible participants randomly assigned to the VR group ( n =53) or the control group ( n =52) in a 1:1 ratio. The VR group received VR simulation training focused on PROM management and CS practice, while the control group watched a video presentation with narrative of clinical scenario and recording of CS. Both groups completed questionnaires assessing their prior experiences with VR, experience in managing patients with PROM and performing CS, as well as their confidence levels. These questionnaires were administered before and after the intervention, along with a mini-test quiz.

RESULTS

Baseline characteristics and previous experiences were comparable between the two groups. After the intervention, the VR group had higher confidence scores in all four aspects, including managing patients with PROM, performing CS as an operator, and understanding the indications and complications of CS, compared to the control group. The VR group also achieved significantly higher scores on the mini-test quiz [median (interquartile range), 42 (37-48) in the VR group; 36 (32-40) in the control group, P <0.001].

CONCLUSION

VR simulation program can be an effective educational tool for improving participants' knowledge and confidence in managing patients with PROM and performing CS.

Auxiliary use of ChatGPT in surgical diagnosis and treatment

ChatGPT can be used as an auxiliary tool in surgical diagnosis and treatment in several ways. One of the most incredible values of using ChatGPT is its ability to quickly process and handle large amounts of data and provide relatively accurate information to healthcare workers. Due to its high accuracy and ability to process big data, ChatGPT has been widely used in the healthcare industry for tasks such as assisting medical diagnosis, giving predictions of some diseases, and analyzing some medical cases. Surgical diagnosis and treatment can serve as an auxiliary tool to help healthcare professionals. Process large amounts of medical data, provide real-time guidance and feedback, and increase healthcare's overall speed and quality. Although it has great acceptance, it still faces issues such as ethics, patient privacy, data security, law, trustworthiness, and accuracy. This study aimed to explore the auxiliary use of ChatGPT in surgical diagnosis and treatment.

Imagistic Findings Using Artificial Intelligence in Vaccinated versus Unvaccinated SARS-CoV-2-Positive Patients Receiving In-Care Treatment at a Tertiary Lung Hospital

BACKGROUND

In December 2019 the World Health Organization announced that the widespread severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection had become a global pandemic. The most affected organ by the novel virus is the lung, and imaging exploration of the thorax using computer tomography (CT) scanning and X-ray has had an important impact.

MATERIALS AND METHODS

We assessed the prevalence of lung lesions in vaccinated versus unvaccinated SARS-CoV-2 patients using an artificial intelligence (AI) platform provided by Medicai. The software analyzes the CT scans, performing the lung and lesion segmentation using a variant of the U-net convolutional network.

RESULTS

We conducted a cohort study at a tertiary lung hospital in which we included 186 patients: 107 (57.52%) male and 59 (42.47%) females, of which 157 (84.40%) were not vaccinated for SARS-CoV-2. Over five times more unvaccinated patients than vaccinated ones are admitted to the hospital and require imaging investigations. More than twice as many unvaccinated patients have more than 75% of the lungs affected. Patients in the age group 30-39 have had the most lung lesions at almost 69% of both lungs affected. Compared to vaccinated patients with comorbidities, unvaccinated patients with comorbidities had developed increased lung lesions by 5%.

CONCLUSION

The study revealed a higher percentage of lung lesions among unvaccinated SARS-CoV-2-positive patients admitted to The National Institute of Pulmonology "Marius Nasta" in Bucharest, Romania, underlining the importance of vaccination and also the usefulness of artificial intelligence in CT interpretation.