Artificial Polymerizations in Living Organisms for Biomedical Applications.

Within living organisms, numerous nanomachines are constantly involved in complex polymerization processes, generating a diverse array of biomacromolecules for maintaining biological activities. Transporting artificial polymerizations from lab settings into biological contexts has expanded opportunities for understanding and managing biological events, creating novel cellular compartments, and introducing new functionalities. This review summarizes the recent advancements in artificial polymerizations, including those responding to external stimuli, internal environmental factors, and those that polymerize spontaneously. More importantly, the cutting-edge biomedical application scenarios of artificial polymerization, notably in safeguarding cells, modulating biological events, improving diagnostic performance, and facilitating therapeutic efficacy are highlighted. Finally, this review outlines the key challenges and technological obstacles that remain for polymerizations in biological organisms, as well as offers insights into potential directions for advancing their practical applications and clinical trials.

Characterization and Biomedical Applications of Green-Synthesized Selenium Nanoparticles Using Tridax procumbens Stem Extract.

Background Selenium nanoparticles (SeNPs) are one of the metal nanoparticles that have been widely utilized for their anti-microbial, anti-oxidant, anti-inflammatory activities, and other biomedical applications. Tridax procumbens (TP) stem extract is a promising herb species rich in flavonoids, tannins, alkaloids, phytosterols, and hydroxycinnamates, which play a major role in wound healing applications.  Aim The study aims to synthesize SeNPs using TP stem extract, characterizations, and its biomedical applications. Materials and methods SeNPs were synthesized using TP stem extract. The green synthesis of SeNPs was confirmed by ultraviolet-visible (UV-vis) spectra analysis. The synthesized SeNPs were characterized using Fourier-transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The agar well diffusion method was utilized to evaluate the anti-bacterial properties of the green synthesized SeNPs using TP stem extract. The anti-oxidant effect of SeNPs was tested using the 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay, ferric-reducing anti-oxidant power assay (FRAP), and hydroxyl radical scavenging assay (H2O2). The anti-inflammatory effect was investigated using the bovine serum albumin assay and egg albumin denaturation method, and the cytotoxic effect of the green synthesized SeNPs was tested using the brine shrimp lethality (BSL) assay. Results The green synthesis of SeNPs was confirmed using different types of analysis techniques. The characterizations were done by UV-visible spectroscopy analysis, exhibiting a maximum peak at the range of 330 nm. SEM analysis revealed the shape of the nanoparticle to be hexagonal. The agar well diffusion method exhibited the anti-bacterial efficacy of SeNPs against wound microorganisms with a zone of inhibition of 14.6 mm for Escherichia coli (E. coli), 15.8 mm for Staphylococcus aureus (S. aureus), and 15.4 mm for Pseudomonas aeruginosa (P. aeruginosa). The TP stem-mediated SeNPs showed potential effects in anti-oxidant, anti-inflammatory, and cytotoxic activity, which shows very little toxicity. Conclusion Overall, the green synthesis of TP-stem-mediated SeNPs has great potential in biomedical applications. Thus, the synthesized SeNPs exhibit significant anti-bacterial efficacy against wound pathogens. The TP stem-mediated SeNPs showed potential effects in anti-oxidant, anti-inflammatory, and cytotoxic activity, which shows low toxicity. Furthermore, the green-synthesized SeNPs can be utilized in therapeutic management.

Imperceptible augmentation of living systems with organic bioelectronic fibres.

The functional and sensory augmentation of living structures, such as human skin and plant epidermis, with electronics can be used to create platforms for health management and environmental monitoring. Ideally, such bioelectronic interfaces should not obstruct the inherent sensations and physiological changes of their hosts. The full life cycle of the interfaces should also be designed to minimize their environmental footprint. Here we report imperceptible augmentation of living systems through in situ tethering of organic bioelectronic fibres. Using an orbital spinning technique, substrate-free and open fibre networks-which are based on poly (3,4-ethylenedioxythiophene):polystyrene sulfonate-can be tethered to biological surfaces, including fingertips, chick embryos and plants. We use customizable fibre networks to create on-skin electrodes that can record electrocardiogram and electromyography signals, skin-gated organic electrochemical transistors and augmented touch and plant interfaces. We also show that the fibres can be used to couple prefabricated microelectronics and electronic textiles, and that the fibres can be repaired, upgraded and recycled.

A framework for decolonising and diversifying biomedical sciences curricula: rediscovery, representation and readiness.

To date, most efforts to decolonise curricula have focussed on the arts and humanities, with many believing that science subjects are objective, unbiased, and unaffected by colonial legacies. However, science is shaped by both contemporary and historical culture. Science has been used to support imperialism, to extract and exploit knowledge and natural resources, and to justify racist and ableist ideologies. Colonial legacies continue to affect scientific knowledge generation and shape contemporary research priorities. In the biomedical sciences, research biases can feed into wider health inequalities. Reflection of these biases in our taught curricula risks perpetuating long-standing inequities to future generations of scientists. We examined attitudes and understanding towards decolonising and diversifying the curriculum among students and teaching staff in the biomedical sciences at the University of Bristol, UK, to discover whether our current teaching practice is perceived as inclusive. We used a mixed methods study including surveys of staff (N = 71) and students (N = 121) and focus groups. Quantitative data showed that staff and students think decolonising the curriculum is important, but this is more important to female respondents (P < 0.001). Students are less aware than staff of current efforts to decolonise the curriculum, while students from minority ethnic groups feel less represented by the curriculum than white students. Thematic analysis of qualitative data revealed three themes that are important for a decolonised curriculum in our context: rediscovery, representation and readiness. We propose that this '3Rs framework' could guide future efforts to decolonise and diversify the curriculum in the biomedical sciences and beyond.

Biomedical literature mining: graph kernel-based learning for gene-gene interaction extraction.

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene-gene interaction. The main purpose of this study is to develop extraction methods for gene-gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene-gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene-gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene-gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene-gene interactions. In this way, the results of this study are expected to help achieve precision medicine.

Ecofriendly biosynthesis of copper nanoparticles from novel marine S. rhizophila species for enhanced antibiofilm, antimicrobial and antioxidant potential.

Marine microorganisms offer a promising avenue for the eco-friendly synthesis of nanoparticles due to their unique biochemical capabilities and adaptability to various environments. This study focuses on exploring the potential of a marine bacterial species, Stenotrophomonas rhizophila BGNAK1, for the synthesis of biocompatible copper nanoparticles and their application for hindering biofilms formed by monomicrobial species. The study begins with the isolation of the novel marine S. rhizophila species from marine soil samples collected from the West coast region of Kerala, India. The isolated strain is identified through 16S rRNA gene sequencing and confirmed to be S. rhizophila species. Biosynthesis of copper nanoparticles using S. rhizophila results in the formation of nanoparticles with size of range 10-50 nm. The nanoparticles exhibit a face-centered cubic crystal structure of copper, as confirmed by X-Ray Diffraction analysis. Furthermore, the synthesized nanoparticles display significant antimicrobial activity against various pathogenic bacteria and yeast. The highest inhibitory activity was against Staphylococcus aureus with a zone of 27 ± 1.00 mm and the least activity was against Pseudomonas aeruginosa with a zone of 22 ± 0.50 mm. The zone of inhibition against Candida albicans was 16 ± 0.60 mm. The antibiofilm activity against biofilm-forming clinical pathogens was evidenced by the antibiofilm assay and SEM images. Additionally, the copper nanoparticles exhibit antioxidant activity, as evidenced by their scavenging ability against DPPH, hydroxyl, nitric oxide, and superoxide radicals, as well as their reducing power in the FRAP assay. The study highlights the potential of the marine bacterium S. rhizophila BGNAK1 for the eco-friendly biosynthesis of copper nanoparticles with diverse applications. Synthesized nanoparticles exhibit promising antibiofilm, antimicrobial, and antioxidant properties, suggesting their potential utility in various fields such as medicine, wastewater treatment, and environmental remediation.

What public health challenges and unmet medical needs would benefit from interdisciplinary collaboration in the EU? A survey and multi-stakeholder debate.

In the past decade, significant European calls for research proposals have supported translational collaborative research on non-communicable and infectious diseases within the biomedical life sciences by bringing together interdisciplinary and multinational consortia. This research has advanced our understanding of disease pathophysiology, marking considerable scientific progress. Yet, it is crucial to retrospectively evaluate these efforts' societal impact. Research proposals should be thoughtfully designed to ensure that the research findings can be effectively translated into actionable policies. In addition, the choice of scientific methods plays a pivotal role in shaping the societal impact of research discoveries. Understanding the factors responsible for current unmet public health issues and medical needs is crucial for crafting innovative strategies for research policy interventions. A multistakeholder survey and a roundtable helped identify potential needs for consideration in the EU research and policy agenda. Based on survey findings, mental health disorders, metabolic syndrome, cancer, antimicrobial resistance, environmental pollution, and cardiovascular diseases were considered the public health challenges deserving prioritisation. In addition, early diagnosis, primary prevention, the impact of environmental pollution on disease onset and personalised medicine approaches were the most selected unmet medical needs. Survey findings enabled the formulation of some research-policies interventions (RPIs), which were further discussed during a multistakeholder online roundtable. The discussion underscored recent EU-level activities aligned with the survey-derived RPIs and facilitated an exchange of perspectives on public health and biomedical research topics ripe for interdisciplinary collaboration and warranting attention within the EU's research and policy agenda. Actionable recommendations aimed at facilitating the translation of knowledge into transformative, science-based policies are also provided.

[Childhood overweight and obesity: A biomedical challenge in Mexico]. / Sobrepeso y obesidad infantil: un reto biomédico en México.

In Mexico, 1 out of 3 schoolchildren aged 5 to 11 years is overweight or obese, which represents one of the main public health concerns, due to the fact that this condition in the child population is highly associated with the development of metabolic complications in adults. To date, dietary and physical activity interventions to prevent this problem have shown modest results worldwide. Biomedical studies in Mexico have shown that the pathophysiology of childhood overweight and obesity presents different molecular patterns, inflammation and oxidative stress, possibly associated with specific variants in the genome. However, the challenge is to achieve a secure characterization of this evidence so that it can be used in intervention studies aimed to improve the ability to predict and treat childhood overweight and obesity in Mexico. The biomedical challenge is to make knowledge a prevention strategy in families, in society and in the country, in order to fight the serious problem of obesity and its consequences.

En México 1 de cada 3 escolares de 5 a 11 años presenta sobrepeso u obesidad, lo cual representa una de las principales preocupaciones de salud pública, debido a que en la población infantil este padecimiento se asocia altamente con el desarrollo de complicaciones metabólicas en el adulto. Hasta el momento las intervenciones dietéticas y de actividad física para prevenir este problema han mostrado resultados modestos a nivel mundial. Los estudios biomédicos en México han demostrado que la fisiopatología del sobrepeso y la obesidad infantil presenta diferentes patrones moleculares, de inflamación y de estrés oxidativo, posiblemente asociados a variantes específicas en el genoma. Sin embargo, el reto es lograr la caracterización segura de estas evidencias para que sea posible emplearlas en los estudios de intervención encaminados a mejorar la capacidad de predicción y tratamiento del sobrepeso y la obesidad infantil en México. El reto biomédico es hacer del conocimiento una estrategia de prevención en las familias, en la sociedad y en el país, a fin de combatir el grave problema de la obesidad y sus consecuencias.

The sample locator: A federated search tool for biosamples and associated data in Europe using HL7 FHIR.

BACKGROUND: This study outlines the development of a highly interoperable federated IT infrastructure for academic biobanks located at the major university hospital sites across Germany. High-quality biosamples linked to clinical data, stored in biobanks are essential for biomedical research. We aimed to facilitate the findability of these biosamples and their associated data. Networks of biobanks provide access to even larger pools of samples and data even from rare diseases and small disease subgroups. The German Biobank Alliance (GBA) established in 2017 under the umbrella of the German Biobank Node (GBN), has taken on the mission of a federated data discovery service to make biosamples and associated data available to researchers across Germany and Europe. METHODS: In this context, we identified the requirements of researchers seeking human biosamples from biobanks and the needs of biobanks for data sovereignty over their samples and data in conjunction with the sample donor's consent. Based on this, we developed a highly interoperable federated IT infrastructure using standards such as Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Quality Language (CQL). RESULTS: The infrastructure comprises two major components enabling federated real-time access to biosample metadata, allowing privacy-compliant queries and subsequent project requests. It has been in use since 2019, connecting 16 German academic biobanks, with additional European biobanks joining. In production since 2019 it has run 4941 queries over the span of one year on more than 900,000 biosamples collected from more than 170,000 donors. CONCLUSION: This infrastructure enhances the visibility and accessibility of biosamples for research, addressing the growing demand for human biosamples and associated data in research. It also underscores the need for improvements in processes beyond IT infrastructure, aiming to advance biomedical research and similar infrastructure development in other fields.

Insights Into the Research Landscape of Biomedical Physics: A Bibliometric Inquiry.

Biomedical physics is the interdisciplinary field that links the scientific concepts in physics to the practice of medicine and biology, in an effort to understand biological processes, help in the development of medical technologies, to improve human health. This bibliometric study investigates the interdisciplinary field of biomedical physics, which integrates the principles of physics with biological and medical sciences to develop innovative diagnostic and therapeutic technologies. Utilizing the Web of Science database for bibliographic data collection, the analysis employs advanced bibliometric software tools, including Biblioshiny and VOSviewer, to comprehensively map the research landscape. Our findings delineate the annual scientific production, highlighting growth trends and identifying the most influential authors and key publication venues in the field. A thematic analysis reveals prevailing research topics and the evolution of scientific interests over time, providing insights into the shifting focus areas within biomedical physics. The factorial analysis goes further to clarify the conceptual structure of the discipline by providing a topological image of how the different research areas are involved. It helps to recognize topical fields and the possibility of the topicalization of other subjects. Keyword co-occurrence assumes the leading themes and measures the value of the topology. Meanwhile, bibliographical information defines the authors' network, and co-citation analysis identifies the critical authors' pool. The last points to the topic dependence and the network of research collaboration on a global scale. As a result, a survey identifies the deficits and rules of recommendations for the further development of research. It adds practical implications that are necessary for the development and identifies influences for popularization that it might have in the future.

Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.

Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning for classification.

Autism spectrum disorder detection using facial images: A performance comparison of pretrained convolutional neural networks.

Autism spectrum disorder (ASD) is a complex psychological syndrome characterized by persistent difficulties in social interaction, restricted behaviours, speech, and nonverbal communication. The impacts of this disorder and the severity of symptoms vary from person to person. In most cases, symptoms of ASD appear at the age of 2 to 5 and continue throughout adolescence and into adulthood. While this disorder cannot be cured completely, studies have shown that early detection of this syndrome can assist in maintaining the behavioural and psychological development of children. Experts are currently studying various machine learning methods, particularly convolutional neural networks, to expedite the screening process. Convolutional neural networks are considered promising frameworks for the diagnosis of ASD. This study employs different pre-trained convolutional neural networks such as ResNet34, ResNet50, AlexNet, MobileNetV2, VGG16, and VGG19 to diagnose ASD and compared their performance. Transfer learning was applied to every model included in the study to achieve higher results than the initial models. The proposed ResNet50 model achieved the highest accuracy, 92%, compared to other transfer learning models. The proposed method also outperformed the state-of-the-art models in terms of accuracy and computational cost.

Tensor Methods in Biomedical Image Analysis.

In the past decade, tensors have become increasingly attractive in different aspects of signal and image processing areas. The main reason is the inefficiency of matrices in representing and analyzing multimodal and multidimensional datasets. Matrices cannot preserve the multidimensional correlation of elements in higher-order datasets and this highly reduces the effectiveness of matrix-based approaches in analyzing multidimensional datasets. Besides this, tensor-based approaches have demonstrated promising performances. These together, encouraged researchers to move from matrices to tensors. Among different signal and image processing applications, analyzing biomedical signals and images is of particular importance. This is due to the need for extracting accurate information from biomedical datasets which directly affects patient's health. In addition, in many cases, several datasets have been recorded simultaneously from a patient. A common example is recording electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) of a patient with schizophrenia. In such a situation, tensors seem to be among the most effective methods for the simultaneous exploitation of two (or more) datasets. Therefore, several tensor-based methods have been developed for analyzing biomedical datasets. Considering this reality, in this paper, we aim to have a comprehensive review on tensor-based methods in biomedical image analysis. The presented study and classification between different methods and applications can show the importance of tensors in biomedical image enhancement and open new ways for future studies.

Racial Equity, Diversity and Inclusion in Bioethics: Recommendations from the Association of Bioethics Program Directors Presidential Task Force.

Recent calls to address racism in bioethics reflect a sense of urgency to mitigate the lethal effects of a lack of action. While the field was catalyzed largely in response to pivotal events deeply rooted in racism and other structures of oppression embedded in research and health care, it has failed to center racial justice in its scholarship, pedagogy, advocacy, and practice, and neglected to integrate anti-racism as a central consideration. Academic bioethics programs play a key role in determining the field's norms and practices, including methodologies, funding priorities, and professional networks that bear on equity, inclusion, and epistemic justice. This article describes recommendations from the Racial Equity, Diversity, and Inclusion (REDI) Task Force commissioned by the Association of Bioethics Program Directors to prioritize and strengthen anti-racist practices in bioethics programmatic endeavors and to evaluate and develop specific goals to advance REDI.

Nanogel: A versatile drug delivery system for the treatment of various diseases and their future perspective.

Nanogel (NG) drug delivery systems have emerged as promising tools for targeted and controlled drug release, revolutionizing treatment approaches across various diseases. Their unique physicochemical properties, such as nano size, high surface area, biocompatibility, stability, and tunable drug release, make them ideal carriers for a wide range of therapeutic agents. Nanogels (NGs), characterized by their 3D network of crosslinked polymers, offer unique edges like high drug loading capacity, controlled release, and targeted delivery. Additionally, the diverse applications of NGs in medical therapeutics highlight their versatility and potential impact on improving patient outcomes. Their application spans cancer treatment, infectious diseases, and chronic conditions, allowing for precise drug delivery to specific tissues or cells, minimizing side effects, and enhancing therapeutic efficacy. Despite their potential, challenges such as scalability, manufacturing reproducibility, and regulatory hurdles must be addressed. Achieving clinical translation requires overcoming these obstacles to ensure therapeutic payloads' safe and efficient delivery. Strategies such as surface modification and incorporating stimuli-responsive elements enhanced NG performance and addressed specific therapeutic challenges. Advances in nanotechnology, biomaterials, and targeted drug design offer opportunities to improve the performance of NGs and address current limitations. Tailoring NGs for exploring combination therapies and integrating diagnostics for real-time monitoring represent promising avenues for future research. In conclusion, NG drug delivery systems have demonstrated tremendous potential in diverse disease applications. Overcoming challenges and leveraging emerging technologies will pave the way for their widespread clinical implementation, ushering in a new era of precision medicine and improved patient care.

Synthesis of Hydrofluoric (HF) Acid Free, Two-Dimensional (2D) Tantalum Carbide MXene Nanosheets and Quantum Dots.

MXenes are two-dimensional (2D) transition metal-based carbides, nitrides, and carbonitrides that are synthesized from its precursor MAX phase. The selective etching of the "A" from the MAX phase yields multi-functional MXenes that hold promise in a wide range of energy-based applications and biomedical applications. Based on its intended application, MXenes are prepared as multilayered sheets, monolayer flakes, and quantum dots. Conventionally, MXenes are prepared using hydrofluoric (HF) acid etching; however, the use of HF impedes its effective use in biomedical applications. This calls for the use of nontoxic HF-free synthesis protocols to prepare MXenes safe for biological use. Therefore, we have discussed a facile process to synthesize biocompatible, HF-free MXene nanosheets and quantum dots.

A Chatbot-Like Platform to Enhance the Discovery of OMOP CDM Databases.

Multicentre studies become possible with the current strategies to solve the interoperability problems between databases. With the great adoption of those strategies, new problems regarding data discovery were raised. Some were solved using database catalogues and graphical dashboards for data analysis and comparison. However, when these communities grow, these strategies become obsolete. In this work, we addressed those challenges by proposing a platform with a chatbot-like mechanism to help medical researchers identify databases of interest. The tool was developed using the metadata extracted from OMOP CDM databases.

Cohort Builder: A Software Pipeline for Generating Patient Cohorts with Predetermined Baseline Characteristics from Medical Records and Raw Ophthalmic Imaging Data.

In clinical research, the analysis of patient cohorts is a widely employed method for investigating relevant healthcare questions. The ability to automatically extract large-scale patient cohorts from hospital systems is vital in order to unlock the potential of real-world clinical data, and answer pivotal medical questions through retrospective research studies. However, existing medical data is often dispersed across various systems and databases, preventing a systematic approach to access and interoperability. Even when the data are readily accessible, clinical researchers need to sift through Electronic Medical Records, confirm ethical approval, verify status of patient consent, check the availability of imaging data, and filter the data based on disease-specific image biomarkers. We present Cohort Builder, a software pipeline designed to facilitate the creation of patient cohorts with predefined baseline characteristics from real-world ophthalmic imaging data and electronic medical records. The applicability of our approach extends beyond ophthalmology to other medical domains with similar requirements such as neurology, cardiology and orthopedics.

Rapid Deployment of a Pseudonymization Service in a Distributed Research Data Infrastructure - Lessons Learned.

The SARS-CoV-2 pandemic highlighted the importance of fast, collaborative research in biomedicine. Within the ORCHESTRA consortium, we rapidly deployed a pseudonymization service with minimal training and maintenance efforts under time-critical conditions to support a complex, multi-site research project. Over two years, the service was deployed in 13 sites across 11 countries to register more than 10,000 study participants and 15,000 biosamples. In this work, we present lessons learned as part of this process. Most importantly, we learned that common challenges can be overcome by creatively utilizing widely available tools and that having a dedicated partner to manage software rollout and pre-configure software packages for each site fosters the effective implementation.

Performance of ChatGPT in Solving Questions From the Progress Test (Brazilian National Medical Exam): A Potential Artificial Intelligence Tool in Medical Practice.

Background The use of artificial intelligence (AI) is not a recent phenomenon, but the latest advancements in this technology are making a significant impact across various fields of human knowledge. In medicine, this trend is no different, although it has developed at a slower pace. ChatGPT is an example of an AI-based algorithm capable of answering questions, interpreting phrases, and synthesizing complex information, potentially aiding and even replacing humans in various areas of social interest. Some studies have compared its performance in solving medical knowledge exams with medical students and professionals to verify AI accuracy. This study aimed to measure the performance of ChatGPT in answering questions from the Progress Test from 2021 to 2023. Methodology An observational study was conducted in which questions from the 2021 Progress Test and the regional tests (Southern Institutional Pedagogical Support Center II) of 2022 and 2023 were presented to ChatGPT 3.5. The results obtained were compared with the scores of first- to sixth-year medical students from over 120 Brazilian universities. All questions were presented sequentially, without any modification to their structure. After each question was presented, the platform's history was cleared, and the site was restarted. Results The platform achieved an average accuracy rate in 2021, 2022, and 2023 of 69.7%, 68.3%, and 67.2%, respectively, surpassing students from all medical years in the three tests evaluated, reinforcing findings in the current literature. The subject with the best score for the AI was Public Health, with a mean grade of 77.8%. Conclusions ChatGPT demonstrated the ability to answer medical questions with higher accuracy than humans, including students from the last year of medical school.