Prehospital real-time AI for trauma mortality prediction: a multi-institutional and multi-national validation study

Domain: neuroscience
Published: 2026-01-07
Editorial outcome: major_revision

Abstract
Early identification of high-risk trauma patients in the prehospital setting is crucial for optimizing resource allocation and improving survival. We developed and externally validated a real-time AI model predicting emergency room mortality using 21 prehospital variables. Model development and internal validation utilized the Korean Trauma Data Bank (KTDB; 204,189 patients), and external validation included four South Korean trauma centers (8,358 patients) and one Australian Level 1 center (3,578 patients). Our Prehospital-AI model, an ensemble of XGBoost, LightGBM, and random forest, achieved an AUROC of 0.923 (sensitivity: 0.780, specificity: 0.880) on the test set, outperforming the shock index (AUROC: 0.712). External validation yielded AUROCs of 0.925–0.956 across South Korean centers and 0.895 in the Australian center. Here we show that the Prehospital-AI model enables accurate, real-time risk assessment in the prehospital setting, outperforming traditional triage tools and improving trauma system efficiency. Nonetheless, additional multinational studies are warranted to further evaluate its generalizability across diverse trauma care systems.

Introduction
Trauma is a major cause of morbidity and mortality worldwide, necessitating early and accurate risk stratification for optimal patient management1. Timely trauma severity assessment improves outcomes, resource allocation, and intervention decisions. Traditional scoring systems such as the Injury Severity Score (ISS)2, New Injury Severity Score (NISS)3, and Trauma and Injury Severity Score (TRISS)4 rely on post-admission data, making them unsuitable for prehospital triage.

Prehospital trauma assessment is challenging due to limited clinical information and the unpredictable nature of injuries. Existing triage guidelines5,6, while useful, often require experienced personnel and can be subjective, leading to variability in decision-making. Additionally, the time-sensitive nature of prehospital care demands rapid, reliable, and automated risk assessment tools7.

Artificial intelligence (AI) has the potential to improve trauma outcome prediction and early risk stratification8–14. Our team previously developed AI models for predicting in-hospital15–17 and emergency department (ED)18 mortality, but these relied solely on data available at ED admission, lacking prehospital information. Given the need for real-time risk assessment during emergency transport, we developed the Prehospital-AI model, designed to predict ED mortality using only prehospital variables. Unlike traditional methods requiring post-admission data, this AI-driven approach enables real-time mortality risk estimation, allowing hospitals to prepare in advance, optimize resource use, and expedite critical interventions.

We hypothesized that prehospital trauma data alone could reliably predict ED mortality, even without hospital-based assessments such as ISS. To test this, we developed the Prehospital-AI model, designed to predict mortality using only prehospital variables. This AI-driven approach enables real-time risk assessment during emergency transport, minimizing human subjectivity and reducing errors compared to conventional triage methods5,6. By transmitting mortality probabilities to receiving hospitals, the model has the potential to improve prehospital triage, optimize resource allocation, and facilitate timely, life-saving interventions before patient arrival.

Results
Patient characteristics
Supplementary Table 1 summarizes the prehospital characteristics of the Korean Trauma Data Bank (KTDB) datasets and the external validation datasets from four South Korean trauma centers and one Australian Level 1 trauma center. Notably, the WH dataset in Australia had a high proportion of missing data, with 9 of 21 prehospital variables missing entirely: time of injury occurrence, job-related injury status, hospital arrival route, mode of arrival, insurance type, prehospital cardiac arrest status, prehospital notification status, prehospital body temperature, and protective equipment use. Additionally, six physiological variables also had significantly higher missing rates (74.6–77.1%).

In total, 9 variables were completely missing, and 6 had a high proportion of missing values, underscoring the WH dataset’s limitations compared to the more complete data from the South Korean trauma centers. Despite these gaps, the WH dataset provided valuable external validation by representing a distinct Australian trauma population, whereas the model was trained on Korean data.

K-fold cross-validation in KTDB
Supplementary Table 2 presents the results of 5-fold cross-validation, evaluating the performance of individual ML models, ensemble configurations, and the SI. Among the single ML models, XGBoost, LightGBM, and RF exhibited the highest AUROC and balanced accuracy, outperforming Adaptive Boosting (AdaBoost)19, Gradient Boosting Machine (GBM)20, Categorical Boosting (CatBoost)21.

To optimize predictive performance, various ensemble models were explored. The Prehospital-AI model, an ensemble of XGBoost, LightGBM, and RF, achieved the highest AUROC (0.9433 ± 0.0053) and balanced accuracy (0.8687 ± 0.0076), surpassing all single models and alternative ensembles. It also demonstrated well-balanced sensitivity (0.8735 ± 0.0176) and specificity (0.8639 ± 0.0039), ensuring reliable classification of trauma patient outcomes. However, the AdaBoost model achieved the highest AUPRC (0.4249 ± 0.0407), compared with 0.4057 ± 0.0375 for the Prehospital-AI model. (Supplementary Table 2)

In contrast, the SI showed significantly lower performance, with AUROC values of 0.6656 ± 0.0203 and balanced accuracy of 0.6641 ± 0.0088 at TH=0.8241 (determined using the Youden index22). The performance further declined at the recommended threshold (TH=0.9000)23. Figure 1 compares the AUROC and AUPRC curves, highlighting the superior predictive performance of the Prehospital-AI model over individual ML models and the SI.

Model calibration
We found that our original model demonstrated suboptimal calibration performance. The recalibrated model demonstrated improved performance, achieving a calibration slope of 0.982 and a Brier score of 0.0069. Figure 2 illustrates the calibration performance of our prediction model before and after applying the beta calibration method. The blue line represents the original model’s output, which shows poor alignment with the ideal (diagonal) line, indicating suboptimal calibration. In contrast, the red line depicts the model’s output after beta calibration, demonstrating a close fit to the diagonal and indicating improved alignment between predicted probabilities and observed outcomes. Quantitatively, the recalibrated model achieved a calibration slope of 0.982 and an intercept of 0.013, both suggesting good calibration. The Brier score improved significantly from 0.1018 (uncalibrated) to 0.0069 (calibrated), and the mean calibration error was reduced to 0.039. These results confirm that the post hoc beta calibration method effectively enhanced the model’s reliability in probability estimation. Figure 3 illustrates the performance of the AI models after calibration.

Feature analysis
Figure 4 presents the Shapley Additive Explanation (SHAP)24 summary plot, displaying the top 20 variables with the highest impact on the Prehospital-AI model’s predictions. Key predictors included transport to the hospital by other vehicles, injury mechanism (slip down), SpO₂ >95%, and alert status. Features such as high SpO₂ levels and being alert were strongly associated with survival, aligning with clinical expectations that stable physiological states correlate with better outcomes. Conversely, lack of protective equipment, unknown accident location, and prearrival report status were among the variables that contributed significantly to mortality predictions.

Out of the 149 features analyzed, 125 had a measurable impact on model predictions, while the remaining features had SHAP values of 0, indicating no contribution to the model’s decision-making process. Supplementary Table 3 in the Supplement provides a detailed breakdown of the absolute SHAP values for all impactful features.

Testing data results and comparison with SI
Supplementary Table 4 in the Supplement summarizes the performance of the Prehospital-AI model on the testing dataset, demonstrating strong predictive capability. The calibrated model achieved a sensitivity of 0.8399, specificity of 0.8828, accuracy of 0.8824, balanced accuracy of 0.8613, AUPRC of 0.3927 and AUROC of 0.9479, indicating excellent discrimination between survivors and deceased patients. These results are highly consistent with the 5-fold cross-validation findings in the KTDB dataset, where the model attained a balanced accuracy of 0.8687 and an AUROC of 0.9433. The close agreement between cross-validation and independent testing performance confirms the robustness and generalizability of the model.

To further assess its clinical utility, the Prehospital-AI model was compared with the SI. For a fair comparison, the performance metrics was recalculated on the subset of the testing data where SI could be computed. The SI exhibited significantly weaker predictive performance, with an AUROC of 0.7117. The ROC curves in Supplementary Fig. 1 visually highlight the performance disparity.

External validation data results
Supplementary Table 5 presents the external validation performance of the Prehospital-AI model, demonstrating its robust predictive capability across diverse clinical settings. In the four South Korean trauma centers, the calibrated model consistently exhibited strong predictive performance, with balanced accuracy ranging from 0.8255 to 0.8880. These results closely align with the 5-fold cross-validation outcome (0.8687) and the independent testing dataset performance (0.8613), confirming the model’s generalizability across different trauma centers.

At WH in Australia, the validation posed unique challenges due to a high proportion of missing prehospital data. Of the 21 prehospital variables, only six were fully available, while nine were entirely missing and thus excluded. Furthermore, six key physiological indicators, such as systolic and diastolic blood pressure, pulse rate, respiratory rate, oxygen saturation, and the AVPU scale, were missing in ~75% of cases. Despite these substantial data gaps, the Prehospital-AI model maintained a balanced accuracy of 0.7614 and an AUROC of 0.8946, demonstrating its ability to adapt to incomplete datasets while still providing clinically relevant predictions.

To assess its clinical utility, the model was compared against the SI across all external validation sites. The Prehospital-AI model consistently outperformed SI, achieving AUROC values between 0.8759 and 0.9976, whereas SI showed significantly lower AUROCs, ranging from 0.6580 to 0.9156. Notably, in CBUH, SI exhibited particularly poor predictive performance (AUROC: 0.6580, balanced accuracy: 0.6243), reinforcing the limitations of conventional physiologic-based triage tools.

Decision curve analysis (DCA)
DCA of the AI models in the KTDB test dataset is shown in Fig. 5. The blue line represents the calibrated AI model, which consistently demonstrated a higher net benefit than the default strategies of treating all (red) or treating none (green), particularly within the lower threshold range. This indicates that the calibrated AI model may enhance clinical applicability by optimizing decision-making across plausible thresholds. These findings underscore the value of calibration in improving the real-world utility of AI-driven predictions. The decision curve analyses of the external validation datasets are presented in Supplementary Fig. 6, which similarly demonstrate the superior net benefit of the calibrated AI model across diverse hospital cohorts. Similarly, DCA of the external validation datasets showed that the calibrated AI model provided a higher net benefit, particularly within the 0–1% mortality range, which reflects real-world prevalence. Notably, in the high-risk subgroup characterized by altered mental status, the AI model also yielded superior net benefit (Supplementary Fig. 7).

Deployment for trauma centers
The Prehospital-AI model is now deployed for real-time clinical use in trauma centers, enabling instant mortality risk prediction to support critical decision-making in prehospital and early hospital settings. The web-based interface allows emergency teams to input key patient details, including age, gender, injury mechanism, and vital signs, in seconds (Supplementary Fig. 8). Upon submission, the system rapidly processes the data and generates a mortality probability score, which is displayed in Fig. 6.

Crucially, the AI-driven predictions are automatically transmitted to receiving trauma centers, allowing hospital teams to anticipate high-risk cases, optimize resource allocation, and activate specialized trauma teams before the patient arrives. This seamless prehospital-hospital integration enhances efficiency in trauma triage, expedites life-saving interventions, and improves overall patient outcomes.

Methods
Ethical approval
This study was approved by the IRBs of five participating institutions. Ethical approval was obtained from CHH (CHH-2024-L03), CBUH (CBNUH 2024-10-017), GUGC (GFIRB2024-357), CNUH (CNUH-2024-016), and WH (2024/PID0326).

Study design and dataset
This retrospective cohort study was conducted in accordance with the TRIPOD (Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis) + AI and TRIPOD-cluster guidelines (Supplementary Checklist)30,31. The AI model was developed using data from the KTDB, a national trauma registry managed by the Korean National Emergency Medical Center. The KTDB includes trauma data from 19 centers across South Korea, comprising all 17 regional trauma centers, which serve as the highest-level trauma centers, and 2 trauma training centers. The dataset used for model training spanned the years 2017 to 2022 (dataset identification number K2024-03-2-02-29) and included 204,189 patients.

For external validation, the model was further tested on independent datasets obtained in 2023 from four regional trauma centers in South Korea and from 2022 to 2024 at one Level 1 trauma center in Australia. The South Korean validation sites included Chonnam National University Hospital (CNUH, n=1675), Cheju Halla General Hospital (CHH, n=2309), Chungbuk National University Hospital (CBUH, n=1277), and Gachon University Gil Medical Center (GUGC, n=3097). The Australian validation dataset was obtained from Westmead Hospital (WH, n=3578). A total of 11,936 trauma patients were included in the external validation dataset.

Study outcome and patient selection
The primary outcome was ED mortality, defined as death occurring in the ED. Patients who died after ICU or ward admission were considered survivors. Eligible patients were those registered in the KTDB, admitted through trauma EDs at four Korean trauma centers, or transported under major trauma protocols to the Australian trauma center. Exclusion criteria included non-trauma-related deaths, transfers to other facilities, medical futility discharges, or unclear outcomes. The detailed patient selection process is illustrated in Fig. 7.

Training, testing, and external validation datasets
The KTDB dataset was divided into training (80%) and testing (20%) subsets for the development and evaluation of the Prehospital-AI model. The training set consisted of 163,351 patients, while the testing set included 40,838 patients. For external validation, the model was tested on 11,936 independent patient records from four South Korean trauma centers and one Level 1 trauma center in Australia. Further details on dataset distribution are provided in Supplementary Methods 1.

Prehospital variables and data preprocessing
A summary of the variables used in the Prehospital-AI model is presented in Supplementary Table 8. The AI model was developed using 21 prehospital variables from the KTDB, categorized into demographics, injury details, physiological data, and transport and hospital arrival details. Age and gender were included as demographic variables. Injury-related factors included intentionality, injury mechanism, type of injury, time of injury, protective equipment use, and occupation-related status. Physiological data encompassed AVPU scale, systolic and diastolic blood pressure, pulse rate, respiratory rate, body temperature, and oxygen saturation. Transport and hospital arrival details included accident location, hospital arrival route, mode of arrival, insurance type, prehospital cardiac arrest status, and prehospital notification status. Details on prehospital variables and data preprocessing are provided in Supplementary Methods 2.

Prehospital-AI model based on ensemble machine learning
To improve predictive performance, our Prehospital-AI model uses an ensemble machine learning (ML) approach, integrating extreme gradient boosting (XGBoost)32, light gradient boosting machine (LightGBM)33, and random forest (RF)34. This ensemble approach leverages the strengths of multiple models to enhance generalization and robustness beyond what could be achieved with a single model. A detailed description of the model selection process, hyperparameter tuning, and internal validation strategy, including 5-fold cross-validation, is provided in Supplementary Methods 3.

Model calibration
We applied a post hoc calibration procedure using the beta calibration method based on the beta distribution35. Beta calibration is a post-processing method used to improve the probability estimates from binary classifiers. It extends logistic calibration by using a more flexible transformation of predicted probabilities. This allows the method to better correct distorted or asymmetric probability outputs. Model parameters are fitted using maximum likelihood on a separate validation dataset.

Model performance evaluation and benchmarking against shock index
To comprehensively assess the predictive performance of the Prehospital-AI model, we compared it with the shock index (SI), a widely used physiologic marker for evaluating trauma severity. The comparison employed a broad set of performance metrics, including sensitivity, specificity, precision, adjusted precision36, Lift37–40, accuracy, balanced accuracy, area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), adjusted precision-recall gain (APG)41, and normalized AUPRC (nAUPRC)42. These metrics were chosen to capture various aspects of model discrimination, calibration, and performance under class imbalance. Detailed descriptions of the SI calculation, inclusion and exclusion criteria, and statistical methods are provided in Supplementary Methods 4. To further evaluate the clinical utility of the model, we perform decision curve analysis (DCA)43. This method assesses the net benefit of the model across various threshold probabilities, thereby illustrating its potential value in clinical decision-making. The observation of greater net benefit compared to treat-all or treat-none strategies within relevant thresholds supports its clinical applicability.

Real-time AI model deployment
To enable real-time prehospital trauma mortality prediction, we developed and deployed a web-based application designed for seamless integration into emergency medical workflows. The system features a streamlined user interface where prehospital personnel can input 21 critical prehospital variables, including patient demographics, injury mechanism, and vital signs. Once the data is submitted, the AI model instantly processes the information and provides a mortality risk assessment. Beyond generating rapid predictions, the platform is equipped with a communication module that transmits results to designated trauma centers. This feature enhances prehospital-to-hospital coordination, ensuring that trauma teams receive real-time risk assessments before the patient’s arrival.

Statistics & reproducibility
No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment. All statistical analyses were performed using Python (version 3.8.19) and R (version 4.4.0); detailed package versions are provided in Supplementary Methods 3.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This study applies machine learning algorithms to develop a predictive model for emergency department (ED) mortality due to trauma, using only 21 pre-hospital variables. The model was trained on a large-scale database in South Korea, and external validation was conducted using data from four domestic institutions and one institution in Australia.\nAccording to Table 1, the incidence of ED mortality—the target outcome of this analysis—is extremely low: approximately 1% at domestic facilities in South Korea and 0.1% at the Australian institution. Since this analysis deals with highly imbalanced data, SMOTE was used during model training. However, the model’s performance was evaluated using only AUROC, sensitivity, specificity, accuracy, and balanced accuracy, which is insufficient. When handling severely imbalanced data, AUROC tends to overestimate the model's utility, and thus AUPRC should be used for a more rigorous evaluation. Additionally, precision (positive predictive value) should be included as a threshold-dependent metric. Calibration plots should also be presented to evaluate the predictive performance, but none were included in this paper, raising doubts about the conclusion that the model is highly effective.\nRegarding external validation, Table 3 shows high AUROC values at domestic facilities in South Korea but lower AUROC values for the Australian dataset. Although the data collection periods differ, this raises the possibility of overfitting, and the international generalizability of the model remains uncertain.\nFrom a triage perspective, given the reported sensitivity and specificity, and considering the low outcome prevalence, the positive predictive value would be only a few percent, resulting in a high number of false positives. The actual benefit of using this predictive model is unclear. A Decision Curve Analysis would be useful in this context, but such an evaluation was not conducted.\n(Remarks on code availability)\nThe code for model training and testing is adequate. However, since the data has not been shared and the trained model is not provided, it is difficult to verify the reproducibility of the predictions.", "reviewer_2": "The manuscript by Oh et al. presents a real-time AI model designed to predict emergency room (ER) mortality among pre-hospital trauma patients. The model was trained on retrospective data from the Korean Trauma Data Bank (KTDB) and externally validated using retrospective datasets from four regional trauma centers in South Korea and one Level 1 trauma center in Australia. The model demonstrated strong performance in both internal and external validations, outperforming the Shock Index. While the study offers a promising approach for early mortality prediction in trauma care, several major concerns need to be addressed:\n1. Data Representation in Table 1: The data presentation in Table 1 is unclear, particularly for variables with categories labeled as \"uncheckable\" or \"unchecked.\" For instance, the SpO₂ (%) feature includes 3.0% as uncheckable and 51.2% as unchecked, alongside subcategories of 0.2%, 1.1%, 2.7%, 6.1%, and 86.9% for different value ranges. These proportions do not add up to 100%, leading to confusion about how the data is categorized and interpreted.\n2. Model Interpretability: While SHAP values were used to identify the importance of input features in predicting mortality, the manuscript lacks a discussion on model interpretability. The authors should elaborate on how these key features relate to trauma mortality and provide clinical insights or implications for practice.\n3. Comparative Evaluation: The model's performance is only compared against the Shock Index. However, as noted in the Introduction, other existing models (referenced in citations 6, 7, 8, and 13) also address similar tasks. A thorough comparison and discussion of how the proposed model performs relative to these existing approaches is necessary to better contextualize its contribution.\n4. Reproducibility and Code Availability: Although the authors have shared scripts for data preprocessing, model training, and validation via a GitHub repository, important resources are missing. Specifically, detailed scripts and documentation of the data preprocessing steps, access to raw or sample datasets, and the trained model itself (and the web-based AI system for mobile use) are not provided. These omissions hinder the reproducibility of the study’s results.\n(Remarks on code availability)\nAlthough the authors have shared scripts for data preprocessing, model training, and validation via a GitHub repository, important resources are missing. Specifically, detailed scripts and documentation of the data preprocessing steps, access to raw or sample datasets, and the trained model itself (and the web-based AI system for mobile use) are not provided. These omissions hinder the reproducibility of the study’s results."}, "author_response": "We would like to express our sincere gratitude to the reviewers for their thoughtful and constructive feedback on our manuscript. We carefully reviewed each comment and have revised the manuscript accordingly.\nIn response to Reviewer #1’s valuable suggestions, we incorporated additional evaluation metrics suitable for imbalanced data, including AUPRC and precision, and we now provide calibration plots and decision curve analysis to better demonstrate the model’s clinical utility. We have also addressed concerns regarding external validation and generalizability by clarifying differences in data sources and further discussing potential limitations.\nIn response to Reviewer #2’s comments, we have clarified the data representation in Table 1 and revised the relevant descriptions to improve interpretability. We expanded our discussion on model interpretability using SHAP values, providing clinical insights into key predictors. We also added further discussion of other relevant models previously cited in the manuscript to better contextualize the contribution of our proposed approach. To enhance reproducibility, we have updated our GitHub repository with detailed documentation of preprocessing steps, the trained model, the web-based inference system, and a sample dataset.\nWe deeply appreciate the reviewers’ efforts in improving the clarity, rigor, and impact of our work.\nBelow, we provide a point-by-point response to each comment.\nWe have carefully revised the document to make the reviewer’s comments and our responses clearly distinguishable. As requested, our responses are marked in blue text, and changes in the manuscript are highlighted in red for clarity. In addition, page numbers have been added to facilitate easy cross-referencing."}, {"round": 2, "reviewer_comments": {"reviewer_1": "In this revision, the evaluation using AUROC and precision, calibration, and decision curve analysis that were previously pointed out have been added, improving the interpretability of the results in the context of imbalanced data. The external validation using data from Korea appears to be generally robust, and the DCA results also suggest clinical usefulness.\nOn the other hand, for the Australian dataset, although the AUROC appears favorable at 0.895, the AUPRC is quite low at 0.064. The DCA shows a marginally higher net benefit, but only within a very narrow range of thresholds, and the overall net benefit is considerably lower compared to that based on Korean data. This is likely due to the influence of missing values and differences in emergency medical systems between countries. Given that some features used in the prediction model inherently reflect country-specific system characteristics, this limitation is unavoidable and also indicates that the generalizability of the model to other countries is limited.\nTherefore, as mentioned at the beginning of the Discussion, it would be inaccurate to state that the model performed well even for the Australian data. A more appropriate interpretation would be that while the external validation using Korean data yielded robust results, the model’s usefulness for the Australian dataset remains limited due to differences in healthcare systems and data collection processes.", "reviewer_2": "The authors have addressed most of the concerns I raised in the previous round. However, I am unable to access the GitHub repository and the web server, which are intended to provide the code and model for application."}, "author_response": "Thank you for your thoughtful and constructive comments. We fully agree with your assessment regarding the limited generalizability of our model to the Australian dataset. In response, we have carefully revised the abstract, discussion, and conclusion to ensure that the interpretation aligns with the reviewer’s observation.\nSpecifically, we have clarified that while the external validation using Korean datasets demonstrated robust and consistent results, the model’s performance for the Australian dataset was comparatively lower. We now explicitly attribute this to substantial missing prehospital variables and differences in healthcare systems, documentation practices, and prehospital workflows. Accordingly, the statement that the model “performed well” for the Australian dataset has been removed and replaced with a more accurate description emphasizing the limitation of generalizability.\nTo further reflect this revision, the abstract was refined within the 200-word limit and now includes a concise statement emphasizing the need for future multinational studies to evaluate model generalizability across diverse trauma systems. Similarly, the first paragraph of the Discussion has been updated to clarify the likely causes of reduced performance in the Australian cohort, and the Conclusion has been refined to underscore the importance of international validation using standardized datasets.\n\n[...]\n\nResponse> Thank you for your valuable comment. It appears that the link may not have worked due to a trailing period at the end of the URL. For accurate access, please use the following address without punctuation: https://github.com/ohnaeun111/Pre_hospital_mortality\nTo avoid confusion, we have revised it as follows:\nOriginal Text:\n“Code Availability Statement\nThe statistical and analytic code is available at https://github.com/ohnaeun111/Pre_hospital_mortality.”\nRevised Text:\n“Code Availability Statement\nThe statistical and analytic code is available (https://github.com/ohnaeun111/Pre_hospital_mortality).”"}, {"round": 3, "reviewer_comments": {"reviewer_1": "Thank you for your thorough and considerate responses to the review comments. I have no additional revisions to request at this time."}, "author_response": "We sincerely appreciate the opportunity to submit the final revised version of our manuscript entitled “Prehospital Real-Time AI for Trauma Mortality Prediction: A Multi-Institutional and Multi-National Validation Study” (Manuscript ID: NCOMMS-25-24191B). We are grateful to the editor and reviewers for their insightful comments and constructive feedback, which have greatly contributed to improving the clarity and scientific rigor of our work.\nIn this final revision, we have carefully addressed all remaining points raised in the previous round of review. All changes made in response to the comments are clearly indicated in the revised manuscript and a detailed point-by-point response is provided below.\nWe believe that the revised version now meets the journal’s standards and we respectfully submit it for your consideration."}]

Decision letter