Artificial Intelligence in Medicine

When developing Machine Learning models to support emergency medical triage, it is important to consider how changes over time in the data can negatively affect the models performance. The objective of this study was to assess the effectiveness of novel Deep Continual Learning pipelines in maximizing model performance when input features are subject to change over time, including the emergence of new features and the disappearance of existing ones. The model is designed to identify life-threatening situations, predict its admissible response delay, and determine its institutional jurisdiction. We analyzed a total of 1 414 575 events spanning from 2009 to 2019. Our findings demonstrate important performance improvements, up to 4.9% in life-threatening, 18.5% in response delay and 1.7% in jurisdiction, in absolute F1-score, compared to the current triage protocol, and improvements up to 4.4% in life-threatening and 11% in response delay, in absolute F1-score, respect to non-continual approaches.

Head injuries are a leading global cause of mortality and disability, highlighting the critical need for advanced prognostic tools to inform clinical decision-making and optimize healthcare resource utilization. For the first time, this study introduces a cutting-edge artificial intelligence (AI) framework designed to predict mortality outcomes from head injury narratives. Leveraging deep learning-based natural language processing techniques, the framework identifies and extracts key features from unstructured text describing injury mechanisms and patient conditions to train predictive models. Validation was conducted on a diverse dataset of 1,500 head injury cases using a stratified holdout approach, with 90% allocated for training and 10% for testing. The one-dimensional convolutional neural network model demonstrated strong performance, achieving averagely 85% accuracy, 74% correct mortality prediction, 88% correct survival prediction, and an impressive area under the receiver operating characteristic curve of 0.91. This work highlights the transformative potential of AI in harnessing narrative clinical data to enhance prognostic accuracy, paving the way for more effective, evidence-based management of head injury patients.

We present a suite of foundational, outcome prediction models for critically ill patients, developed using readily available, routine blood tests and advanced machine learning techniques. The input data of the models includes complete blood counts (CBCs), metabolic panels, and additional biomarkers that assess liver and kidney function, coagulation status, and cardiac injury. The output yields the predicted outcome at a given future horizon. For diagnoses, the length of the future horizon is set to zero while it is set to a fixed time interval for prognoses. The training dataset in this study comprises clinical data from 332 ICU patients, augmented with 200 synthetic samples generated via a conditional diffusion model. Generative machine learning-based data imputation and augmentation approaches yielded modest gains in predictive accuracy. However, substantial performance improvements were achieved through additional methods, including dimensionality and order reduction, SHAP-based feature importance analysis, and a novel time-series-to-image encoding strategy that enables the use of image-based classifiers for temporal clinical data. Principal component analysis-based order reduction produced measurable gains in outcome prediction, while the time-series-to-image encoding proved particularly effective in mitigating small-data limitations common in clinical research. Across all evaluation metrics--accuracy, precision, recall, F1 score, and AUROC--the prognostic models achieved performance exceeding 85%, with some models attaining AUROC scores above 90%. We innovated a new model-ensemble approach to optimize the predictive outcome. This ensemble modeling approach improves the overal prediction, pushing all assessment metrics over 90%. This work establishes a robust and interpretable AI-enabled diagnostic and prognostic toolkit for outcome predictions in critically ill patients and demonstrates a scalable workflow for developing high-performing models from sparse healthcare datasets. The proposed framework is readily deployable in ICU environments with routine blood testing capabilities and serves as a foundation for future integration into digital twin systems for critical care.

Predicting hospitalization from nurse triage notes has significant implications in health informatics. To this end, we compared the performance of the deep-learning transformer-based model, bio-clinical-BERT, with a bag-of-words logistic regression model incorporating term frequency-inverse document frequency (BOW-LR-tf-idf). A retrospective analysis was conducted using data from 1,391,988 Emergency Department patients at the Mount Sinai Health System spanning 2017-2022. The models were trained on four hospitals data and externally validated on a fifth. Bio-clinical-BERT achieved higher AUCs (0.82, 0.84, and 0.85) compared to BOW-LR-tf-idf (0.81, 0.83, and 0.84) across training sets of 10,000, 100,000, and [~]1,000,000 patients respectively. Notably, both models proved effective at utilizing triage notes for prediction, despite the modest performance gap. Importantly, our findings suggest that simpler machine learning models like BOW-LR-tf-idf could serve adequately in resource-limited settings. Given the potential implications for patient care and hospital resource management, further exploration of alternative models and techniques is warranted to enhance predictive performance in this critical domain.

ObjectiveWe present a proof-of-concept digital scribe system as an ED clinical conversation summarization pipeline and report its performance. Materials and MethodsWe use four pre-trained large language models to establish the digital scribe system: T5-small, T5-base, PEGASUS-PubMed, and BART-Large-CNN via zero-shot and fine-tuning approaches. Our dataset includes 100 referral conversations among ED clinicians and medical records. We report the ROUGE-1, ROUGE-2, and ROUGE-L to compare model performance. In addition, we annotated transcriptions to assess the quality of generated summaries. ResultsThe fine-tuned BART-Large-CNN model demonstrates greater performance in summarization tasks with the highest ROUGE scores (F1ROUGE-1=0.49, F1ROUGE-2=0.23, F1ROUGE-L=0.35) scores. In contrast, PEGASUS-PubMed lags notably (F1ROUGE-1=0.28, F1ROUGE-2=0.11, F1ROUGE-L=0.22). BART-Large-CNNs performance decreases by more than 50% with the zero-shot approach. Annotations show that BART-Large-CNN performs 71.4% recall in identifying key information and a 67.7% accuracy rate. DiscussionThe BART-Large-CNN model demonstrates a high level of understanding of clinical dialogue structure, indicated by its performance with and without fine-tuning. Despite some instances of high recall, there is variability in the models performance, particularly in achieving consistent correctness, suggesting room for refinement. The models recall ability varies across different information categories. ConclusionThe study provides evidence towards the potential of AI-assisted tools in reducing clinical documentation burden. Future work is suggested on expanding the research scope with larger language models, and comparative analysis to measure documentation efforts and time.

Natural Language Processing (NLP) on electronic health records (EHRs) can be used to monitor the evolution of pathologies over time to facilitate diagnosis and improve decision-making. In this study, we designed an NLP pipeline to classify Magnetic Resonance Imaging (MRI) radiology reports of patients with high-grade gliomas. Specifically, we aimed to distinguish reports indicating changes in tumors between one examination and the follow-up examination (treatment response/tumor progression versus stability). A total of 164 patients with 361 associated reports were retrieved from routine imaging, and reports were labeled by one radiologist. First, we assessed which embedding is more suitable when working with limited data, in French, from a specific domain. To do so, we compared a classic embedding techniques, TF-IDF, to a neural embedding technique, Doc2Vec, after hyperparameter optimization for both. A random forest classifier was used to classify the reports into stable (unchanged tumor) or unstable (changed tumor). Second, we applied the post-hoc LIME explainability tool to understand the decisions taken by the model. Overall, classification results obtained in repeated 5-fold cross-validation with TF-IDF reached around 89% AUC and were significantly better than those achieved with Doc2Vec (Wilcoxon signed-rank test, P = 0.009). The explainability toolkit run on TF-IDF revealed some interesting patterns: first, words indicating change such as progression were rightfully frequent for reports classified as unstable; similarly, words indicating no change such as not were frequent for reports classified as stable. Lastly, the toolkit discovered misleading words such as T2 which are clearly not directly relevant for the task. All the code used for this study is made available.

This paper proposes a proof of concept of using natural language processing techniques to categorize valence of family relationships described in free texts written by french teenagers. The proposed study traces the evolution of techniques for word embedding. After decomposing the different texts in our possession into short texts composed of sentences and manual labeling, we tested different word embedding scenarios to train a multi-label classification model where a text can take several labels : labels describing the family link between the teenager and the person mentioned in the text and labels describing the teenagers relationship with them positive/negative/neutral valence). The natural baseline for word vector representation of our texts is to build a TF-IDF and train classical classifiers (Elasticnet logistic regression, gradient boosting, random forest, support vector classifier) after selecting a model by cross validation in each class of machine learning models. We then studied the strengths of word-vectors embeddings by an advanced language representation technique via the CamemBERT transformer model, and, again, used them with classical classifiers to compare their respective performances. The last scenario consisted in augmenting the CamemBERT with output dense layers (perceptron) representing a classifier adapted to the multi-label classification and fine-tuning the CamemBERT original layers. The optimal fine-tuning depth that achieves a bias-variance trade-off was obtained by a cross-validation procedure. The results of the comparison of the three scenarios on a test dataset show a clear improvement of the classification performances of the scenario with fine-tuning beyond the baseline and of a simple vectorization using CamemBERT without fine-tuning. Despite the moderate size of the dataset and the input texts, fine-tuning to an optimal depth remains the best solution to build a classifier.

Online cancer peer-support communities generate large volumes of patient-authored and caregiver-authored text that may reflect distress, coping, and informational needs. Automated emotional tone classification could support scalable monitoring, but supervised modeling depends on label quality and may benefit from explicit context features. Using the Mental Health Insights: Vulnerable Cancer Survivors & Caregivers dataset, we compared five model families (TF-IDF Logistic Regression, Random Forest, LightGBM, GRU, and fine-tuned ALBERT) on a three-class target (Negative/Neutral/Positive) derived from four original categories. We introduced two extensions: (i) LLM-based annotation to generate parallel "AI labels" and (ii) token-based augmentation that prepends LLM-extracted structured variables (reporter role and cancer type) to the post text. Models were trained with a 60/20/20 stratified train/validation/test split, with hyperparameters selected on validation data only. Test performance was summarized using weighted F1 and macro one-vs-rest AUC with bootstrap confidence intervals, with paired comparisons based on McNemar tests and false discovery rate adjustment. The LLM annotator produced substantial redistribution in the four-class label space, shifting prevalence toward very negative relative to the original labels; the shift persisted but attenuated after collapsing to three classes. Across all model families, token augmen-tation improved held-out performance, with the largest gains for GRU and consistent improvements for ALBERT. Augmentation also reduced polarity-reversing errors (Nega-{leftrightarrow} tive Positive) for ALBERT, while adjacent errors (Negative {leftrightarrow} Neutral) remained the dominant residual failure mode. These results indicate that LLM-based supervision can introduce systematic measurement shifts that require auditing, yet LLM-extracted context incorporated via simple token augmentation provides a pragmatic, model-agnostic mechanism to improve downstream emotional tone classification for supportive oncology decision support. Author summaryWe studied how to better monitor emotional tone in posts from online cancer peer-support communities, where patients and caregivers share experiences that may signal distress, coping, or unmet needs. Automated classification could help organizations and moderators identify when additional support may be needed, but these systems depend on the quality of the labels used for training and may miss clinical context. Using a public dataset of cancer survivor and caregiver posts, we trained and compared several machine-learning and deep-learning models to classify each post as negative, neutral, or positive. We tested two practical improvements. First, we used a large language model to generate an additional set of "AI labels" and examined how these differed from the original categories. Second, we extracted simple context information--whether the writer was a patient or caregiver and what cancer type was mentioned--and added this context to the text before model training. We found that adding context consistently improved performance across model types. However, the AI-generated labels shifted class distributions, indicating that automated labeling can introduce systematic changes that should be audited. Overall, simple context extraction can make emotional tone monitoring more accurate and useful for supportive oncology decision support.

AO_SCPLOWBSTRACTC_SCPLOWCancer is a complex disease characterized by the uncontrolled growth of abnormal cells in the body but can be prevented and even cured when detected early. Advanced medical imaging has introduced Whole Slide Images (WSIs). When combined with deep learning techniques, it can be used to extract meaningful features. These features are useful for various tasks such as classification and segmentation. There have been numerous studies involving the use of WSIs for survival analysis. Hence, it is crucial to determine their effectiveness for specific use cases. In this paper, we compared three publicly available vision encoders-UNI, Phikon and ResNet18 which are trained on millions of histopathological images, to generate feature embedding for survival analysis. WSIs cannot be fed directly to a network due to their size. We have divided them into 256 x 256 pixels patches and used a vision encoder to get feature embeddings. These embeddings were passed into an aggregator function to get representation at the WSI level which was then passed to a Long Short Term Memory (LSTM) based risk prediction head for survival analysis. Using breast cancer data from The Cancer Genome Atlas Program (TCGA) and k-fold cross-validation, we demonstrated that transformer-based models are more effective in survival analysis and achieved better C-index on average than ResNet-based architecture. The code1 for this study will be made available.

The introduction of deep learning in both imaging and genomics has significantly advanced the analysis of biomedical data. For complex diseases such as cancer different data modalities may reveal different disease characteristics, and the integration of imaging with genomic data has the potential to unravel additional information then when using these data sources in isolation. Here, we propose a DL framework that by combining histopathology images with gene expression profiles can predict prognosis of brain tumors. Using two separate cohorts of 783 adult and 305 pediatric brain tumors, the developed multimodal data models achieved better prediction results compared to the single data models, but also leads to the identification of more relevant biological pathways. Importantly, when testing our adult models on a third independent brain tumor dataset, we show our multimodal framework is able to generalize and performs better on new data from different cohorts. Furthermore, leveraging the concept of transfer learning, we demonstrate how our multimodal models pre-trained on pediatric glioma can be used to predict prognosis for two more rare (less available samples) pediatric brain tumors, i.e. ependymoma and medulloblastoma. To summarize, our study illustrates that a multimodal data fusion approach can be successfully implemented and customized to model clinical outcome of adult and pediatric brain tumors.

Background: Embedding models are an integral part of generative AI architectures, transforming text into embedding vectors that represent semantic content in numerical form. Despite their central role, their performance in clinical settings remains underexplored. We evaluate embedding models across two tasks: semantic difference detection in clinical texts, and data retrieval from patient records. Methods: Eight models were applied to synthetic discharge summaries in English, Finnish, and Swedish. Semantic sensitivity was assessed by introducing controlled perturbations (deletion, modification, and paraphrasing) at three levels of severity; cosine similarity, and L1 and Euclidean distances were computed between the vectors of the original and perturbed texts. Partial vectors were compared to explore dimensionality reduction. Two models with the biggest contrast in semantic difference detection were evaluated on retrieval of relevant information from real Finnish vascular surgery records. Results: Embedding vectors captured semantic differences in clinical text: content deletion and modification produced larger increases in vector distance than paraphrasing. On average, models detected the direction of semantic change correctly, but case-level performance varied considerably. Qwen3-Embedding-8B was the only model with zero directional errors, while multilingual-E5-large erred in 13.8% of cases. In data retrieval, Qwen3-Embedding-8B again outperformed multilingual-E5-large, though the margin was narrower: sufficiency scores were 3.25 vs. 3.17 out of 5 for the first query and 2.25 vs. 1.15 out of 5 for the second query. For some models, as few as 0.6-1.2% of dimensions sufficed to replicate full-vector accuracy; principal component analysis and coordinate-level analysis did not account for this finding. Conclusions: Our results show that the choice of embedding model is important: performance differences between models can be large enough to determine whether clinically relevant information reaches the end user, and model weaknesses can be both task-specific and context-dependent.

This paper presents novel datasets providing numerical representations of ICD-10-CM codes by generating description embeddings using a large language model followed by a dimension reduction via autoencoder. The embeddings serve as informative input features for machine learning models by capturing relationships among categories and preserving inherent context information. The model generating the data was validated in two ways. First, the dimension reduction was validated using an autoencoder, and secondly, a supervised model was created to estimate the ICD-10-CM hierarchical categories. Results show that the dimension of the data can be reduced to as few as 10 dimensions while maintaining the ability to reproduce the original embeddings, with the fidelity decreasing as the reduced-dimension representation decreases. Multiple compression levels are provided, allowing users to choose as per their requirements. The readily available datasets of ICD-10-CM codes are anticipated to be highly valuable for researchers in biomedical informatics, enabling more advanced analyses in the field. This approach has the potential to significantly improve the utility of ICD-10-CM codes in the biomedical domain.

Background & AimsHepatocellular carcinoma is the most common type of primary liver cancer and remains a major global health challenge. In resource-limited settings, patients often face barriers such as low screening rates, poor adherence, and limited access to medical information. Despite comprehensive clinical guidelines, issues like inadequate patient education and ineffective communication persist. While large language models show promise in clinical communication and decision support, their performance in hepatocellular carcinoma management has not been systematically evaluated across multiple dimensions. MethodsTen emerging language models, including general-purpose and medical-domain models, were assessed under prompted and unprompted conditions using a standardized question set covering five key stages: general knowledge, screening, diagnosis, treatment, and follow-up. Accuracy was rated by experts, while semantic consistency, local interpretability, information entropy, and readability were measured computationally. ResultsChatGPT-4o and Grok-3 achieved the highest accuracy (2.62 {+/-} 0.06, 93%; 2.60 {+/-} 0.06, 95%) and interpretability (0.43;0.43). Prompting significantly improved accuracy (p < 0.001) and interpretability (p < 0.001) across all models. Semantic consistency declined slightly in most models; information entropy generally increased; readability changes varied. ConclusionsThis study presents the first multidimensional evaluation of large language models in hepatocellular carcinoma-related clinical tasks. General-purpose models outperformed some medical models, revealing limitations in domain-specific fine-tuning. Prompt design strongly influenced model performance. Further research should integrate diverse prompt strategies and clinical scenarios to improve the usability of language models in real-world oncology settings. Lay summaryThis study evaluated how well-advanced language-based artificial intelligence models can answer clinical questions related to hepatocellular carcinoma. The results showed that some models, especially when guided with structured instructions, provided accurate and understandable responses. These findings suggest that such tools may help improve communication and access to information for both doctors and patients managing liver cancer.

MotivationRecent developments in technology have enabled researchers to collect multiple OMICS datasets for the same individuals. The conventional approach for understanding the relationships between the collected datasets and the complex trait of interest would be through the analysis of each OMIC dataset separately from the rest, or to test for associations between the OMICS datasets. In this work we show that by integrating multiple OMICS datasets together, instead of analysing them separately, improves our understanding of their in-between relationships as well as the predictive accuracy for the tested trait. As OMICS datasets are heterogeneous and high-dimensional (p >> n) integrating them can be done through Sparse Canonical Correlation Analysis (sCCA) that penalises the canonical variables for producing sparse latent variables while achieving maximal correlation between the datasets. Over the last years, a number of approaches for implementing sCCA have been proposed, where they differ on their objective functions, iterative algorithm for obtaining the sparse latent variables and make different assumptions about the original datasets. ResultsThrough a comparative study we have explored the performance of the conventional CCA proposed by Parkhomenko et al. [2009], penalised matrix decomposition CCA proposed by Witten and Tibshirani [2009] and its extension proposed by Suo et al. [2017]. The aferomentioned methods were modified to allow for different penalty functions. Although sCCA is an unsupervised learning approach for understanding of the in-between relationships, we have twisted the problem as a supervised learning one and investigated how the computed latent variables can be used for predicting complex traits. The approaches were extended to allow for multiple (more than two) datasets where the trait was included as one of the input datasets. Both ways have shown improvement over conventional predictive models that include one or multiple datasets. Contacttr1915@ic.ac.uk

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSTrauma patients present with heterogeneous injury patterns that are challenging to represent in statistical models. Traditional approaches either use high-dimensional one-hot encoding, resulting in sparse features, or aggregate injuries into summary scores that lose patient-specific detail. This study developed data-driven ICD-10 embeddings for trauma injuries and evaluated their ability to preserve injury information. MethodsUsing the National Trauma Data Bank, we trained autoencoder models on all trauma patients from 2018 to generate dense vector representations of ICD-10 injury codes. We evaluated embeddings of dimensions 2, 4, 8, 16, and 32 against one-hot encoding using three prediction tasks: in-hospital mortality, emergency department disposition, and blood transfusion within 24 hours. For each hospital included, we trained separate logistic regression and LightGBM models using 2018 data from that hospital, then evaluated performance on 2019 data from the same hospital. Performance was measured using area under the receiver operating characteristic curve (AUC) and stratified by hospital size. ResultsIn LightGBM models, 8-dimensional embeddings improved AUC compared to one-hot encoding of 0.08 (95% CI: 0.06, 0.10) in small hospitals, 0.03 (0.02, 0.04) in medium hospitals, and 0.02 (0.01, 0.02) in large hospitals, with comparable performance in major hospitals (0.00 [-0.01, 0.01]). In logistic regression, 32-dimensional embeddings showed AUC improvements of 0.03 (0.01, 0.05), 0.02 (0.01, 0.03), and 0.02 (0.02, 0.03) for small, medium, and large hospitals respectively, with similar performance in major hospitals (0.01 [0.00, 0.01]). ConclusionICD-10 code injury embeddings with [&ge;]8 dimensions preserve clinically relevant information and can outperform one-hot encoding while reducing dimensionality. The embeddings and software are openly available to support further trauma research and applications.

MotivationThe increasing prevalence of omics data sources is pushing the study of regulatory mechanisms underlying complex diseases such as cancer. However, the vast quantities of features produced and the inherent interplay between them lead to a level of complexity that hampers both descriptive and predictive tasks, requiring custom-built algorithms that can extract relevant information from these sources of data. ResultsWe propose a transformation that moves data centered on molecules (e.g. transcripts and proteins) to a new data space focused on putative regulatory modules given by statistically relevant patterns of coexpression. The proposed transformation extracts patterns from the data through biclustering and uses them to create new variables with guarantees of interpretability and discriminative power. The transformation is shown to achieve dimensionality reductions of up to 99% and to increase the predictive performance of various classifiers across multiple omics layers. Our results suggest that a transformation of omics data from gene-centric to pattern-centric data provides benefits to both prediction tasks and human interpretation. The proposed approach is expected to greatly support further bioinformatic analyses for precision medicine applications. AvailabilitySoftware code and the raw results generated are available at github.com/Andrempp/Pattern-Centric-Transformation. Contactandremppatricio@tecnico.ulisboa.pt Supplementary informationSupplementary data are available at Journal Name online.

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBO_SCPLOWACKGROUNDC_SCPLOWC_ST_ABSLarge Language Nodels (LLMs) have raised broad expectations for clinical use, particularly in the processing of complex medical narratives. However, in practice, more targeted Natural Language Processing (NLP) approaches may offer higher precision and feasibility for symptom extraction from real-world clinical texts. NLP provides promising tools for extracting clinical information from unstructured medical narratives. However, few studies have focused on integrating symptom information from free texts in German, particularly for complex patient groups such as emergency department (ED) patients. The ED setting presents specific challenges: high documentation pressure, heterogeneous language styles, and the need for secure, locally deployable models due to strict data protection regulations. Furthermore, German remains a low-resource language in clinical NLP. MO_SCPLOWETHODSC_SCPLOWWe implemented and compared two models for zero-shot learning--GLiNER and Mistral--and a fine-tuned BERT-based SCAI-BIO/BioGottBERT model for named entity recognition (NER) of symptoms, anatomical terms, and negations in German ED anamnesis texts in an on-premises environment in a hospital. Manual annotations of 150 narratives were used for model validation. The postprocessing steps included confidence-based filtering, negation exclusion, symptom standardization, and integration with structured oncology registry data. All computations were performed on local hospital servers in an on-premises implementation to ensure full data protection compliance. RO_SCPLOWESULTSC_SCPLOWThe fine-tuned SCAI-BIO/BioGottBERT model outperformed both zero-shot approaches, achieving an F1 score of 0.84 for symptom extraction and demonstrating superior performance in negation detection. The validated pipeline enabled systematic extraction of affirmed symptoms from ED-free text, transforming them into structured data. This method allows large-scale analysis of symptom profiles across patient populations and serves as a technical foundation for symptom-based clustering and subgroup analysis. CO_SCPLOWONCLUSIONSC_SCPLOWOur study demonstrates that modern NLP methods can reliably extract clinical symptoms from German ED free text, even under strict data protection constraints and with limited training resources. Fine-tuned models offer a precise and practical solution for integrating unstructured narratives into clinical decision-making. This work lays the methodological foundation for a new way of systematically analyzing large patient cohorts on the basis of free-text data. Beyond symptoms, this approach can be extended to extracting diagnoses, procedures, or other clinically relevant entities. Building upon this framework, we apply network-based clustering methods (in a subsequent study) to identify clinically meaningful patient subgroups and explore sex- and age-specific patterns in symptom expression.

This paper presents the results of the Data Science for Digital Health (DS4DH) group in the MEDIQA-Chat Tasks at ACL-ClinicalNLP 2023. Our study combines the power of a classical machine learning method, Support Vector Machine, for classifying medical dialogues, along with the implementation of oneshot prompts using GPT-3.5. We employ dialogues and summaries from the same category as prompts to generate summaries for novel dialogues. Our findings exceed the average benchmark score, offering a robust reference for assessing performance in this field.

Federated Learning (FL) has emerged as a promising solution to address the limitations of centralised machine learning (ML) in oncology, particularly in overcoming privacy concerns and harnessing the power of diverse, multi-center data. This systematic review synthesises current knowledge on the state-of-the-art FL in oncology, focusing on breast, lung, and prostate cancer. Distinct from previous surveys, our comprehensive review critically evaluates the real-world implementation and impact of FL on cancer care, demonstrating its effectiveness in enhancing ML generalisability, performance and data privacy in clinical settings and data. We evaluated state-of-the-art advances in FL, demonstrating its growing adoption amid tightening data privacy regulations. FL outperformed centralised ML in 15 out of the 25 studies reviewed, spanning diverse ML models and clinical applications, and facilitating integration of multi-modal information for precision medicine. Despite the current challenges identified in reproducibility, standardisation and methodology across studies, the demonstrable benefits of FL in harnessing real-world data and addressing clinical needs highlight its significant potential for advancing cancer research. We propose that future research should focus on addressing these limitations and investigating further advanced FL methods, to fully harness data diversity and realise the transformative power of cutting-edge FL in cancer care.

ObjectivesTo present a model that enhances the accuracy of clinicians when presented with a possibly critical Covid-19 patient. MethodsA retrospective study was performed with information of 5,745 SARS-CoV2 infected patients admitted to the Emergency room of 4 public Hospitals in Madrid belonging to Quiron Salud Health Group (QS) from March 2020 to February 2021. Demographics, clinical variables on admission, laboratory markers and therapeutic interventions were extracted from Electronic Clinical Records. Traits related to mortality were found through difference in means testing and through feature selection by learning multiple classification trees with random initialization and selecting the ones that were used the most. We validated the model through cross-validation and tested generalization with an external dataset from 4 hospitals belonging to Sanitas Hospitals Health Group. The usefulness of two different models in real cases was tested by measuring the effect of exposure to the model decision on the accuracy of medical professionals. ResultsOf the 5,745 admitted patients, 1,173 died. Of the 110 variables in the dataset, 34 were found to be related with our definition of criticality (death in <72 hours) or all-cause mortality. The models had an accuracy of 85% and a sensitivity of 50% averaged through 5-fold cross validation. Similar results were found when validating with data from the 4 hospitals from Sanitas. The models were found to have 11% better accuracy than doctors at classifying critical cases and improved accuracy of doctors by 12% for non-critical patients, reducing the cost of mistakes made by 17%.