Intelligent Management and Artificial Intelligence: Trends, Challenges, and Opportunities, Vol.2

Purpose: This study aims to apply machine learning as an AI-based tool for comprehensive literature review and to propose an Integrated Clinical Management Model (ICMM) that leverages AI-driven decision support, data integration, and change management to optimize decision-making in reproductive medicine. The medical context is exemplified by Preimplantation Genetic Testing-In Vitro Fertilization (PGT-IVF) for rare monogenic diseases, focusing on improving clinical outcomes and patient-centered care through personalized and transparent support systems. Need for the study: As PGT-IVF procedures become more common in reproductive medicine, there is a need to better assess their effectiveness in monogenic diseases. Machine learning can enhance literature analysis and support decision-making, leading to more informed choices for both doctors and patients. Methodology: Literature review based on the Machine Learning Perspective. Findings: Machine learning has enabled an in-depth analysis of available knowledge and the identification of research gaps in the context of implementing new AI-driven solutions in reproductive medicine using PGT-IVF: The scientific evidence is based mainly on case studies with a lack of comprehensive population-level data (1); PGT-IVF in couples at risk of genetic disease is justified, but the procedure does not guarantee a healthy birth (2); Effectiveness analysis boils down to demonstrating a birth to a healthy child (3); The possibilities of implementing the solution differ between countries, Poland is an example (4). Practical Implications: The effective implementation of the proposed AI-powered ICMM requires comprehensive access to clinical, genetic, financial, and psychosocial data across all stages of the PGT-IVF process. This integrated approach supports complex, multi-dimensional decision-making, enabling personalized treatment pathways that balance medical effectiveness, cost-efficiency, and emotional well-being for couples undergoing treatment.