Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.

Recently, there has been explosive growth in the development of the digital industry concept. One of the most important elements of this concept is the application of mathematical modeling methods and data mining to create models of production processes and final products, with the aim of making decisions under stochastic uncertainty. In this paper, a method of diversifying the investment portfolio in the formation of a digital twin is proposed, which is aimed at reducing the total risk by distributing existing assets (resources, investments, etc.) between the analog and digital enterprises. Three types of scenario are proposed: pessimistic - when an enterprise - a pessimist - does not show risk exposure under this scenario when forming a digital counterpart; neutral - when an enterprise shows a neutral attitude to risk (indifference); optimistic - enterprise - optimist is exposed to risk, in particular the risk of untapped opportunities. The result is a set of optimal portfolios, each reflecting a particular position of the enterprise on the risks of untapped opportunities and a certain scenario of forming a portfolio structure, taking into account the relationship between the individual components of the risks of untapped opportunities. Highlighted the advantages of technology "digital twins" for business. Digital counterparts use the data obtained from sensors installed on production lines or on the basis of the final product to predict equipment malfunctions, optimize product quality and reduce the negative impact of production processes on the environment.

This bibliometric survey examines 200 highly cited publications spanning from 2018 to 2025, retrieved from the Scopus database using search terms targeting federated learning applications in healthcare, medical imaging, and clinical diagnostics. The analysis reveals a rapidly maturing interdisciplinary field at the convergence of distributed machine learning, privacy-preserving technologies, and biomedical informatics. Key findings indicate that federated learning has emerged as a transformative paradigm for collaborative AI model training across healthcare institutions without compromising patient data privacy. The United States, China, the United Kingdom, Germany, and India emerge as the most productive nations, with extensive international collaboration networks reflecting the global need for privacy-compliant healthcare AI. Thematic clustering identifies five major research domains: (1) federated learning architectures and algorithms for healthcare, (2) privacy-preserving techniques including differential privacy, homomorphic encryption, and blockchain integration, (3) medical imaging applications (radiology, pathology, dermatology, ophthalmology), (4) electronic health records and predictive analytics, and (5) wearable devices and remote patient monitoring. Emerging trends include the integration of federated learning with explainable AI for clinical interpretability, the convergence of federated learning with digital twins and the healthcare metaverse, and the development of personalized federated learning frameworks for handling non-IID medical data. This survey provides a comprehensive mapping of the intellectual landscape, identifies persistent challenges including data heterogeneity, communication efficiency, and security vulnerabilities, and proposes future directions for privacy-preserving healthcare AI.

Digital twin technology, which creates virtual replicas of physical entities, has emerged as a transformative paradigm in healthcare engineering, enabling personalized medicine, surgical simulation, and real-time patient monitoring. This study presents a comprehensive scien-tometric analysis of 200 highly cited articles retrieved from Scopus using a targeted search string focused on digital twins, virtual patients, and patient avatars in healthcare. The analysis characterizes publication trends, citation impact, leading contributors, thematic clusters, and collaborative networks. Results indicate that research output has accelerated dramatically since 2019, with 2021–2024 representing the most productive period. The highest citation concentrations are found in studies applying digital twins to cardiology, oncology, surgical planning, and healthcare system optimization. The United States, China, Germany, and the United Kingdom dominate production, with strong interdisciplinary collaborations among en-gineering, computer science, and clinical departments. Co-occurrence analysis of author keywords reveals six major thematic clusters: cardiovascular digital twins, surgical simulation and virtual patients, personalized and precision medicine, enabling technologies (IoT, AI, blockchain), healthcare system digital twins, and ethical and regulatory considerations. This scientometric mapping provides researchers, clinicians, and policymakers with an evidence-based roadmap of a rapidly evolving field positioned at the intersection of engineering and medicine.