Eurasian Journal of Chemical, Medicinal and Petroleum Research

Abstract Background: Controlled ovarian stimulation (COS) is a critical component of assisted reproductive technology (ART), yet ovarian response remains highly heterogeneous despite the use of established biomarkers such as anti-Müllerian hormone (AMH) and antral follicle count (AFC). Artificial intelligence (AI), particularly machine learning (ML), has emerged as a potential decision-support technology for individualized gonadotropin dosing, prediction of oocyte yield, optimization of trigger timing, and selection of stimulation protocols. This systematic review synthesized current evidence concerning the clinical effectiveness, safety, and translational readiness of AI-guided personalized ovarian stimulation.
Methods: Published studies evaluating AI, ML, or algorithm-based personalization of ovarian stimulation in IVF/ICSI considered, with particular emphasis on gonadotropin starting-dose selection, mature oocyte prediction, trigger timing, ovarian response, live birth, clinical pregnancy, cycle cancellation, gonadotropin consumption, and ovarian hyperstimulation syndrome (OHSS). Evidence from randomized controlled trials (RCTs), prospective clinical studies, retrospective validation studies, systematic reviews, and relevant clinical guidelines synthesized narratively. Prediction-model quality and applicability considered using principles derived from PROBAST and emerging PROBAST+AI recommendations.
Results: AI models demonstrated promising predictive performance for ovarian response, total and mature oocyte yield, and trigger-day selection. Prospective clinical evidence remains limited. A multicenter prospective study involving 291 AI-assisted cycles reported 12.20 versus 11.24 MII oocytes and 16.01 versus 14.54 retrieved oocytes compared with historical controls, while total FSH exposure was 3671.95 versus 3846.29 IU; none of these differences reached statistical significance. Conventional individualized dosing trials demonstrated that reducing FSH in predicted hyper-responders reduced any-grade OHSS without reducing cumulative live birth, whereas increasing FSH in predicted poor responders did not improve cumulative live birth.
Conclusions: AI-guided ovarian stimulation is clinically promising but remains insufficiently validated for routine autonomous clinical decision-making. Current evidence supports AI primarily as a clinician-supervised decision-support technology. Large, prospective, externally validated, randomized trials incorporating live birth and safety outcomes are required before widespread implementation.

Abstract Adverse drug reactions (ADRs) and adverse drug events (ADEs) remain major threats to patient safety in hospitals, particularly among older adults, patients with multimorbidity, individuals exposed to polypharmacy, and patients experiencing rapid changes in physiological status. Conventional pharmacovigilance and medication-monitoring approaches depend heavily on retrospective reporting and clinical recognition, which may delay identification of preventable harm. Artificial intelligence (AI), particularly machine learning (ML), natural language processing, and deep learning, offers opportunities to transform pharmacovigilance from a predominantly reactive process into a predictive and continuously updated safety system. This systematic evidence synthesis examined the performance and clinical implications of AI-based models for predicting ADRs and ADEs among hospitalized patients, with particular attention to nursing practice. Evidence published in major biomedical databases synthesized with emphasis on model performance, predictors, validation, explainability, and implications for medication administration and clinical surveillance. Recent systematic reviews indicate that random forest, gradient boosting, support vector machines, regularized regression, and deep-learning architectures frequently used, with pooled predictive performance generally demonstrating moderate discrimination. A 2025 systematic review specifically examining hospitalized patients reported pooled sensitivity and specificity of 78.1% and 70.6%, respectively, for development-only models, 81.5%, and 79.5% for models undergoing external validation. Contemporary evidence also demonstrates increasing use of longitudinal EHR representations, clinical narratives, nursing documentation, and foundation models. However, heterogeneous outcome definitions, missing-data procedures, limited external validation, class imbalance, alert fatigue, algorithmic bias, and insufficient explainability remain substantial barriers to implementation. AI therefore conceptualized as an augmentation technology rather than a replacement for nurses or other clinicians. Effective implementation requires interdisciplinary governance, transparent algorithms, prospective evaluation, workflow integration, education, and continuous monitoring. Properly implemented, AI-supported ADR prediction may strengthen nursing vigilance, prioritize high-risk patients, improve medication safety, and facilitate earlier clinical intervention.

Abstract Skin cancer, particularly melanoma, poses significant health risks globally. Early detection is crucial for effective treatment and improved patient outcomes. Dermoscopy, a non-invasive imaging technique, has enhanced dermatologists' ability to examine skin lesions. Recent advancements in artificial intelligence (AI), especially deep learning, have shown promising results in automating the analysis of dermoscopic images for skin cancer detection. AI models, particularly convolutional neural networks (CNNs), have been trained on large datasets of dermoscopic images, achieving diagnostic accuracies comparable to or surpassing those of experienced dermatologists. These AI systems can assist in identifying malignant lesions, thereby aiding in early diagnosis and reducing the workload on healthcare professionals. However, challenges remain, including the need for diverse and representative datasets, addressing biases in AI models, and ensuring the clinical applicability of these technologies. This paper reviews the current state of AI applications in dermoscopic image analysis for skin cancer detection, discusses the methodologies employed, evaluates the performance of various AI models, and examines the potential impact on clinical practice. The integration of AI into dermatology holds the promise of enhancing diagnostic accuracy, improving patient outcomes, and optimizing healthcare resources.

Abstract Breast cancer remains one of the leading causes of cancer-related mortality among women globally, highlighting the critical need for early detection and accurate diagnosis. Recent advances in artificial intelligence (AI), encompassing both machine learning (ML) and deep learning (DL) approaches, have demonstrated significant potential in enhancing diagnostic accuracy, reducing human error, and supporting clinical decision-making. This systematic review critically analyzes existing studies that employ AI for breast cancer detection, focusing on methodological approaches, dataset characteristics, model performance, and interpretability. ML-based techniques, including support vector machines, random forests, and gradient boosting, show promising results in structured datasets, particularly where dataset sizes are limited, and interpretability is essential. In contrast, DL approaches, primarily convolutional neural networks and their variants, outperform ML in raw image analysis, multi-modal imaging, and complex feature extraction, achieving higher accuracy and sensitivity. Hybrid models integrating ML and DL, often augmented with radiomics features, offer a balanced framework, combining high predictive performance with improved interpretability. Additionally, explainable AI (XAI) techniques are increasingly applied to DL models, mitigating the “black-box” problem and fostering clinical trust. Despite these advancements, challenges remain, including the need for large, high-quality, multi-institutional datasets, computational resource demands, and generalizability across diverse populations. Low-resource and portable AI solutions offer potential for broader accessibility, though with modest reductions in predictive performance. Overall, AI demonstrates transformative potential in early breast cancer detection, particularly when combined with hybrid and explainable frameworks. Future research should prioritize multi-modal integration, rigorous cross-center validation, and deployment strategies that balance accuracy, interpretability, and accessibility, ultimately facilitating clinical adoption and improving patient outcomes.

Abstract Artificial Intelligence (AI) is rapidly transforming various sectors, and medical education is no exception. This review explores the multifaceted applications of AI in medical education, including its potential benefits, associated risks, and necessary ethical considerations. It synthesizes current research to provide insights into how AI tools, such as personalized learning platforms and AI-driven content generation, can enhance educational outcomes. Additionally, the review addresses the challenges of AI implementation, such as maintaining academic integrity and ensuring data accuracy, and proposes strategies for responsible integration. By examining these aspects, this article aims to guide educators and institutions in effectively leveraging AI to prepare future healthcare professionals for the digital age.

Abstract The field of AI ethics has become a global enterprise with a wide range of actors. However, AI ethics is not new. With new uses of AI, AI ethics has flourished beyond academia. For example, in 2021, UNESCO appointed 24 experts from around the world and launched a global online consultation on AI ethics to facilitate dialogue and exchange of views with all member states. One of the major problems that AI ethics and ethicists may face is the ambiguity of what is actually happening in AI, given that understanding what is good about an activity is itself very useful in determining its ethical issues. Despite the lack of transparency of AI in its current form, the important point is what kind of ethical issues can arise due to the use and development of this technology, which is a matter of debate and investigation. Another concern is transparency and explain ability. Many AI systems act like black boxes. The decision-making process is not transparent. This undermines trust among healthcare providers and patients and their companions. It is unacceptable in the medical field that even the designers of AI systems do not know exactly how AI reached a particular result due to the complexity of algorithms such as artificial neural networks. Because we are dealing with human lives here. The challenge of human decision-making is also raised. The use of medical automation processes can reach the point where AI judgment is pitted against human judgment. Relying on AI for diagnostic or treatment decisions raises questions about the role of humans. Although the use of processing power and big data reduces the error rate of AI results, who will be responsible if AI makes a mistake?