Artificial lung systems rely almost exclusively on hollow fiber membrane (HFM) bundles, where gas exchange is constrained by heterogeneous flow distribution and thrombogenic blood-material interfaces. Here, we introduce an architecture-driven design framework for artificial lungs based on additively manufactured triply periodic minimal surface (TPMS) membranes. In contrast to discrete fiber bundles, TPMS membranes form continuous three-dimensional architectures that simultaneously regulate perfusion pathways, diffusion interfaces, and blood-material interactions. Computational fluid dynamics and multiphysics transport simulations reveal that membrane architecture governs gas exchange through coupled effects of membrane thickness, unit cell size, and three-dimensional flow topology. Optimized TPMS architectures achieved on average up to ∼88% higher oxygen transfer rates across the investigated flow regime compared to conventional HFM while enabling substantially more homogeneous flow fields and reduced stagnation zones. Experimental screening identifies polydimethylsiloxane-based printable elastomers compatible with thin gas-permeable membranes and endothelial functionalization. The biohybrid endothelial interface mitigates thrombogenic interactions, while maintaining gas transport. Computed tomography-derived implant geometries demonstrate the feasibility of translating architected membrane systems into anatomically integrated artificial lungs. Together, these results establish a new design paradigm for artificial lungs, in which membrane architecture becomes the primary determinant of gas transport, flow distribution, and hemocompatibility.
Artificial intelligence (AI) is increasingly being integrated into healthcare systems and has the potential to improve health outcomes. In Sub-Saharan Africa (SSA), however, concerns remain that AI may either reduce or exacerbate existing health inequities depending on how it is developed, governed, and implemented. This scoping review aimed to map and synthesise the existing evidence on the implications of AI for health equity among marginalised populations in Sub-Saharan Africa. PubMed, Web of Science, Scopus, and selected grey literature sources were searched between February and March 2026. Peer-reviewed and grey literature examining AI applications, governance, or implementation in healthcare involving marginalised populations or health systems within SSA were eligible for inclusion. The review followed the Arksey and O'Malley methodological framework and the PRISMA-ScR reporting guideline. Two reviewers independently screened sources of evidence and extracted data using a standardised charting form, and findings were synthesised thematically. Twenty-three sources of evidence met the inclusion criteria. Two dominant narratives emerged. AI may reinforce existing inequities through digital infrastructure gaps, algorithmic bias, under-representation of African datasets, weak governance, and data colonialism. Conversely, AI has the potential to improve health equity by expanding healthcare access, strengthening disease surveillance, supporting health system planning, and improving access to specialised services. Across the literature, AI's impact consistently depended on equitable infrastructure, inclusive governance, and context-specific implementation. AI has considerable potential to advance health equity in SSA. However, achieving equitable benefits requires investment in digital infrastructure, representative data systems, ethical governance, and inclusive policies. This paper situates AI within the context of health equity in Sub-Saharan Africa.Main findings: Artificial intelligence in African health systems presents both opportunities to expand healthcare access and risks of reinforcing existing inequalities driven by infrastructure gaps, data bias, and governance challenges.Added knowledge: This review provides a focused synthesis of how artificial intelligence specifically affects marginalised populations in Sub-Saharan Africa, highlighting the structural conditions that shape equitable or inequitable outcomes.Global health impact for policy and action: The findings emphasise the need for deliberate policy action on inclusive data systems, digital infrastructure investment, and contextually grounded governance to ensure artificial intelligence advances health equity rather than deepening disparities.
In the field of 3D printing technology in the medical application is becoming more and more widely, especially in orthopaedic surgery, its importance is increasingly prominent. This technology through precise biological tissue engineering, and can be produced with the patient's own tissue matching the artificial bone implants, thus in the bone graft surgery and cosmetic surgery play a key role. With the deepening of the research found that different material and printing method can significantly affect the artificial bone implant biological specificity and clinical effect. In addition, the personalization of 3D printing implants can better adapt to the patient's anatomical structure, improve the success rate of surgery and patient satisfaction. In recent years, 3D printing technology in expanding the application of orthopedic surgery, its potential value also gradually by mining. Therefore the author through the reviews in recent years, 3D biological technology and 3D printing to print and the research status of the artificial bone implants, as well as their application in orthopedic surgery and potential value, in order to provide new ideas for the research of the field.
This Viewpoint explores how artificial intelligence may mitigate or exacerbate inequities in correctional health systems and outlines policy and implementation considerations necessary to ensure health equity for incarcerated populations.
Globally, AI and technology are being integrated into nursing education and practice, providing students with realistic patient care scenarios for safe, hands-on learning. These technological advancements and AI have also aided Nurses in documentation and data input, allowing nurses to focus more on patient care. However, the adoption of AI and other technologies in Nigeria's nursing environment is still in its early stages compared to more developed countries.This study investigates the readiness of Nigerian nurses and nurse educators in Oyo state to integrate technology and Artificial Intelligence (AI) into their training and clinical practice. A quantitative survey design was employed, with 115 registered nurses from Oyo State, Nigeria, participating. The study reveals moderate levels of technological use, with 73% of respondents having used some form of technology or AI tools in their practice. Perceptions towards AI integration were predominantly positive, with over 95% agreeing on its potential to improve healthcare delivery and patient outcomes. Readiness to utilise AI was high, with almost all respondents (97.4%) ready to utilise AI tools in practice. Key barriers to adoption included unavailability of necessary technologies (65.2%), insufficient infrastructure (62.7%), and inadequate training (60.8%). The study found significant associations between previous utilisation of AI, practice designation and readiness to utilise AI, as well as between nursing roles and perceptions of AI integration. These findings highlight the need to develop and implement comprehensive AI training programs for Nurses, focusing on hands-on experience and understanding of AI applications in patient care. Given the high level of readiness, tailored training programs can help close the gap created by inadequate training.
Given the pivotal role of imaging in diagnosing urological cancers, artificial intelligence (AI) has emerged as a promising tool to improve diagnostic accuracy and reliability. This study systematically evaluates the diagnostic performance of AI models in radiologic imaging of urological cancers. A systematic search was conducted in four electronic databases up to June 2026 to identify studies that applied AI algorithms for the diagnosis of urological cancers using CT, MRI, or ultrasound. Eligible studies reported diagnostic accuracy metrics for AI models, with clinician comparator data extracted when available. A bivariate random-effects model was used to pool sensitivity, specificity, and AUC values. Subgroup analyses were conducted to examine diagnostic performance across different cancer types and imaging modalities, and to explore potential sources of heterogeneity. Study quality was assessed using the QUADAS-2 tool. A total of 110 studies were included in the meta-analysis. AI models achieved pooled sensitivity and specificity of 0.85 (95% CI: 0.83-0.87) and 0.83 (95% CI: 0.80-0.86), with an AUC of 0.91 (95% CI: 0.88-0.93). Clinicians demonstrated a pooled sensitivity of 0.82 (95% CI: 0.79-0.85) and specificity of 0.68 (95% CI: 0.62-0.73), with an AUC of 0.83 (95% CI: 0.80-0.86). Subgroup analyses indicated that AI models showed overall diagnostic advantages across cancer types and imaging modalities, particularly in specificity, AUC, and diagnostic odds ratios, although clinicians demonstrated higher sensitivity in the prostate cancer and MRI subgroups. AI models demonstrate strong diagnostic performance across various urological cancers and imaging modalities, showing potential as supportive tools in radiological workflows. Further prospective, standardized, and multi-center evaluations are warranted to confirm AI's clinical utility across diverse diagnostic tasks in urological oncology.
Artificial intelligence (AI) has achieved remarkable success in the diagnosis of Alzheimer's disease (AD) in the literature, where many of the models use multi-modal methods including neuroimaging, cerebrospinal fluid, genetics, and cognitive assessment. But clinical adoption of these systems is still limited since most systems are developed in an idealized setting, as cost-effective and specialized diagnostic studies are not universally accessible. We discuss the translation of benchmark performance of AI to real-world dementia care pathways. A practical framework that would be useful for scalable, equitable, and clinically deployable AI-assisted dementia care. In fact, recent advancements in blood-based biomarkers such as plasma phosphorylated tau, glial fibrillary acidic protein, and neurofilament light chain are providing new opportunities for a flexible and minimally invasive diagnosis method. Based on these advances, we propose a clinically grounded AI-assisted cascading model which mirrors real-world workflows via progressive screening, biomarker-guided assessment, selective imaging escalation, and longitudinal prognostic monitoring. We further discuss enabling methods such as sequential decision-making, reinforcement learning, cost-sensitive learning, missing-modality robustness, and explainable AI. Finally, we outline the challenges for data design, for future validation and integration into healthcare systems, and ethical use.
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Artificial intelligence (AI) has emerged as a transformative tool in gastrointestinal (GI) endoscopy, addressing challenges in detection, diagnosis, and decision-making. In upper GI endoscopy, AI supports blind spot monitoring, Helicobacter pylori diagnosis, and the identification of premalignant and malignant lesions, with high accuracy and reduced miss rates. In lower GI endoscopy, computer-aided detection improves adenoma detection, whereas computer-aided diagnosis supports "resect-and-discard" and "diagnose-and-leave" strategies. However, real-world benefits remain modest, with concerns regarding overdetection and variable performance across lesion types and colon segments. In inflammatory bowel disease, AI standardizes endoscopic and histologic scoring, reduces interobserver variability, and accelerates capsule endoscopy interpretation, including high diagnostic accuracy for Crohn's disease. Pancreatobiliary applications, including endoscopic ultrasound, endoscopic retrograde cholangiopancreatography, and cholangioscopy, demonstrate strong performance in differentiating pancreatic masses and biliary strictures and in predicting postprocedural complications. Despite expert-level performance across multiple domains, most studies remain single-center or retrospective, and explainability, workflow integration, medicolegal responsibility, and cost-effectiveness continue to limit adoption. Emerging solutions, including explainable AI and AI-generated common data model-compatible reports, may bridge these gaps. With rigorous multicenter validation and real-world implementation, AI can evolve from an experimental adjunct into a core component of routine endoscopic practice.
Objectives: Oral potentially malignant disorders may present as white, red, or mixed red-white lesions and require accurate early recognition. This study evaluated whether texture-analysis features extracted from clinical digital images could distinguish oral precancerous lesions from other oral mucosal lesions and normal mucosa using gray-level co-occurrence matrix (GLCM), gray-level run-length matrix (GLRLM), and wavelet analysis. Materials and Methods: Sixty-four clinical digital images were selected according to predefined inclusion and exclusion criteria. The dataset included leukoplakia, erythroplakia, oral submucous fibrosis, candidiasis, lichen planus, leukoderma, frictional keratosis, white spongy nevus, and normal mucosa. Regions of interest were extracted from each image, and texture features were derived using GLCM, GLRLM, and wavelet analysis. A support vector machine (SVM) classifier was then used to categorize images as oral precancerous lesions or non-precancerous/normal mucosa. Results: GLCM yielded the highest classification accuracy (88%), followed by GLRLM (81%) and wavelet analysis (79%). The corresponding sensitivity values were 77%, 64%, and 60%, and the specificity values were 93%, 90%, and 89%, respectively. The positive predictive values were 83% for GLCM, 75% for GLRLM, and 75% for wavelet analysis. Conclusion: GLCM-based texture features provided the best diagnostic performance in this dataset. These image-analysis methods may be useful as non-invasive adjuncts to conventional clinical examination and histopathological diagnosis; however, larger datasets and external validation are required before clinical implementation.
Previous research has demonstrated changes in neural oscillations associated with varying levels of roughness during active-touch exploration of surfaces. In the present study, we aimed to investigate changes in neural oscillatory activity and softness perception during touch exploration of skin. Two experiments were conducted. Study 1 evaluated active touch of artificial skin samples mounted to a purpose-built touch sensor, whereas Study 2 investigated active stroking of one's own forearm. In both experiments, the substrates were treated with commercially available bar soaps to deliver either a soft or draggy skin feel. Oscillatory brain activity was measured using a 129-channel electroencephalography system. In 31 participants, changes in oscillatory band power were evaluated in relevant frequency bands during touch exploration periods. For the artificial skin study, the soft condition led to lower alpha-band power over bilateral somatosensory cortices, which has previously been proposed as a marker of reduced roughness, compared to the draggy condition. Similar results were obtained during the self-touch paradigm, which additionally led to reduced theta-band changes over the frontal and central-parietal electrodes indicating modulation of activity involved in affective (pleasant) touch. Using a novel and highly controlled experimental approach using a novel artificial skin paradigm and active exploration during self-touch of participants own skin, we were able to demonstrate for the first time the neural correlates associated with softness perception of skin and their impact on brain activity associated with affective touch, thereby advancing our understanding of brain oscillatory activity during active-touch exploration of skin. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
Methods: In addition to traditional testing methods, the use of PSMA-directed imaging to guide biopsy procedures using Multiparametric Magnetic Resonance Imaging (mpMRI) allows for better localization and characterization of lesions. Researchers have begun to develop and continue to innovate Molecular & Imaging processes, called Liquid Biopsies, that are now being used to assess health risk factors associated with PC, by sampling macro-molecules from various biological liquids. Recently, the use of Artificial Intelligence (AI) and Machine Learning Models for improving the speed and accuracy of lesion detection and gland segmentation has significantly increased both precision and consistency in these areas. Results: However, there is still great concern regarding overdiagnosis and lack of standardization associated with all types of molecular and imaging techniques currently available. Conclusion: This review provides a comprehensive overview of the currently available diagnostic modalities for prostate cancer, including their weaknesses as well as gaps in clinical translation and standardization, which can assist with providing guidance for future development of diagnostic innovations.. The number of new cases of prostate cancer in global incidence over the next two decades is expected to double worldwide. The need for improved diagnostic tools that are more precise and less invasive is becoming increasingly urgent due to this projected increase.Liquid biopsies (CTCs, cfDNA/ctDNA, ctRNA, and exosomes) provide an opportunity for less-invasive assessment for diagnosis, prognosis, selection of therapy, and follow-up, but there are still significant barriers to their routine use: 1) limited sensitivity of these markers in patients in the early stages of disease; and 2) the absence of standardized protocols or reference materials for performing liquid biopsies.Emerging urine and blood biomarker panels demonstrate utility in distinguishing PC from BPH and for risk stratification according to Gleason score, but most of these are preliminary and require additional validation before they can be integrated into routine patient care.PET scans that look for prostate-specific membrane antigen, MRI images, and PSA blood test results provide key information for accurate diagnosis of and treatment planning for men with prostate cancer. Current imaging methods of differentiating prostate cancer lesions are DWI and PI-RADS, each of which is evaluated for risk, and help physicians determine what treatment options are available to them. Hybrid PET/MRI imaging produces both anatomical and functional imaging modalities, providing information for physicians to make more accurate diagnoses and more collaborative decisions about treatment.Artificial Intelligence (AI) is being used in the diagnosis, detection of lesions, prediction of risk, and treatment decisions based on cancer images, pathology, history, and patient data, but issues surrounding the quality of data, interpretability of models, and generalizability of results continue to restrict full adoption of AI in standard care pathways for prostate cancer.
BackgroundWith population aging and an increase in sports injuries, articular cartilage wear has become increasingly severe, significantly impairing patients' quality of life. Cross-shear motion is a common loading pattern in daily joint activities, yet its effects on cartilage and counterpart materials remain insufficiently investigated.ObjectiveTo investigate the wear behavior and underlying mechanisms of articular cartilage under cross-shear motion, and to compare the wear resistance of different artificial joint materials, so as to propose material optimization strategies suitable for this motion pattern.MethodsFresh bovine knee femoral cartilage was used to simulate the human cartilage environment under various cross-shear conditions and loading regimes. Stepwise loading, microhardness testing, cartilage compression deformation measurements, and surface wettability tests were performed to systematically analyze the tribological characteristics and wear mechanisms of each material. The evaluated materials included CoCrMo, ultra-high molecular weight polyethylene, and polyether-ether-ketone (PEEK).ResultsCross-shear motion significantly increased wear in all materials. CoCrMo exhibited the largest increase (ΔK = 230.51 ± 25.67%), while PEEK showed the smallest increase in wear rate (138.37%). No significant linear correlation was found between material hardness and cross-shear wear rate (r = -0.32, p > 0.05). In terms of dynamic wettability, PEEK performed best, with a hysteresis angle 38.46% lower than that of natural cartilage, and its energy dissipation parameters were close to those of natural cartilage, indicating the best cartilage compatibility.ConclusionUnder cross-shear conditions, PEEK demonstrates the best overall wear resistance and biomechanical compatibility, suggesting its potential as a promising material for joint repair. The wear mechanisms revealed in this study provide experimental evidence and reference for performance optimization and clinical selection of artificial joint materials.
Robotic technologies are increasingly being integrated into cosmetic dermatology; however, the scientific development of this field has not been comprehensively evaluated. This study aimed to characterize the global research landscape of robotic applications in cosmetic dermatology through a bibliometric analysis. Publications were retrieved from the Web of Science Core Collection, and 48 publications, including 43 articles and 5 reviews, were included after screening. Biblioshiny and VOSviewer were used to analyze publication trends, journals, countries, collaboration networks, keyword co-occurrence, thematic structure, reference publication year spectroscopy (RPYS), and cited reference co-citation analysis. Scientific production remained limited but gradually increased over time. Dermatologic Clinics and Dermatologic Surgery were the most productive journals. The United States demonstrated the greatest international collaboration, corresponding authorship, and citation impact. Keyword and thematic analyses identified hair restoration, particularly robotic follicular unit extraction, as the dominant research focus, while laser-assisted procedures, skin rejuvenation, and facial aesthetic interventions represented smaller but evolving areas of investigation. RPYS and reference co-citation analyses further demonstrated that robotic hair restoration has formed the principal intellectual foundation of the field. Overall, robotic technologies in cosmetic dermatology represent an emerging field with steadily increasing scientific interest. Although hair restoration currently dominates the literature, continued technological advances are expected to broaden their use and stimulate further investigation of laser-assisted procedures, skin rejuvenation, and artificial intelligence-assisted technologies.
Ergonomics is essential for ensuring safe, high-quality surgical care. However, existing studies vary widely in their clinical settings, terminology, and assessment methods. This scoping review aimed to map the terminology and methods used to evaluate surgeons' ergonomics in real-world surgical environments. We systematically searched MEDLINE, Embase, CENTRAL, IEEE Xplore, the WHO International Clinical Trials Registry Platform, and ClinicalTrials.gov from inception to July 1, 2025 for studies that assessed physical, cognitive, or organizational ergonomics. Two reviewers independently screened titles and abstracts, assessed full texts, and extracted study characteristics and outcomes. Assessment tools were categorized as self-reported, observational, direct or instrumental, or computer based. The review protocol was registered in PROSPERO (CRD420251088370). Ninety-nine studies met the inclusion criteria. Minimally invasive surgery predominated: 60 studies involved laparoscopic surgery (including thoracoscopic and other endoscopic minimally invasive approaches), and 36 evaluated robotic surgery. Comparative designs most frequently compared laparoscopic versus robotic surgery (n = 14). Physical, cognitive, and organizational ergonomics were assessed in 64, 49, and 19 studies, respectively, with 31 addressing multiple domains. Considerable variation was observed in terminology and assessment methods. Physical ergonomics was most commonly evaluated using observational or instrumental approaches, whereas cognitive ergonomics primarily relied on self-reported measures. Computer-based assessments, including artificial intelligence-assisted tools, have recently emerged. This review provides a structured framework for assessing ergonomics in surgery and highlights substantial heterogeneity in terminology and assessment methods. Standardized, multimodal, and objective approaches are needed to enhance comparability across studies.
Research on potentially inappropriate care at the end of life in older adults rarely distinguishes between acute and elective care. As elective care is plannable, it allows for timely deliberation on its appropriateness. This study aimed to explore elective hospital care and specific elective treatments in the final three months of life to identify areas warranting re-evaluation. A nationwide retrospective observational study was conducted using linked administrative data, including data from electronic patient records and health insurance claims from all secondary and tertiary hospitals across the Netherlands. Included were 104,544 older adults aged ≥ 65 who died between April and December 2019. In the final three months of life, 41.7% of older adults received no hospital care, 14.5% received outpatient care only, 42.4% were admitted, and 15.9% were electively admitted. Some of the most frequent elective treatments were chemo- and/or immunotherapy (12,102), radiotherapy (13,924), dialysis (30,087), and cataract surgery (584). Treatments during elective admissions included gastrointestinal endoscopies (979), gastrointestinal tract organ resections (229), gastrointestinal-ostomy surgeries (228), exploratory laparotomies (177), heart valve surgeries (179), arterial interventions (coronary: 266; aorta/side branches: 147; peripheral: 786), hip surgeries (136), leg amputations (115), and artificial feeding procedures (683). A substantial proportion occurred in the final month of life. Elective hospital care is common in the final three months of life among older adults, with limited time to experience meaningful benefit. These findings highlight the need to reconsider the appropriateness of elective care at the end of life to better align care with patients' preferences.
To evaluate the feasibility and early safety observations of Apple Vision Pro as the intended primary intraoperative visualization platform during cataract surgery. This prospective, single-center, single-arm feasibility study evaluated Apple Vision Pro integrated with a ZEISS ARTEVO 850 three-dimensional digital surgical microscope. Forty-five patients undergoing cataract surgery were enrolled. Apple Vision Pro was used as the intended primary visualization platform, with conventional microscope oculars and an ARTEVO 850 three-dimensional monitor available as backup. Primary endpoints were procedure completion without device-related conversion and device-related intraoperative complications. Secondary endpoints included surgeon-reported workload and usability, operating room staff experience, and postoperative visual outcomes. Forty-four of 45 procedures (97.8%) were completed using Apple Vision Pro without device-related conversion. One procedure required conversion to backup visualization after a transient image interruption perceived as image freezing; no patient harm or surgical complication occurred. No device-related intraoperative surgical complications were observed, although the study was not powered to assess rare complications. Mean patient age was 70.6 ± 8.8 years, and mean operative time was 15.4 ± 4.9 minutes. Mean best-corrected visual acuity improved from 0.50 ± 0.40 logMAR preoperatively to 0.02 ± 0.07 logMAR at 1 month. Surgeons reported low perceived workload on the modified NASA Task Load Index, and operating room staff reported minimal workflow disruption. In this prospective single-center feasibility cohort, Apple Vision Pro enabled completion of most selected cataract procedures using the intended primary visualization pathway within a defined workflow with immediate backup visualization. These preliminary findings suggest that wireless, head-mounted stereoscopic visualization may be feasible in a specialized, backup-supported surgical environment and support further controlled evaluation. However, these data cannot guide universal routine practice or establish comparative safety, clinical advantage, ergonomic benefit, routine clinical suitability, cross-specialty applicability, or detection of rare complications. Cataract surgery removes a cloudy lens from the eye and replaces it with an artificial lens. During this operation, surgeons need a clear, steady, three-dimensional view of the eye. Traditional surgical microscopes and screens can limit where a surgeon looks and sits. In this small study at one surgery center, 45 people having cataract surgery took part. The surgeons used Apple Vision Pro as their main way to view the live image from a surgical microscope. Standard microscope eyepieces and a three-dimensional monitor were ready to use if needed. Surgeons completed 44 of 45 operations using Apple Vision Pro from beginning to end. In one operation, the image briefly stopped moving, so the surgeon immediately switched to the backup monitor. The operation was completed without harm or a surgical complication. During the one-month follow-up, no complications were considered related to this viewing system. On average, participants’ vision improved after surgery. Surgeons and operating room staff reported that the system was workable in this setting. However, this study did not compare Apple Vision Pro with standard ways of viewing cataract surgery. Larger studies are needed to learn more about image delay, comfort, safety, and whether this technology can be used safely beyond specialized, backup-supported settings.
The human lung microbiome is increasingly recognized as a key player in drug metabolism, yet it remains largely understudied. To replicate this complex physiological environment in a controlled setting, we developed a simplified artificial lung microbiome model composed of four representative bacterial species: Pseudomonas koreensis, Rothia aeria, Neisseria cinerea, and Streptococcus downei. We successfully established a stable 10-day co-culture at 34°C using brain heart infusion medium, as validated by quantitative PCR, viability PCR, and conventional microbiological methodologies. Integrated bioinformatic analyses revealed a variety of potential microbe-microbe interactions, which were supported by metaproteomic analysis using mass spectrometry. Our model provides a foundation for in-depth studies, such as, for example, the effects of pulmonary drugs on the lung microbiome, and how, in turn, the microbiome may influence therapeutic outcomes.IMPORTANCEOnce thought to be sterile, the lung microbiome is now understood to host a dynamic microbiome capable of influencing respiratory health and disease. Understanding interactions among the microbes within this community is essential, as these relationships may drive disease progression or foster resilience in both acute and chronic inflammatory conditions. We developed a reproducible lung microbiome model comprising Pseudomonas koreensis, Rothia aeria, Streptococcus downei, and Neisseria cinerea. Simplified models enable controlled studies to dissect specific microbial interactions, laying the foundation for insights into lung microbial ecology. In the future, more complex models will enhance our understanding of microbial roles in disease outcomes, with our platform serving as a basis for testing therapeutic strategies.