Tag Archives: artificial intelligence

A data-driven approach to PCOS Diagnosis: Systematic review of machine learning applications in reproductive health

DOI: 10.2478/amma-2025-0054

Background and aim: Polycystic Ovary Syndrome (PCOS) is a prevalent endocrine disorder in reproductive-aged women, characterized by hormonal imbalances, anovulation, and metabolic abnormalities. This systematic review aims to evaluate the effectiveness, types, and diagnostic performance of ML algorithms applied in PCOS detection and classification, and to identify the most frequently used input features and methodological challenges in existing studies.
Methods: A systematic search was conducted across scholarly databased, but not limited to PubMed, Scopus, and Google Scholar for studies published between 2014 and 2024 using keywords related to PCOS and machine learning. Inclusion criteria focused on original, peer-reviewed studies applying ML models for PCOS diagnosis. Data were extracted on model type, input features, diagnostic accuracy, and study design. Quality assessment was performed using the PROBAST tool.
Results: Out of 450 identified studies, 34 met the inclusion criteria and passed the quality assessment. Supervised learning models such as Random Forest, SVM, and XGBoost showed high accuracy (up to 99%). Deep learning approaches, particularly Convolutional Neural Networks (CNNs), achieved accuracies between 95% and 99.89% in analyzing ultrasound images. Hybrid models integrating clinical and imaging data further enhanced performance. Common input features included BMI, LH/FSH ratio, AMH, and ultrasound-based ovarian morphology. However, few studies validated models on external datasets, and input feature selection lacked standardization.
Conclusion: Machine learning models such as supervised, deep learning, and hybrid approaches show strong potential in improving PCOS diagnosis by identifying complex patterns across multi-dimensional datasets. Challenges such as limited generalizability and data standardization remain, therefore future studies should focus on developing explainable ML tools, validating models in clinical settings, and leveraging diverse data types for robust, personalized PCOS diagnosis.

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Smart science: How artificial intelligence is revolutionizing pharmaceutical medicine

DOI: 10.2478/amma-2024-0002

Artificial intelligence (AI) is a discipline within the field of computer science that encompasses the development and utilization of machines capable of emulating human behavior, particularly regarding the astute examination and interpretation of data. AI operates through the utilization of specialized algorithms, and it includes techniques such as deep (DL), and machine learning (ML), and natural language processing (NLP). As a result, AI has found its application in the study of pharmaceutical chemistry and healthcare. The AI models employed encompass a spectrum of methodologies, including unsupervised clustering techniques applied to drugs or patients to discern potential drug compounds or appropriate patient cohorts. Additionally, supervised ML methodologies are utilized to enhance the efficacy of therapeutic drug monitoring. Further, AI-aided prediction of the clinical outcomes of clinical trials can improve efficiency by prioritizing therapeutic intervention that are likely to succeed, hence benefiting the patient. AI may also help create personalized treatments by locating potential intervention targets and assessing their efficacy. Hence, this review provides insights into recent advances in the application of AI and different tools used in the field of pharmaceutical medicine.

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