Publications

Abstract

This text offers a critical reflection on the challenges and opportunities in the healthcare sector that have emerged from the “La Sanità del Futuro” project [translation in english: “the Future of Healthcare”]. The overview presents the essays included in the volume, which address topics such as digitalization, equity in access to care, the doctor-patient relationship dynamic, and the integration of healthcare and social assistance. Through an interdisciplinary approach, the volume brings together perspectives from various disciplines and direct testimonies from healthcare professionals, outlining possible scenarios for the future of Italian healthcare.

How to cite:

Abstract

Background eHealth Literacy (eHL) is a set of competencies and skills encompassing the knowledge, comfort and perceived ability to identify, evaluate and apply electronic health information to health problems. Given its role in the appropriate use of health technologies, ensuring equitable access to health information and improving patient outcomes, this study aims to systematically retrieve, qualitatively and quantitative pool and critically appraise available experimental evidence on the effectiveness of eHL interventions across different population groups.

Methods Following the PRISMA guidelines, we conducted a systematic review in PubMed/Medline, Scopus, Web of Science, Embase, Cochrane Library and ClinicalTrials.gov, including original experimental studies quantifying the effectiveness of interventions aimed at increasing eHL, as assessed by the eHealth Literacy Scale (eHEALS) or other validated scales. We performed a random-effects model meta-analysis comparing changes in eHL levels before and after the interventions, and between the intervention and control groups. Heterogeneity was assessed using I2 statistics.

Results Out of the 504 studies retrieved, 15 studies conducted between 2011 and 2023 met the inclusion criteria. Target populations of eHL interventions included adults in 7 studies, older people in 5 and young people in 4. The meta-analysis included 10 studies that used the eHEALS. Participants showed a mean increase in eHEALS scores of 5.81 points (95% CI = 3.36–8.26, N = 1025) following the eHL interventions compared to the pre-intervention period. In the analysis between the intervention and control groups, we found a statistically significant difference in eHL improvement in favour of the intervention group, with mean eHEALS scores 3.62 points (95% CI = 1.63–5.60, N = 1258) higher in the intervention group than in the control groups. Subgroup analyses by intervention type, stratified by Collaborative Learning (CL) or Individualistic Learning (IL) showed significant increases in eHealth Literacy in the pre-post intervention analysis (CL: UMD = 5.19, CI = 0.01–10.38, N = 402; IL: UMD = 6.05; CI = 3.14–8.97, N = 623) and in the intervention vs. control analysis in the IL group (DMD = 4.98; CI = 1.77–8.12, N = 540).

How to cite:

@article{Barbati2025-dk,

  title = {Effectiveness of eHealth literacy interventions: a systematic review and meta-analysis of experimental studies},

  author = { Barbati, Chiara and Maranesi, Elvira and Giammarchi, Cinzia and Lenge, Matteo and Bonciani, Manila and Barbi, Elisa and Vigezzi,

                  Giacomo Pietro and Dragoni, Mauro and Bailoni, Tania and Odone, Anna and Bevilacqua, Roberta},

  journal = {BMC Public Health",

  publisher = {Springer Science and Business Media LLC},

  volume =  {25},

  number =  {1},

  pages =  {288},

  month =  {jan},

  year =  {2025}

}

Abstract

Artificial Intelligence (AI) is widely used within the healthcare domain. One of the branches of digital health concerns the design and development of digital assistant solutions. AI-enabled digital assistants highlight the need to be trustworthy given their intrusiveness within people’s lives. Such solutions aim to provide intelligent tools to ease the management of care pathways or to enhance the capabilities of healthcare organizations in deploying health prevention campaigns by monitoring the lifestyles of healthy people. In this work, we intend to analyze the recent literature concerning integrating AI techniques within digital assistants. We focus on the contributions published during the last 10 years and perform a careful analysis of whether and how trustworthy pillars have been addressed. We also discuss the risks of designing digital assistants without considering trustworthy pillars and present some recommendations to mitigate them.

How to cite:

@article{10.1145/3714999,

author = {Bailoni, Tania and Dragoni, Mauro},

title = {A Review on Trustworthiness of Digital Assistants for Personal Healthcare},

year = {2025},

issue_date = {July 2025},

publisher = {Association for Computing Machinery},

address = {New York, NY, USA},

volume = {57},

number = {7},

issn = {0360-0300},

url = {https://doi.org/10.1145/3714999},

doi = {10.1145/3714999},

abstract = {Artificial Intelligence (AI) is widely used within the healthcare domain. One of the branches of digital health concerns the design and development of digital assistant solutions. AI-enabled digital assistants highlight the need to be trustworthy given their intrusiveness within people’s lives. Such solutions aim to provide intelligent tools to ease the management of care pathways or to enhance the capabilities of healthcare organizations in deploying health prevention campaigns by monitoring the lifestyles of healthy people. In this work, we intend to analyze the recent literature concerning integrating AI techniques within digital assistants. We focus on the contributions published during the last 10 years and perform a careful analysis of whether and how trustworthy pillars have been addressed. We also discuss the risks of designing digital assistants without considering trustworthy pillars and present some recommendations to mitigate them.},

journal = {ACM Comput. Surv.},

month = {feb},

articleno = {164},

numpages = {34},

keywords = {Artificial intelligence, trustworthy, digital health, digital assistants}

}

Abstract

The present paper highlights the urgency for a revitalization of the field of bioethics. The authors have identified the "malaise" present in contemporary bioethics, and they claim that it has become a boring way to approach medicine and life sciences instead of challenging them. Starting from a brief explanation of the origin of bioethics, this paper analyzes the main issues at the core of its malaise, i.e., its depersonalization and extreme specialization which exclude of a holistic view of the patient. Clinical ethics, an applied branch of bioethics, provides a prime example of a bioethical discipline that operates in real-world contexts, and it contrasts with the more theoretical nature of traditional bioethical frameworks. Thus, the inherent multidisciplinary nature of clinical ethics offers an opportunity to a way of connecting the hard and soft sciences, and, ultimately, of transcending this distinction in the medical humanities. The cure of the malaise of bioethics proposed in this article comes from the medical humanities, specifically from the applied humanities perspective, which offers a comprehensive approach to current world issues, including the fast evolution of technologies with applications to the health field.

How to cite:

@article{consolandi2025applied,

  title={Applied humanities as the antidote for the malaise of bioethics},

  author={Consolandi, Monica and Pegoraro, Renzo},

  journal={Medicine, health care, and philosophy},

  volume={28},

  number={1},

  pages={121--124},

  year={2025}

}

Abstract

Technologies are revolutionizing the healthcare sector, enhancing its efficiency, accessibility, and quality. The digitization of medical records simplifies data management, facilitating diagnoses and treatments through smoother communication between healthcare facilities. Wearable devices and health apps enable continuous monitoring of vital signs and autonomous management of therapies, improving patients’ well-being. Artificial Intelligence (AI) and machine learning are transforming medical diagnostics by analyzing clinical data and images with high precision, supporting early diagnoses and personalized treatments. Telemedicine and telemonitoring systems allow for remote consultations and monitoring, essential for patients in remote areas or with limited mobility. Finally, robotics is a valuable ally in surgery and patient care, ensuring precision during procedures and providing support in daily activities for the elderly and disabled. In this article, we offer an overview of the current use of AI in healthcare, along with the ethical implications it entails. We will discuss both the potential and the limitations of these systems, providing insights into their future applications across various fields.

How to cite:

@article{Dragoni_Consolandi_2024, 

title = {The ethics of new technologies in healthcare: where are we now?}, volume={73}, 

url = {https://www.medicinaemorale.it/mem/article/view/1621},

DOI = {10.4081/mem.2024.1621}, 

abstractNote = {Technologies are revolutionizing the healthcare sector, enhancing its efficiency, accessibility, and quality. The digitization of medical records simplifies data management,

facilitating diagnoses and treatments through smoother communication between healthcare facilities. Wearable devices and health apps enable continuous monitoring of vital signs and

autonomous management of therapies, improving patients’ well-being. Artificial Intelligence (AI) and machine learning are transforming medical diagnostics by analyzing clinical data and

images with high precision, supporting early diagnoses and personalized treatments. Telemedicine and telemonitoring systems allow for remote consultations and monitoring, essential for

patients in remote areas or with limited mobility. Finally, robotics is a valuable ally in surgery and patient care, ensuring precision during procedures and providing support in daily activities

for the elderly and disabled. In this article, we offer an overview of the current use of AI in healthcare, along with the ethical implications it entails. We will discuss both the potential and the

limitations of these systems, providing insights into their future applications across various fields.}, 

number = 4}, 

journal = {Medicina e Morale}, 

author = {Dragoni, Mauro and Consolandi, Monica}, 

year = {2024}, 

month = {Dec.}, 

pages = {487–500}

}

Abstract

The rapid evolution of new technologies, particularly those in the health sector, requires ethical reflection that precedes their implementation rather than following it. This reflection must be integrated from the earliest stages of technological development to ensure that innovations align with fundamental ethical values and principles. In this context, a multidisciplinary dialogue is proposed that transcends the traditional separation between hard sciences and soft sciences (such as the humanities and social sciences). This integrated approach, known as “ethics by design”, aims to embed ethical considerations into the very fabric of technological design. The rapid advancement of medical technologies, such as artificial intelligence applied to diagnosis and treatment, raises new ethical issues but also ancient ones, such as privacy, patient autonomy, and equity in access to care. An ongoing and collaborative dialogue between sciences allows these challenges to be addressed holistically, keeping the human beings at the core of care. Such dialogue can contribute to a more comprehensive understanding of the ethical implications of emerging technologies, enabling the anticipation of problems and the development of solutions that respect human dignity and promote the common good. The merging of diverse perspectives facilitates a design ethics that not only meets current needs but is also capable of adapting to and addressing future changes in a responsible and sustainable manner.

How to cite:

@article{consolandi2024consulenza,

  title={Consulenza etica e nuove tecnologie per la salute: un binomio necessario},

  author={Consolandi, Monica and others},

  journal={Medicina e Morale},

  volume={73},

  number={4},

  pages={477--486},

  year={2024}

}

Abstract

Risk communication represents a nuanced discourse within the healthcare sector, characterized by the topics’ sensitivity and the potential for misunderstandings between healthcare providers and patients. This delicacy stems from the complexity of effectively conveying information about risks. Consequently, a primary obstacle lies in fostering healthcare providers’ understanding of implicit communication nuances inherent in pre-operative risk discussions. This study aims to address this gap in the literature by examining the topic through the lens of the philosophy of language, specifically utilizing pragmatic analysis tools to elucidate implicit understandings in doctor-patient interactions. We employ this approach to scrutinize instances of risk evaluation preceding cardiac surgery. Through empirical analysis of gathered data, we illustrate the inadequacies of current state-of-the-art models in accurately identifying misunderstandings within healthcare dialogues. In conclusion, we propose avenues for future research in this domain, underscoring the importance of further exploration into improving risk communication in healthcare settings.

How to cite:

@inproceedings{consolandi2024risk,

  title={Risk Communication in Healthcare: The Management of Misunderstandings},

  author={Consolandi, Monica and Magnolini, Simone and Dragoni, Mauro and others},

  booktitle={Proceedings of HC@ AIxIA 2024: 3rd AIxIA Workshop on Artificial Intelligence For Healthcare},

  year={2024}

}

Abstract

Communication is not only a means but also a place, where relationships establish. This book argues that a trustworthy relationship can be established through carefully managed communication. Thus, knowing and understanding language and its dynamic is essential to orient oneself during communication; this allows the speakers to fully take the opportunity to foster mutual trust. Knowing language does not only mean managing what is said, but especially being aware of what it implies, entails, and what is unsaid. This is especially true in the case of doctor-patient communication, where one of the speakers is also the subject of the speech. The author looks at the moment of interaction between the physician and the patient as the chance for building and consolidating a strong therapeutic alliance. If the chance is not taken or wisely managed, it could cause the opposite, ie, loss of trust, also possibly influencing patient’s concordance to treatment. This unusual and valuable approach to doctor-patient communication has its roots in the philosophy of language, and the philosophy of science.

How to cite:

@book{consolandi2024science,

  title={Science Communication and Health: Fostering Trust in Doctor-Patient Interaction},

  author={Consolandi, Monica},

  year={2024},

  publisher={Ethics International Press}

}

Abstract

This paper explores a brand-new interdisciplinary approach applied to an enduring problem: the communication of severe diagnoses. The moment when physicians explain the diagnosis to patients and their relatives is sensitive, particularly for a disease that is rarely diagnosed early. The first part of the article is dedicated to the context of this delicate doctor-patient interaction. With this framework in mind, the paper delves into the innovative interdisciplinary methodology developed in the pilot study Communi.CARE, conducted in a hospital in Northern Italy, which focuses on the diagnosis of pancreatic ductal adenocarcinoma (PDAC). SARS-CoV-2 impact on the study development is highlighted. The study aims to explore the topic by combining different areas of expertise, including medicine, philosophy, sociology, and psychology. The contribution of philosophy is here presented as essential: it has a leading role in the conception of the study, its development, and the elaboration of results. It is shown throughout the study, from methodology to the analysis of results. Strengths and weaknesses of the methodology are discussed. In conclusion, further philosophical considerations on effective and ethical communication in this delicate context are recommended.

How to cite:

@article{consolandi2024philosophy,

  title={Philosophy leading the way: An interdisciplinary approach to study communication of severe diagnoses},

  author={Consolandi, Monica},

  journal={Plos one},

  volume={19},

  number={7},

  pages={e0305937},

  year={2024},

  publisher={Public Library of Science San Francisco, CA USA}

}

Abstract

This paper focuses on the importance of digital communication between medical teams and patients and their families when mediated by technological tools. Medicine is changing following the fourth industrial (the digital) revolution: from CAT scans, to X-rays, to UV radiation, to electronic records, to treatment tracking apps, to telemedicine, and the use of AI in doctors' decision-making processes. The COVID-19 pandemic highlighted both the fruitful and problematic sides of this medical evolution. Digital tools such as tablets, smartphones, and video calling apps proved to be essential. Accordingly, I analyze three cases that reveal the helpfulness and the limitations of new communication technologies: on physicians and non-hospitalized patients, on families and patients, and on healthcare professionals and patients' families. Since the medical relationship is not only clinical but also relational and human, one must pay attention to the communicative dimensions of it to remain at least partly human-e.

How to cite:

@article{consolandi2024toward,

  title={Toward a digitalized medicine: the Covid-19 pandemic as a disclosure of the importance of digital communication in the clinical world},

  author={Consolandi, Monica},

  journal={Theoretical Medicine and Bioethics},

  volume={45},

  number={3},

  pages={211--219},

  year={2024},

  publisher={Springer}

}

Abstract

This article is a reflection on a new (but ancient) definition of medicine, which considers doctors and patients alike from their complex human experiences. It explores the doctor-patient relationship as well as the practice of medicine itself through an historical lens, by examining some of the scholarship of Galen of Pergamum, the ancient Greek doctor and philosopher of the second century CE. The intention is not to give a new, definite answer, but to use an example from the past to look at the matter from a different, perhaps unusual, perspective.

How to cite:

@article{consolandi2024s,

  title={It’s “Classical Advice!” Why Medical Education Should Go Beyond Science},

  author={Consolandi, Monica and Agnelli, Sara},

  journal={Medical Science Educator},

  volume={34},

  number={3},

  pages={661--670},

  year={2024},

  publisher={Springer}

}

Abstract

Background

Objectives: To investigate communication clarity and understanding at the time of pancreatic adenocarcinoma (PDAC) diagnosis and whether they can influence patient engagement and compliance.

Methods

Consecutive PDAC patients were enrolled at the time of diagnosis after obtaining informed consent in a single-center study. The patients completed a validated scale (PHE-s®), and the understanding rate was assessed using standardized tools. Patient compliance was evaluated, and the correlation between the PHE-s®, understanding, and compliance was calculated.

Results

Thirty patients were enrolled (15 female) with a mean age 64.4, 13 were metastatic. The mean visit time was 31 min, being longer if visiting doctor was an oncologist (p = 0.002). The engagement level was high in 70% of the patients, and all but one were compliant. The analysis of doctor-patient interactions showed a median of 121 conversational turns for doctors, 75 for patients, and 20 for caregivers (p < 0.0001), and the median percentage of speaking time was 77% for doctors, 13% for patients, and 2% for caregivers (p < 0.0001). Female caregivers spent more time speaking than did male caregivers (median 11.6% vs. 1.3%; p = 0.06). There were 290 instances of problematic understanding, most of which occurred during the taking of patients' personal medical history for doctors, while for patients and caregivers, these occurred mainly during the discussion of diagnosis/treatment (p < 0.0001). In a multivariable analysis, only origin from central or southern Italy was associated with high engagement (p = 0.0087).

Conclusion

In this first attempt to measure clarity of communication and engagement in patients with PDAC, typical features of conversation and problematic understanding emerged, which deserves further investigation.

How to cite:

@article{consolandi2024communication,

  title={Communication, understanding and engagement of patients with pancreatic cancer at time of diagnosis},

  author={Consolandi, Monica and Floris, Mara and Pecorelli, Nicol{\`o} and Archibugi, Livia and Macchini, Marina and Rossi, Maria Grazia and Falconi, Massimo and Graffigna, Guendalina and Arcidiacono, Paolo Giorgio and Reni, Michele and others},

  journal={Pancreatology},

  volume={24},

  number={3},

  pages={437--444},

  year={2024},

  publisher={Elsevier}

}

Abstract

Data augmentation comprises a set of techniques that are used to improve the performance of machine learning models. In the Natural Language Processing (NLP) field, the discrete nature of language represents a challenge to data augmentation, as text can lose coherence or syntactic accuracy during the augmentation process. This challenge is particularly pronounced in persuasive dialog systems, where high-quality data is crucial and scarce due to privacy regulations. In this study, we investigate the application of Large Language Models (LLMs) to enhance persuasive arguments from an Automatic Persuasive System (APS). Using a limited COVID-19 dialogue dataset of user-machine persuasive interactions, our goal is to evaluate the efficacy of different augmentation techniques, combined with LLMs, in generating syntactically coherent and accurate dialogues, with a specific emphasis on dialogue quality and persuasiveness. The results show that integrating LLMs with augmentation generates realistic and diverse examples, aiding the overall quality and effectiveness of the persuasive dialogues produced by the system.

How to cite:

@inproceedings{DBLP:conf/aiia/SouzaD24,

  author = {Renan Lirio de Souza and Mauro Dragoni},

  editor = {Davide Bacciu and Ivan Donadello},

  title = {Augmenting Persuasive Argument Datasets using Large Language Models  (Short paper)},

  booktitle = {Proceedings of the AIxIA Doctoral Consortium 2024 co-located with the 23rd International Conference of the Italian Association for Artificial

Intelligence (AIxIA 2024), Bolzano, Italy, November 25-28, 2024},

  series = {{CEUR} Workshop Proceedings},

  volume = {3914},

  publisher = {CEUR-WS.org},

  year = {2024},

  url = {https://ceur-ws.org/Vol-3914/short85.pdf},

  timestamp = {Thu, 06 Mar 2025 17:26:58 +0100},

  biburl = {https://dblp.org/rec/conf/aiia/SouzaD24.bib},

  bibsource = {dblp computer science bibliography, https://dblp.org}

}

Abstract

The ability to reason with and integrate different sensory inputs is the foundation underpinning human intelligence and it is the reason for the growing interest in modelling multi-modal information within Knowledge Graphs. Multi-Modal Knowledge Graphs extend traditional Knowledge Graphs by associating an entity with its possible modal representations, including text, images, audio, and videos, all of which are used to convey the semantics of the entity. Despite the increasing attention that Multi-Modal Knowledge Graphs have received, there is a lack of consensus about the definitions and modelling of modalities, whose definition is often determined by application domains. In this paper, we propose a novel ontology design pattern that captures the separation of concerns between an entity (and the information it conveys), whose semantics can have different manifestations across different media, and its realisation in terms of a physical information entity. By introducing this abstract model, we aim to facilitate the harmonisation and integration of different existing multi-modal ontologies which is crucial for many intelligent applications across different domains spanning from medicine to digital humanities.

How to cite:

@misc{apriceno2024patternalignallintegrating,

      title = {A Pattern to Align Them All: Integrating Different Modalities to Define Multi-Modal Entities}, 

      author = { Gianluca Apriceno and Valentina Tamma and Tania Bailoni and Jacopo de Berardinis and Mauro Dragoni},

      year = {2024},

      eprint = {2410.13803},

      archivePrefix = {arXiv},

      primaryClass = {cs.AI},

      url = {https://arxiv.org/abs/2410.13803}, 

}

Abstract

Functional Status Information refers to a person’s overall mental and physical health. Collecting and analyzing Function Status Information data is crucial for addressing the needs of a growing elderly population, as well as for providing effective care to those with chronic diseases, multiple health issues, or disabilities. Knowledge Graphs provide an effective method for organizing and representing Functional Status Information data in a structured way. Furthermore, they can also allow reasoning over this data to create personalized health support solutions that assist people in maintaining a healthy lifestyle and improving daily living. In this paper, we describe the integration of our Functional Status Knowledge Graph, namely FuS-KG, into a real-world application run within a large-scale living lab involving more than 4,000 people. We provide the road map of this experience including the challenges, the platform’s architecture, the focus on the knowledge layer, the evaluation and the insights observed.

How to cite:

@InProceedings{10.1007/978-3-031-77792-9_25,

author = {Dragoni, Mauro and Apriceno, Gianluca and Bailoni, Tania}

editor = {Alam, Mehwish and Rospocher, Marco and van Erp, Marieke and Hollink, Laura and Gesese, Genet Asefa},

title = {Validating a Functional Status Knowledge Graph in a Large-Scale Living Lab},

booktitle = {Knowledge Engineering and Knowledge Management},

year = {2025},

publisher = {Springer Nature Switzerland},

address ={Cham},

pages = {416--433},

abstract ={ Functional Status Information refers to a person's overall mental and physical health. Collecting and analyzing Function Status Information data is crucial for addressing the needs of a growing elderly population, as well as for providing effective care to those with chronic diseases, multiple health issues, or disabilities. Knowledge Graphs provide an effective method for organizing and representing Functional Status Information data in a structured way. Furthermore, they can also allow reasoning over this data to create personalized health support solutions that assist people in maintaining a healthy lifestyle and improving daily living. In this paper, we describe the integration of our Functional Status Knowledge Graph, namely FuS-KG, into a real-world application run within a large-scale living lab involving more than 4,000 people. We provide the road map of this experience including the challenges, the platform's architecture, the focus on the knowledge layer, the evaluation and the insights observed.},

isbn = {978-3-031-77792-9}

}

Abstract

This paper enhances Logic Tensor Networks through the integration of uninorm based fuzzy operators. Uninorms, a class of operators that bridge the gap between t-norms and t-conorms, offer unparalleled flexibility and adaptability, making them ideal for modeling the complex, often ambiguous relationships inherent in real-world data. By embedding these operators into Logic Tensor Networks, we present a methodology that significantly increases the network’s capability to handle nuanced logical operations, thereby improving its applicability across different domains. Through a series of experiments, we demonstrate the efficacy of uninorm based operators in enhancing the precision of Logic Tensor Networks. Our findings suggest that the inclusion of uninorms not only broadens the scope of problems that Logic Tensor Networks can address but also deepens their reasoning capabilities, paving the way for more sophisticated artificial intelligence systems. This work lays a foundational stone for future research in the intersection of fuzzy logic and neural-symbolic computing, suggesting directions for further exploration and integration of fuzzy systems elements into Logic Tensor Networks

How to cite:

@InProceedings{10.1007/978-3-031-71170-1_8,

author = {de Campos Souza, Paulo Vitor and Apriceno, Gianluca and Dragoni, Mauro},

editor = {Besold, Tarek R. and d'Avila Garcez, Artur and Jimenez-Ruiz, Ernesto and Confalonieri, Roberto and Madhyastha, Pranava and Wagner, Benedikt",

title = {Enhancing Logical Tensor Networks: Integrating Uninorm-Based Fuzzy Operators for Complex Reasoning",

booktitle = {Neural-Symbolic Learning and Reasoning},

year = {2024},

publisher = {Springer Nature Switzerland},

address = {Cham},

pages = {68--79},

abstract = {This paper enhances Logic Tensor Networks through the integration of uninorm based fuzzy operators. Uninorms, a class of operators that bridge the gap between t-norms and t-conorms, offer unparalleled flexibility and adaptability, making them ideal for modeling the complex, often ambiguous relationships inherent in real-world data. By embedding these operators into Logic Tensor Networks, we present a methodology that significantly increases the network's capability to handle nuanced logical operations, thereby improving its applicability across different domains. Through a series of experiments, we demonstrate the efficacy of uninorm based operators in enhancing the precision of Logic Tensor Networks. Our findings suggest that the inclusion of uninorms not only broadens the scope of problems that Logic Tensor Networks can address but also deepens their reasoning capabilities, paving the way for more sophisticated artificial intelligence systems. This work lays a foundational stone for future research in the intersection of fuzzy logic and neural-symbolic computing, suggesting directions for further exploration and integration of fuzzy systems elements into Logic Tensor Networks.},

isbn = {978-3-031-71170-1}

}