Background/Objectives: Cervical cancer (CC) is the fourth most common cancer among women worldwide. This study explored the use of machine learning (ML) and deep learning (DL) in the prediction, diagnosis, and prognosis of CC. Methods: An electronic search was conducted in the PubMed, IEEE, Web of Science, and Scopus databases from January 2015 to April 2025 using the search terms ML, DL, and uterine cervical neoplasms. A total of 153 studies were selected in this review. A comprehensive summary of the available evidence was compiled. Results: We found that 54.9% of the studies addressed the application of ML and DL in CC for diagnostic purposes, followed by prognosis (22.9%) and an incipient focus on CC treatment (22.2%). The five countries where most ML and DL applications have been generated are China, the United States, India, Republic of Korea, and Japan. Of these studies, 48.4% proposed a DL-based approach, and the most frequent input data used to train the models on CC were images. Conclusions: Although there are results indicating a promising application of these artificial intelligence approaches in oncology clinical practice, further evidence of their validity and reproducibility is required for their use in early detection, prognosis, and therapeutic management of CC.

Background: Mexico ranks fifth worldwide in the number of deaths due to COVID-19. Identifying risk markers through easily accessible clinical data could help in the initial triage of COVID-19 patients and anticipate a fatal outcome, especially in the most socioeconomically disadvantaged regions. This study aims to identify markers that increase lethality risk in patients diagnosed with COVID-19, based on machine learning (ML) methods. Markers were differentiated by sex and age-group. Methods: A total of 11,564 cases of COVID-19 in Mexico were extracted from the Epidemiological Surveillance System for Viral Respiratory Disease. Four ML classification methods were trained to predict lethality, and an interpretability approach was used to identify those markers. Results: Models based on Extreme Gradient Boosting (XGBoost) yielded the best performance in a test set. This model achieved a sensitivity of 0.91, a specificity of 0.69, a positive predictive value of 0.344, and a negative predictive value of 0.965. For female patients, the leading markers are diabetes and arthralgia. For males, the main markers are chronic kidney disease (CKD) and chest pain. Dyspnea, hypertension, and polypnea increased the risk of death in both sexes. Conclusions: ML-based models using an interpretability approach successfully identified risk markers for lethality by sex and age. Our results indicate that age is the strongest demographic factor for a fatal outcome, while all other markers were consistent with previous clinical trials conducted in a Mexican population. The markers identified here could be used as an initial triage, especially in geographic areas with limited resources.

Chagas disease (CD), caused by the protozoan parasite Trypanosoma cruzi (T. cruzi), represents a major public health concern in most of the American continent and causes 12,000 deaths every year. CD clinically manifests in two phases (acute and chronic), and the diagnosis can result in complications due to the difference between phases and the long period between them. Still, strategies are lacking for the automatic diagnosis of healthy and T. cruzi-infected individuals with missing and limited data. In this work, we propose a Weighted Variational Auto-Encoder (W–VAE) for imputing and augmenting multimodal data to classify healthy individuals and individuals in the acute or chronic phases of T. cruzi infection from a murine model. W–VAE is a deep generative architecture trained with a new proposed loss function to which we added a weighting factor and a masking mechanism to improve the quality of the data generated. We imputed and augmented data using four modalities: electrocardiography signals, echocardiography images, Doppler spectrum, and ELISA antibody titers. We evaluated the generated data through different multi-classification tasks to identify healthy individuals and individuals in the acute or chronic phase of infection. In each multi-classification task, we assessed several classifiers, missing rates, and feature-selection methods. The best obtained accuracy was 92 ± 4% in training and 95% in the final test using a Gaussian Process Classifier with a missing rate of 50%. The accuracy achieved was 95% for individuals in healthy and acute phase and 100% for individuals in the chronic phase. Our approach can be useful in generating data to study the phases of T. cruzi infection.