Sophisticated Models Research Articles

Recovering the properties of the interstellar medium through integrated spectroscopy: Application to the z∼0 ECO volume-limited star-forming galaxy sample

Deriving physical parameters from integrated galaxy spectra is paramount to interpret the cosmic evolution of the star formation, chemical enrichment, and energetic processes at play. Previous studies have highlighted the power of interstellar medium tracers but also the associated complexities that can be captured only through sophisticated modeling approaches. We developed modeling techniques to characterize the ionized gas properties in the subset of $2052$ star-forming galaxies from the volume-limited, dwarf-dominated, z∼0 ECO catalog (stellar mass range M_*∼10^8-11,M_⊙). Our study sheds light on the internal distribution and average values of parameters such as the metallicity, ionization parameter, and electron density within galaxies. We used the MULTIGRIS statistical framework to evaluate the performance of various models using strong lines as constraints. The reference model involves physical parameters distributed as power laws with free parameter boundaries. Specifically, we used combinations of 1D photoionization models (i.e., considering the propagation of radiation toward a single cloud) to match optical H2 region lines, in order to provide probability density functions of the inferred parameters. The inference predicts nonuniform physical conditions within galaxies. The integrated spectra of most galaxies are dominated by relatively low-excitation gas with a metallicity around $0.3$,Z_⊙. Using the average metallicity in galaxies, we provide a new fit to the mass-metallicity relationship which is in line with direct abundance method determinations from the low-metallicity calibrated range up to high-metallicity stacks. The average metallicity shows a weakly bimodal distribution which may be due to external (e.g., refueling of non-cluster early-type galaxies) or internal processes (higher star-formation efficiency in metal-rich regions). The specific line set used for inference affects the results and we identify potential issues with the use of the S2 line doublet. Complex modeling approaches may capture diverse physical conditions within galaxies but require robust statistical frameworks. Such approaches are limited by the inherent 1D model database as well as caveats regarding the gas geometry. Our results highlight, however, the possibility to extract useful and significant information from integrated spectra.

Tracking Outflow Using Line Locking (TOLL). II. Large Line-locking Web Identified in Quasar J151352+085555

Abstract Quasar outflows often consist of two clouds with velocity separations matching the doublet spacings of common UV resonance transitions, a phenomenon known as line locking, which is commonly observed in quasar spectra. Multiple clouds can be locked together through multi-ion doublets, forming a “line-locking web.” In the second paper of the TOLL project, we present the discovery of one of the largest “line-locking webs” known to date from the Very Large Telescope UVES spectra of QSO J151352+085555. We identify 12 associated narrow absorption line systems through the C iv, N v, Si iv, O vi, and multiple Lyman lines (Lyα to Lyϵ) and find 10 out of the 12 absorbers are line-locked together by comparing velocity separations between different absorption systems. By conducting photoionization modeling with CLOUDY, we measure the total hydrogen column densities, metallicities, and ionization parameters of these absorbers, which suggests that the absorbers likely have subsolar metallicities. A preliminary statistical analysis demonstrates that the shadowed clouds tend to have similar ionization states compared to the shadowing ones. Identification of one of the largest line-locking webs implies that radiative acceleration plays an important role in sorting out cloud velocities in quasar outflows and highlights the need for more sophisticated theoretical models to explain its formation and evolution.

Comprehensive Analysis of a YOLO-based Deep Learning Model for Cotton Plant Leaf Disease Detection

Diagnosis of cotton plant diseases is essential to maintain agricultural sustainability and output. This study proposes a YOLO-based deep learning model for leaf disease detection to maximize cotton plant leaf disease detection accuracy. This method ensures a comprehensive evaluation of cotton plant health by combining various image processing techniques, improving the accuracy of disease identification. This study provides a viable path to improve crop health monitoring and management in cotton farming systems and emphasizes the importance of utilizing cutting-edge image processing techniques in agricultural activities. ROC curve performance and classification metrics were better for YOLOv5 than for VGG16 and ResNet50, as it had the highest F1 score (99.21%), recall, and precision. Consistent performance in classification tests was demonstrated by all models, which showed balanced precision, recall, and F1 scores. ResNet50 marginally outperformed VGG16 in terms of true positive rates, F1 score (98.88% vs. 98.65%), recall, and precision. More sophisticated models, such as YOLOv5 and ResNet50, showed higher efficiency and accuracy than VGG16, which makes them more appropriate for applications demanding low false positive rates and high precision. The proposed YOLO-based method improves the accuracy of disease identification, ensuring a thorough assessment of cotton plant health using image processing techniques. The results show that the proposed approach is quite successful in correctly detecting and classifying a variety of diseases that affect cotton plants.

Fitting cure models using pseudo-observations approaches with the R package pseudoCure

Abstract In survival analysis, it is traditionally assumed that all individuals are susceptible to the event of interest given sufficient time, a premise supported by well-established statistical software. However, scenarios exist where a considerable subset of individuals may never experience the event, highlighting the need for more sophisticated models. To address this, cure rate models have been developed, which acknowledge a “cured” subpopulation inherently nonsusceptible to the event. Existing methods for these analyses can be complex and computationally demanding. In response, the pseudoCure package (Chiou et al. in pseudoCure: a pseudoobservations approach for analyzing survival data with a cure fraction, 2024) implements an innovative solution by utilizing the pseudo-observations approach, as proposed by Su et al. (Stat Methods Med Res 31:2037–2053, 2022), offering a computationally efficient alternative. This package not only provides robust estimations but also enhances the application of generalized estimating equations in specialized cases, providing an invaluable tool in statistical practices. We demonstrate the efficacy of this package in this paper through examples to highlighting its substantial contributions to the field of survival analysis.

The Impact of AI Tools on Education: ChatGPT in Focus

Education nowadays is irreversibly interrelated with technology, and AI has turned into an agent of change in learning environments. Among these AI tools, ChatGPT, developed by OpenAI, has emerged as one of the most sophisticated natural language processing models, which has revolutionized student interactions, research assistance, and academic support. This study will critically review the literature on the role of ChatGPT in education, focusing on its benefits, limitations, and ethical implications. The review highlights how ChatGPT enhances comprehension, critical thinking, and creativity by providing information that is instant, accurate, and contextually relevant. Its adoption rate has been phenomenal since its release in November 2022, with studies indicating that 60% of educators and 40% of students have already adopted ChatGPT into their academic practices. Technology is increasingly used for automated assessments, literature reviews, content generation, and tutoring, contributing to personalized learning experiences and engagement. However, concerns persist regarding AI bias, misinformation, ethical considerations, and student over-reliance on AI-generated content. This review categorizes ChatGPT’s educational applications into four key areas: (1) pedagogical applications, (2) academic research assistance, (3) challenges and ethical concerns, and (4) future directions in AI-enhanced education. It examines the impact of AI-driven tools on curriculum integration, educator support, and student engagement, informed by peer-reviewed articles, policy reports, and case studies. The results of this study confirm that while ChatGPT holds great promise, the integration of AI responsibly does indeed require thoughtful regulation, educator training, and ethical considerations. This research informs ongoing discussions of AI-driven education, policy formulation, and responsible AI adoption in learning environments to ensure that technological advancements align with educational integrity and innovation.

Machine learning emulators of dynamical systems for understanding ecosystem behaviour

Minimal models (MM) aim to capture the simplified behaviour of complex systems to facilitate system-level analyses that would be unfeasible with more sophisticated numerical models. However, the choices involved in minimal model development heavily rely on expert knowledge, a source of bias that can interfere with good modelling practices. In this paper, a new method is proposed in which a machine learning (ML) model is trained with transient data generated by a detailed physically-based numerical model, predicting the rate of change of the target state variables given their current value and additional drivers. The trained model is then used to mimic the analysis made with traditional minimal models. This approach (ML-MM) is deployed in a semiarid hillslope ecosystem characterising its soil and vegetation components. The ML-MM outputs share most of the general features with previous expert-based results but show a better ability of the hillslope to (1) recover its vegetation, (2) resist total disappearance of the soil and (3) reach substantially higher soil depths in steady state. Furthermore, a new intermediate stable equilibrium is found between the already known healthy and degraded ones, revealing a more complex pattern of ecosystem collapse that avoids a critical shift, as supported by numerical model simulations. The transient behaviour is also investigated, from which we conclude that the system can exhibit strong reactivity, that is, an initial deviation away from equilibrium after a perturbation. In conclusion, the present study demonstrates the potential of ML-MM to obtain new scientific insights on complex systems that might be missed by expert-based alternatives. Hence, minimal models may benefit greatly from incorporating detailed numerical models and data-driven simplification in their development process. Ultimately, this methodology could be applicable to many fields of study and even be expanded to observational data, enhancing our understanding of real-world complex system dynamics.

Advanced neural network systems for analysis of blood-based biomarkers in metastatic disease detection

Neural network systems have become the gold provocative standard in blood-based biomarker analysis for metastatic disease diagnosis, providing higher sensitivity and specificity for early cancer detection. The lexical method also showed that recent developments in artificial intelligence and machine learning technologies allow for the complex interpretation of various types of biomarkers, such as circulating tumor cells, cell-free DNA, microRNAs, and proteins. Many biochemical patterns of biomarkers could be easily handled through sophisticated algorithms and depending on these active approaches in diagnosing metastatic outcomes more accurately at earlier stages. Literature analysis involved a targeted selection of scientific peer-reviewed articles dedicated to neural network utilization in blood-based biomarker screening for cancer. Doing a systematic evaluation critically assessed methodologies associated with artificial intelligence, types of biomarkers, methods of detecting biomarkers, and clinical validation strategies. Filtering was done based on several factors including the identification of new architectures for neural networks, new biomarker analysis methods, and clinical applications primarily focusing on the early detection of metastatic diseases. Analysis of reviewed studies showed a high diagnostic accuracy when neural network systems are used for biomarker analysis. Volumes in Cancer Computational Biology Vol reported that the machine learning models have achieved different sensitivity rates of over 90% including the ability to detect early-stage metastatic disease. The use of several categories of biomarkers and application-improved neural networks for their evaluation showed that the combined use of markers yields better diagnostic results than using single biomarkers. Neural network systems have significant potential to transform metastatic disease diagnosis using circulating biomarker detection. Using sophisticated computational models biomarker patterns are analyzed in detail and the diagnosis is made earlier, thus contributing to better treatment outcomes. Some complexities that are potential barriers include enforcing analysis protocols as standards, correcting and calibrating, and embracing routine technological practice. High-end artificial neural networks are the new frontier in blood-based biomarker analysis of metastatic disease diagnosis. Applying AI within the classic biomarker detection approach increases diagnostic reliability, detection timeliness, and the overall outlook for patient treatment. The advancement in neural network architectures as well as biomarker analysis techniques provides for further improvement in cancer diagnostics in the future.

Constraining Solar Wind Transport Model Parameters Using Bayesian Analysis

Abstract We apply nested-sampling Bayesian analysis to a model for the transport of magnetohydrodynamic-scale solar wind fluctuations. The dual objectives are to obtain improved constraints on parameters present in the turbulence transport model (TTM) and to support quantitative comparisons of the quality of distinct versions of the transport model. The TTMs analyzed are essentially the 1D steady-state ones presented in Breech et al. that describe the radial evolution of the energy, correlation length, and normalized cross helicity of the fluctuations, together with the proton temperature, in prescribed background solar wind fields. Modeled effects present in the TTM include nonlinear turbulence interactions, shear driving, and energy injection associated with pickup-ions. Each of these modeled effects involves adjustable parameters that we seek to constrain using Bayesian analysis. We find that, given the TTMs and observational data sets analyzed, the most appropriate TTM to recommend corresponds to 2D fluctuations and has von Kármán–Howarth parameters of α ≈ 0.16 and β ≈ 0.10, along with reasonably standard values for the other adjustable parameters. The analysis also indicates that it is advantageous to include pickup ion effects in the lengthscale evolution equation by assuming Z 2β/α λ is locally conserved. Such Bayesian analysis is readily extended to more sophisticated solar wind models, space weather models, and might lead to improved predictions of, for example, solar flare and coronal mass ejection interactions with the Earth.

A Comprehensive Survey on the Requirements, Applications, and Future Challenges for Access Control Models in IoT: The State of the Art

The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. While the potential of IoT is significant, the security challenges it faces are equally formidable. IoT security can be addressed from different angles, but one of the key issues is the access control model because among the many challenges, access control is a pivotal concern that determines the overall security of IoT systems. This eventually determines which device is given access to the IoT systems and which is denied access. In this work, we conduct a systematic and thorough survey on the state-of-the-art access control models in IoT. This study includes more than 100 related articles, including 77 best-quartile journal papers. We cover conventional as well as advanced access control models, taking the crucial period of various studies in this particular area. In addition, a number of critical questions are answered and key works are summarized. Furthermore, we identify significant gaps in existing models and propose new considerations and prospects for future developments. Since no existing survey explores both conventional and sophisticated access control models with essential challenges, trends and application domains analysis, and requirements analysis, our study significantly contributes to the literature, especially in the IoT security field.

Unveiling the nexus between ESG performance, climate policy uncertainty and corporate innovation: evidence from textual analysis

PurposeThis paper aims to examine the relationship between environmental, social and governance (ESG) performance and text-based corporate innovation based on a sample of India’s ESG-disclosed companies from financial year 2011–2012 to 2021–2022. Further, it endeavors to investigate the moderating role of heightened climate policy uncertainty (CPU) in this relationship.Design/methodology/approachTo verify these hypotheses, the authors first construct a corporate innovation index for India using a sophisticated natural language processing model on each firm-year’s management discussion and analysis reports. Next, the authors use a panel fixed effects model to examine how ESG performance impacts corporate innovation and its moderating and mediating components.FindingsEmpirical evidence suggests higher ESG performance bolsters text-based corporate innovation. After addressing endogeneity issues with the system GMM estimator and two-stage least square IV, incorporating additional control variables and using alternative innovation measurement, the baseline results remain unchanged. Next, the authors find this link is mediated by reducing information asymmetry, financial constraints and managerial myopia. The authors also observe that increased CPU favorably moderates the ESG-innovation nexus. Additionally, the heterogeneity research shows that ESG only positively impacts innovation in specific industries and firms in their growth and mature life cycle phases.Practical implicationsThe results demonstrate that sustainable and ethical business practices can foster corporate innovation. Thus, this study may provide valuable insight for investors, managers and policymakers.Originality/valueTo the best of the authors’ knowledge, this is the first study to examine the relationship between ESG performance and text-based corporate innovation using a machine learning model.

Analysis of tire contact stresses and asphalt pavement rutting under gradient temperature and typical driving conditions

Studies on triaxial contact stresses and asphalt pavement rutting are of great significance for traffic safety and the durability of the asphalt pavement. Our new approach considers more evaluating indicators by investigating compressive creep, vertical, and longitudinal permanent deformation to analyse asphalt pavement rutting under triaxial contact stress during typical driving conditions. For this purpose, firstly sophisticated three-dimensional finite element models encompassing the truck-bus tire and asphalt pavement temperature are developed. Secondly, a comparative analysis was conducted by collating the predicted deformations obtained through finite element analysis with the deformations measured in laboratory experiments using specialized sensors. Finally, the rutting analysis process under triaxial contact stresses during typical driving conditions is established. The results show asphalt pavement exhibits different temperature gradient changes between daytime and nighttime during the summer season. The longitudinal contact stress under braking condition has a significant impact on the longitudinal permanent deformation and compressive creep of asphalt pavement, especially, the longitudinal permanent deformation of the feature points in the middle layer exhibits the most pronounced sensitivity to this contact stress. In addition, the maximal compressive creep is observed at the top of the middle layer, although the characteristic positions for the maximum values of positive and negative creep differ. The proposed new method is beneficial for improving the accuracy and comprehensiveness of asphalt pavement rutting analysis.

Energy Performance Study of a Data Center Combined Cooling System Integrated with Heat Storage and Waste Heat Recovery System

The energy efficiency of data centers has become an urgent problem as it is enjoying rapid development. This study proposes an integrated energy system involving a data center with different renewable energy sources and waste heat recovery, which can consider the partial and unsteady working load of data center. A dynamic and sophisticated system simulation model is established, which can provide both reliable and fast evaluations but also allow flexible extension of additional components. It is found that the free natural resource cooling system can cover about 28% of cooling demand. Compared to the reference condition, the proposed energy system achieves significant energy-saving benefits, with an energy-saving rate of 16.4%. The system COP increases from 3.88 to 4.64, and the PUE decreases from 1.36 to 1.30, resulting in a 23.45% reduction in electricity expenses. By integrating a waste heat recovery system, the heat pump can absorb approximately 3.57 million kWh of heat from the data center, providing approximately 4.587 million kWh of heating energy for users. The rooftop PV system generates approximately 370,000 kWh of electricity annually, covering approximately 8% of the total electricity consumption of the data center. This study can offer a new channel for the energy efficiency enhancement of data centers.

Model Multiplicity for Responsible AI

Machine learning has experienced a remarkable rise, with highly sophisticated over-parameterized models leading the way. Consequently, these cutting-edge models find application across diverse domains. Their increasing deployment has sparked concerns about their real-world impact, studied under the umbrella of responsible AI. A crucial aspect of building responsible AI models is the idea of model multiplicity. If managed well, model multiplicity gives us the freedom to prioritize several metrics, including those associated with responsible AI, and select the best models to minimize harm. However, the existence of multiplicity also marks the unavoidable presence of arbitrariness in model selection that can impact individual-level decisions, necessitating a broader discussion on the role and expectations of AI decision makers in our society.

Integrating microfluidics, hydrogels, and 3D bioprinting for personalized vessel-on-a-chip platforms.

Thrombosis, a major cause of morbidity and mortality worldwide, presents a complex challenge in cardiovascular medicine due to the intricacy of clotting mechanisms in living organisms. Traditional research approaches, including clinical studies and animal models, often yield conflicting results due to the inability to control variables in these complex systems, highlighting the need for more precise investigative tools. This review explores the evolution of in vitro thrombosis models, from conventional polydimethylsiloxane (PDMS)-based microfluidic devices to advanced hydrogel-based systems and cutting-edge 3D bioprinted vascular constructs. We discuss how these emerging technologies, particularly vessel-on-a-chip platforms, are enabling researchers to control previously unmanageable factors, thereby offering unprecedented opportunities to pinpoint specific clotting mechanisms. While PDMS-based devices offer optical transparency and fabrication ease, their inherent limitations, including non-physiological rigidity and surface properties, have driven the development of hydrogel-based systems that better mimic the extracellular matrix of blood vessels. The integration of microfluidics with biomimetic materials and tissue engineering approaches has led to the development of sophisticated models capable of simulating patient-specific vascular geometries, flow dynamics, and cellular interactions under highly controlled conditions. The advent of 3D bioprinting further enables the creation of complex, multi-layered vascular structures with precise spatial control over geometry and cellular composition. Despite significant progress, challenges remain in achieving long-term stability, incorporating immune components, and translating these models to clinical applications. By providing a comprehensive overview of current advancements and future prospects, this review aims to stimulate further innovation in thrombosis research and accelerate the development of more effective, personalized approaches to thrombosis prevention and treatment.

A machine learning interpretation of the correlation between poverty and air pollution in the contiguous United States

The correlation between poverty and air pollution in the contiguous United States is a widely debated issue, often suggesting that impoverished areas suffer higher pollution levels due to socioeconomic disparities. However, existing studies frequently lack the integration of advanced analytical techniques and fail to account for a comprehensive range of variables. This research paper addresses these gaps by employing sophisticated machine learning models to analyze an extensive dataset encompassing various socioeconomic and environmental metrics. Utilizing linear regression, decision trees, and neural networks, we rigorously examined the data. While the variables were able to predict county-wide poverty rates to reasonably low RMSE and MAE values, our results indicate no significant correlation between poverty levels alone with air pollution indices. The latter finding challenges conventional understanding and highlights the complexity of the relationship between socioeconomic status and environmental quality. By offering a data-driven perspective, our study encourages policymakers to reconsider the factors influencing environmental justice and to look beyond economic status alone. Our work underscores the necessity for further investigation into other potential determinants of pollution, contributing to the discourse on environmental equity. This research provides a fresh, nuanced view that questions established beliefs and underscores the multifaceted nature of socioeconomic-environmental interactions.

Machine Learning Quantum Mechanical/Molecular Mechanical Potentials: Evaluating Transferability in Dihydrofolate Reductase-Catalyzed Reactions.

Integrating machine learning potentials (MLPs) with quantum mechanical/molecular mechanical (QM/MM) free energy simulations has emerged as a powerful approach for studying enzymatic catalysis. However, its practical application has been hindered by the time-consuming process of generating the necessary training, validation, and test data for MLP models through QM/MM simulations. Furthermore, the entire process needs to be repeated for each specific enzyme system and reaction. To overcome this bottleneck, it is required that trained MLPs exhibit transferability across different enzyme environments and reacting species, thereby eliminating the need for retraining with each new enzyme variant. In this study, we explore this potential by evaluating the transferability of a pretrained ΔMLP model across different enzyme mutations within the MM environment using the QM/MM-based ML architecture developed by Pan, X. J. Chem. Theory Comput. 2021, 17(9), 5745-5758. The study includes scenarios such as single point substitutions, a homologous enzyme from different species, and even a transition to an aqueous environment, where the last two systems have MM environment that is substantially different from that used in MLP training. The results show that the ΔMLP model effectively captures and predicts the effects of enzyme mutations on electrostatic interactions, producing reliable free energy profiles of enzyme-catalyzed reactions without the need for retraining. The study also identified notable limitations in transferability, particularly when transitioning from enzyme to water-rich MM environments. Overall, this study demonstrates the robustness of the Pan et al.'s QM/MM-based ML architecture for application to diverse enzyme systems, as well as the need for further research and the development of more sophisticated MLP models and training methods.

Heterogeneous entity representation for medicinal synergy prediction.

Forecasting the synergistic effects of drug combinations facilitates drug discovery and development, especially regarding cancer therapeutics. While numerous computational methods have emerged, most of them fall short in fully modeling the relationships among clinical entities including drugs, cell lines, and diseases, which hampers their ability to generalize to drug combinations involving unseen drugs. These relationships are complex and multidimensional, requiring sophisticated modeling to capture nuanced interplay that can significantly influence therapeutic efficacy. We present a novel deep hypergraph learning method named Heterogeneous Entity Representation for MEdicinal Synergy prediction (HERMES) to predict the synergistic effects of anti-cancer drugs. Heterogeneous data sources, including drug chemical structures, gene expression profiles, and disease clinical semantics, are integrated into hypergraph neural networks equipped with a gated residual mechanism to enhance high-order relationship modeling. HERMES demonstrates state-of-the-art performance on two benchmark datasets, significantly outperforming existing methods in predicting the synergistic effects of drug combinations, particularly in cases involving unseen drugs. The source code is available at https://github.com/Christina327/HERMES. Supplementary data are available at Bioinformatics online.

Modelling Endometriosis Using In Vitro and In Vivo Systems.

Endometriosis is a chronic inflammatory condition characterised by the presence of endometrium-like tissue outside the uterus. Despite its high prevalence and recent advances in molecular science, many aspects of endometriosis and its pathophysiology are still poorly understood. Previously, in vitro and in vivo modelling have been instrumental in establishing our current understanding of endometriosis. As the field of molecular science and the advance towards personalised medicine is ever increasing, more sophisticated models are continually being developed. These hold great potential to provide more intricate knowledge of the underlying pathophysiology and facilitate investigations into potential future approaches to diagnosis and treatment. This review provides an overview of different in vitro and in vivo models of endometriosis that are pertinent to establishing our current understanding. Moreover, we discuss new cross-cutting approaches to endometriosis modelling, such as the use of microfluidic cultures and 3D printing, which have the potential to shape the future of endometriosis research.

EXAMINATION TIME TABLE GENERTOR

Actually, the scheduling of examination timetables is one of the most important administrative challenges that educational institutions face. In examination timetables, the challenges of the same problem are encountered, and through the CSP algorithm Examination Timetable Generator addresses this very challenge by way of an automation and optimization means. It stands as a sophisticated mathematical model where the problem and its solution that involves scheduling a set of an examination can mostly be divided into three main component parts: it includes variables like exams scheduled for, the domains include time slots for all that a sequence of exams proposed to be held, and thus, the domain of constraints or application of constraints takes care of accomplishing all possible requirements. Key Words: Examination Timetable Scheduling ,Educational Institutions Administrative ,Challenges Automation Optimization, CSP Algorithm

Computer-Aided Detection (CADe) and Segmentation Methods for Breast Cancer Using Magnetic Resonance Imaging (MRI).

Breast cancer continues to be a major health concern, and early detection is vital for enhancing survival rates. Magnetic resonance imaging (MRI) is a key tool due to its substantial sensitivity for invasive breast cancers. Computer-aided detection (CADe) systems enhance the effectiveness of MRI by identifying potential lesions, aiding radiologists in focusing on areas of interest, extracting quantitative features, and integrating with computer-aided diagnosis (CADx) pipelines. This review aims to provide a comprehensive overview of the current state of CADe systems in breast MRI, focusing on the technical details of pipelines and segmentation models including classical intensity-based methods, supervised and unsupervised machine learning (ML) approaches, and the latest deep learning (DL) architectures. It highlights recent advancements from traditional algorithms to sophisticated DL models such as U-Nets, emphasizing CADe implementation of multi-parametric MRI acquisitions. Despite these advancements, CADe systems face challenges like variable false-positive and negative rates, complexity in interpreting extensive imaging data, variability in system performance, and lack of large-scale studies and multicentric models, limiting the generalizability and suitability for clinical implementation. Technical issues, including image artefacts and the need for reproducible and explainable detection algorithms, remain significant hurdles. Future directions emphasize developing more robust and generalizable algorithms, integrating explainable AI to improve transparency and trust among clinicians, developing multi-purpose AI systems, and incorporating large language models to enhance diagnostic reporting and patient management. Additionally, efforts to standardize and streamline MRI protocols aim to increase accessibility and reduce costs, optimizing the use of CADe systems in clinical practice. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.