Principles Of Artificial Intelligence Research Articles

Artificial Intelligence: From Theory to Application and Ethical Considerations

Abstract: Artificial Intelligence (AI) has emerged as a transformative force across various domains, driving technological advancements and reshaping societal landscapes. This article presents a comprehensive exploration of the multifaceted facets of AI, encompassing its foundational concepts, diverse applications, and critical ethical considerations. The article begins by elucidating the fundamental principles of AI, encompassing machine learning, neural networks, and natural language processing, providing readers with a clear understanding of the underlying mechanisms driving AI's capabilities. Subsequently, the article delves into the wide-ranging applications of AI, spanning across industries such as healthcare, finance, education, transportation, and more. In each domain, AI's potential to augment decision-making, optimize processes, and foster innovation is analyzed, offering insights into the transformative potential of this technology in diverse sectors. Furthermore, the article tackles the increasingly crucial ethical implications arising from AI's pervasive integration into society. It critically examines issues of bias, fairness, transparency, and accountability that AI systems can manifest, impacting individuals and communities. The potential consequences of AI on privacy, data protection, and human autonomy are also assessed, prompting discussions on the necessity of ethical frameworks to govern AI development and deployment. The article adopts a balanced approach by acknowledging the immense potential of AI in enhancing human lives while acknowledging the need for responsible development and ethical considerations.

A guide to artificial intelligence for cancer researchers.

Artificial intelligence (AI) has been commoditized. It has evolved from a specialty resource to a readily accessible tool for cancer researchers. AI-based tools can boost research productivity in daily workflows, but can also extract hidden information from existing data, thereby enabling new scientific discoveries. Building a basic literacy in these tools is useful for every cancer researcher. Researchers with a traditional biological science focus can use AI-based tools through off-the-shelf software, whereas those who are more computationally inclined can develop their own AI-based software pipelines. In this article, we provide a practical guide for non-computational cancer researchers to understand how AI-based tools can benefit them. We convey general principles of AI for applications in image analysis, natural language processing and drug discovery. In addition, we give examples of how non-computational researchers can get started on the journey to productively use AI in their own work.

Multi-class waste segregation using computer vision and robotic arm

Waste segregation is an essential aspect of a smoothly functioning waste management system. Usually, various recyclable waste types are disposed of together at the source, and this brings in the necessity to segregate them into their categories. Dry waste needs to be separated into its own categories to ensure that the proper procedures are implemented to treat and process it, which leads to an overall increased recycling rate and reduced landfill impact. Paper, plastics, metals, and glass are just a few examples of the many dry waste materials that can be recycled or recovered to create new goods or energy. Over the past years, much research has been conducted to devise effective and productive ways to achieve proper segregation for the waste that is being produced at an ever-increasing rate. This article introduces a multi-class garbage segregation system employing the YOLOv5 object detection model. Our final prototype demonstrates the capability of classifying dry waste categories and segregating them into their respective bins using a 3D-printed robotic arm. Within our controlled test environment, the system correctly segregated waste classes, mainly paper, plastic, metal, and glass, eight out of 10 times successfully. By integrating the principles of artificial intelligence and robotics, our approach simplifies and optimizes the traditional waste segregation process.

A new framework for ethical artificial intelligence: keeping HRD in the loop

ABSTRACT As a result of the rapid development of technologies, artificial intelligence (AI) is changing human experiences and workplaces in ways that we are not prepared for. Recognized as a double-edged tool, AI has brought both positive and negative outcomes to organisations and employees. With its increasing popularity in the workplace and our daily practice, how to use AI intelligently and responsibly has become a top concern raised by both academics and practitioners. In this article, we discuss the desired and unintended consequences of AI as documented in current literature, examine the ethical concerns, and identify current solutions. We propose ‘keeping HRD in the loop’ as a means to addressing ethical challenges associated with AI and offer a new conceptual framework as a tool for reference. Integrating general AI principles and ethical guidelines developed by the Academy of HRD, we map out an ethical boundary consisting of nine human-centred principles and suggest a sample of six HRD interventions that can facilitate responsible AI practices and productive human-machine interfaces. By bringing HRD perspectives into the domain of AI practice, we hope to bring ethical considerations to the forefront of AI decision-making and stimulate more interest in interdisciplinary research and academic-industry collaboration.

Addressing trade-offs in co-designing principles for ethical AI: perspectives from an industry-academia collaboration

AbstractThe development and deployment of artificial intelligence (AI) has rapidly outpaced regulation. As a result, many organizations opt to develop their own principles for the ethical development of AI, though little research has examined the processes through which they are developed. Prior research indicates that these processes involve perceived trade-offs between competing considerations, and primarily between ethical concerns and organizational benefits or technological development. In this paper, we report on a novel, collaborative initiative in Japan between researchers in the humanities and social sciences, and industry actors to co-design organizational AI ethics principles. We analyzed the minutes from 20 meetings from the formative phase of the development of these principles using an inductive process drawing on thematic analysis, to identify the issues of importance to participants. Through this, we identified four core trade-offs faced by participants. We find that, contrary to prior literature, participants were not just concerned with trade-offs between ethical concerns and organizational benefits or technological development, but also between competing, ethically-oriented considerations. We use the results of this study to highlight a need for further research to understand the longer-term impact on organizations and on society of organization-led approaches to AI ethics.

A semi-automated software model to support AI ethics compliance assessment of an AI system guided by ethical principles of AI

AbstractCompliance with principles and guidelines for ethical AI has a significant impact on companies engaged in the development of artificial intelligence (AI) systems. Specifically, ethics is a broad concept that continuously evolves over time and across cultural and geographical boundaries. International organisations (IOs), individual states, and private groups, all have an interest in defining the concept of ethics of AI. IOs, as well as regional and national bodies, have issued many decisions on AI ethics. Developing a system that complies with the ethical framework poses a complex challenge for companies, and the consequences of not complying with ethical principles can have severe consequences, making compliance with these requirements a key issue for companies. Furthermore, there is a shortage of technical tools to ensure that such AI systems comply with ethical criteria. The scarcity of ethics compliance checking tools for AI, and the current focus on defining ethical guidelines for AI development, has led us to undertake a proposal consisting in a semi-automated software model to verify the ethical compliance of an AI system’s code. To implement this model, we focus on the following important aspects: (1) a literature review to identify existing ethical compliance systems, (2) a review of principles and guidelines for ethical AI to determine the international and European views regarding AI ethics, and (3) the identification of commonly accepted principles and sub-principles of AI. These elements served to inform (4) our proposal for the design of a semi-automated software for verifying the ethical compliance of AI systems both at design-time (ethics-by-design perspective) and afterwards on the resulting software.

Embodied AI for dexterity-capable construction Robots: DEXBOT framework

The construction industry is integrating robots into critical tasks at an accelerated pace, with the aim of enhancing efficiency, safety, and productivity. However, construction tasks requiring dexterity remain a challenge due to the need for precise movements, accurate perception, real-time decision-making, and a comprehensive understanding of the environment. To address these challenges, the introduction of embodied artificial intelligence (AI) represents a significant shift in robotic capabilities to enhance their alignment with the broader spectrum of construction settings. Rooted in cognitive science, embodied AI emphasizes the integration of an agent’s physical form into its computational intelligence processes. It resembles how humans develop motor skills by interacting with physical world. This paper introduces DEXBOT, an exploratory framework for designing construction robots capable of high dexterity using embodied AI principles that mimics human strategies in complex tasks. The framework outlines six key perspectives for solving high-dexterity tasks with embodied AI: scene understanding, localization and motion planning, position-based control, force-based control, sequence planning, and correction decision-making. By presenting preliminary test cases for each perspective, the paper emphasizes the role of embodied AI in advancing dexterity level of construction robots. The DEXBOT framework is expected to encourage interdisciplinary collaboration of designing capable construction robots in the future.

Advancing Health Equity Through Artificial Intelligence: An Educational Framework for Preparing Nurses in Clinical Practice and Research.

The integration of artificial intelligence (AI) into health care offers the potential to enhance patient care, improve diagnostic precision, and broaden access to health-care services. Nurses, positioned at the forefront of patient care, play a pivotal role in utilizing AI to foster a more efficient and equitable health-care system. However, to fulfil this role, nurses will require education that prepares them with the necessary skills and knowledge for the effective and ethical application of AI. This article proposes a framework for nurses which includes AI principles, skills, competencies, and curriculum development focused on the practical use of AI, with an emphasis on care that aims to achieve health equity. By adopting this educational framework, nurses will be prepared to make substantial contributions to reducing health disparities and fostering a health-care system that is more efficient and equitable.

Embracing the Era of Artificial Intelligence: Innovation and Preparation in Nursing Education

Rapid recent advances in information technology have opened the door for artificial intelligence (AI)-related technologies to be applied extensively across many industries. The Ministry of Education has emphasized the importance of cultivating advanced-level professionals in diverse fields, particularly in smart machinery, the Asia-Silicon Valley sector, green energy technology, biotechnology, national defense, new agricultural, and circular economy industries, to enhance innovation and promote industrial competitiveness (Kuo, 2019). While interdisciplinary talent in AI and digital innovation is being actively developed elsewhere, nursing education remains in the exploratory phase of AI and digital technology talent cultivation. Although AI is now a well-known term, the competencies required for its application in nursing remain unclear. Moreover, most nursing professionals are unfamiliar with how to best integrate AI into nursing expertise or practice settings. With the application of AI in the healthcare industry now unstoppable, it is vital to consider how to help nursing students adapt to healthcare's new technology landscape (Huang et al., 2021). AI facilitates the digital simulation of human thought patterns, logic, and behaviors with the goal of assisting human users solve problems, especially those that are time-consuming and require repetitive processing. The development of AI requires interdisciplinary collaboration among domain experts, data scientists, software engineers, robotics experts, and computer programmers. Such collaboration is essential to developing products able to meet the demands of the times and to help students become competent future nursing professionals (Murray, 2018). Nurses spend the most time interacting with patients and are thus best able to understand the perceptions and challenges of patients and their families. Collaborating with professionals from interdisciplinary fields is the best strategy for achieving optimal healthcare outcomes. However, nursing schools have yet to provide a clear response to the impact of AI on nursing education. Nursing educational institutions must enable nursing students to comprehend the concepts and principles of AI and equip them with AI literacy to allow them to unleash their potential, continuously innovate, and stay abreast with the times (Ng et al., 2021). In this issue, experts and scholars currently engaged in AI-related research in the nursing discipline share their research findings in the realms of machine learning, deep learning, emotional recognition, and natural language application. These articles offer insights into the implications of AI, suggest how nursing education may best respond to emerging AI trends, and provide the authors' perspectives on nursing education reform. The editor hopes readers will be inspired to explore new concepts, gain a deeper understanding of the application and significance of AI, and apply AI to address clinical and educational challenges to foster competent nursing professionals for tomorrow.

Knowledge, Attitude and Practice Toward Artificial Intelligence Among Healthcare Workers in Private Polyclinics in Jeddah, Saudi Arabia.

The objective of our study was to assess awareness, attitudes, and practices regarding artificial intelligence (AI) among healthcare workers in private polyclinics in Jeddah, Saudi Arabia. We conducted cross-sectional study among healthcare workers in private clinics in Jeddah. Data was collected using a structured, validated questionnaire in Arabic and English on awareness, attitudes, and behaviors regarding AI. Cronbach's alpha for the questionnaire ranged from 0.6 to 0.8. Descriptive and bivariate analysis was done to assess the scores and their association of various sociodemographic variables with awareness, attitudes, and behaviors regarding AI. Multiple linear regression was performed to predict the scores of awareness, attitudes, and behaviors based on the sociodemographic variables. We recruited 361 participants for this study. Approximately, 62% of the healthcare workers were female. The majority (36%) of healthcare workers were nurses, while 25% were physicians. The median awareness, attitude, and behavioral scores were 5/6 (IQR 3-6), 5/8 (IQR 4-7), and 0/3 (IQR 0), respectively. Approximately three-fourths (74%) of the healthcare workers believed that they understood the basic computational principles of AI. Only half of the participants were willing to use AI when making future medical decisions. We found that male healthcare workers had better knowledge scores regarding AI as compared to female healthcare workers (Beta = 0.555, 95%, p value = 0.010), while for attitude scores, being administrative employee as compared to other employees was found to have negative attitude towards AI (Beta = 0.049, 95%, p value = 0.03). We found that healthcare workers had an overall good awareness and optimistic attitude toward AI. Despite this, the majority is worried about the potential consequences of replacing their jobs with AI in the future. There is a dire need to educate and sensitize healthcare workers regarding the potential impact of AI on healthcare.

창작 주체성 보호를 위한 인공지능 융합 수업의 설계 및 교수 전략

Objectives This study has developed teaching strategies that instructors can use to design and implement artificial intelligence convergence education utilizing generative artificial intelligence for image creation. The derived lesson designs and teaching strategies suggest a limited use of artificial intelligence to ensure that learners using generative AI do not lose their creative autonomy.
 Methods Through a review of prior literature, an initial checklist for class design and a teaching strategy were developed. Subsequently, two rounds of internal validation were conducted with four experts. A creative class utilizing image-generating artificial intelligence was then implemented for 100 first-year middle school students in Seoul. The effectiveness of this approach was evaluated through pre- and post-tests of artificial intelligence literacy and interviews with the learners.
 Results Ultimately, 10 items for the class design checklist and 51 items for the teaching strategy were developed. The class design checklist included steps for ‘checking the usage environment’, ‘evaluating the class content’, and ‘assessing expertise’. The teaching strategy was structured into stages of ‘class preparation’, ‘understanding image-generating artificial intelligence’, ‘creative problem-solving’, and ‘class arrangement’. As a result of implementing these creative classes with image-generating artificial intelligence, there was a significant improvement in the students’ artificial intelligence literacy. Furthermore, both instructors and learners highlighted several benefits of these classes, such as the integration of artificial intelligence with various subjects, the enhancement of divergent thinking, firsthand experience with artificial intelligence principles and applications, opportunities for collaboration and discussion, and a strengthened understanding of copyright and ethical considerations.
 Conclusions The instructional class design checklist and teaching strategy developed in this study serve as cross-curricular guidelines applicable to various subjects that involve image creation, including Korean language, history, and art. Designing artificial intelligence convergence lessons with a focus on protecting learner’s creative autonomy can enable them to grasp the entire creative process and its inherent value. This approach goes beyond mere technical use, empowering learners to enhance their own creative expressions.

Artificial Intelligence: Fundamentals and Breakthrough Applications in Epilepsy

Artificial intelligence, machine learning, and deep learning are increasingly being used in all medical fields including for epilepsy research and clinical care. Already there have been resultant cutting-edge applications in both the clinical and research arenas of epileptology. Because there is a need to disseminate knowledge about these approaches, how to use them, their advantages, and their potential limitations, the goal of the 2023 Merritt-Putnam Symposium and of this synopsis review of that symposium has been to present the background and state of the art and then to draw conclusions on current and future applications of these approaches through the following: (1) Initially provide an explanation of the fundamental principles of artificial intelligence, machine learning, and deep learning. These are presented in the first section of this review by Dr Wesley Kerr. (2) Provide insights into their cutting-edge applications in screening for medications in neural organoids, in general, and for epilepsy in particular. These are presented by Dr Sandra Acosta. (3) Provide insights into how artificial intelligence approaches can predict clinical response to medication treatments. These are presented by Dr Patrick Kwan. (4) Finally, provide insights into the expanding applications to the detection and analysis of EEG signals in intensive care, epilepsy monitoring unit, and intracranial monitoring situations, as presented below by Dr Gregory Worrell. The expectation is that, in the coming decade and beyond, the increasing use of the above approaches will transform epilepsy research and care and supplement, but not replace, the diligent work of epilepsy clinicians and researchers.

디자인씽킹 기반의 초등학교 인공지능 교육프로그램 프로토타입 개발

Purpose: The purpose of this research is to develop a prototype of an elementary school artificial intelligence education program based on design thinking. Method: To this end, core values and design principles of AI(Artificial Intelligence) education programs for elementary schools based on Design Thinking were derived through literature studies, case studies, and demand surveys. Results: As a result, the content feasibility was improved in the CVI measurement of the secondary FGI participant. I would like to suggest that: First, this study is the prototype development study of artificial intelligence education programs, and the researcher's intention was to develop a prototype-type artificial intelligence education program and help it be used in the field quickly. Second, this study was not a study of the development of AI education programs in the form of prototypes that measured effectiveness or improvement in the specific competence of learners or professors through the program. Therefore, the contents and areas of AI education used in this study were reconstructed based on foreign cases and the results of AI curriculum research conducted so far. Conclusion: Therefore, if a formal curriculum for AI education is presented under the leadership of the Ministry of Education, it is necessary to develop the final form of AI education programs based on Design Thinking.

Artificial intelligence in intensive care medicine

The integration of artificial intelligence (AI) into intensive care medicine has made considerable progress in recent studies, particularly in the areas of predictive analytics, early detection of complications, and the development of decision support systems. The main challenges remain availability and quality of data, reduction of bias and the need for explainable results from algorithms and models. Methods to explain these systems are essential to increase trust, understanding, and ethical considerations among healthcare professionals and patients. Proper training of healthcare professionals in AI principles, terminology, ethical considerations, and practical application is crucial for the successful use of AI. Careful assessment of the impact of AI on patient autonomy and data protection is essential for its responsible use in intensive care medicine. A balance between ethical and practical considerations must be maintained to ensure patient-centered care while complying with data protection regulations. Synergistic collaboration between clinicians, AI engineers, and regulators is critical to realizing the full potential of AI in intensive care medicine and maximizing its positive impact on patient care. Future research and development efforts should focus on improving AI models for real-time predictions, increasing the accuracy and utility of AI-based closed-loop systems, and overcoming ethical, technical, and regulatory challenges, especially in generative AI systems.

Analysis of hematological indicators via explainable artificial intelligence in the diagnosis of acute heart failure: a retrospective study.

Acute heart failure (AHF) is a serious medical problem that necessitates hospitalization and often results in death. Patients hospitalized in the emergency department (ED) should therefore receive an immediate diagnosis and treatment. Unfortunately, there is not yet a fast and accurate laboratory test for identifying AHF. The purpose of this research is to apply the principles of explainable artificial intelligence (XAI) to the analysis of hematological indicators for the diagnosis of AHF. In this retrospective analysis, 425 patients with AHF and 430 healthy individuals served as assessments. Patients' demographic and hematological information was analyzed to diagnose AHF. Important risk variables for AHF diagnosis were identified using the Least Absolute Shrinkage and Selection Operator (LASSO) feature selection. To test the efficacy of the suggested prediction model, Extreme Gradient Boosting (XGBoost), a 10-fold cross-validation procedure was implemented. The area under the receiver operating characteristic curve (AUC), F1 score, Brier score, Positive Predictive Value (PPV), and Negative Predictive Value (NPV) were all computed to evaluate the model's efficacy. Permutation-based analysis and SHAP were used to assess the importance and influence of the model's incorporated risk factors. White blood cell (WBC), monocytes, neutrophils, neutrophil-lymphocyte ratio (NLR), red cell distribution width-standard deviation (RDW-SD), RDW-coefficient of variation (RDW-CV), and platelet distribution width (PDW) values were significantly higher than the healthy group (p < 0.05). On the other hand, erythrocyte, hemoglobin, basophil, lymphocyte, mean platelet volume (MPV), platelet, hematocrit, mean erythrocyte hemoglobin (MCH), and procalcitonin (PCT) values were found to be significantly lower in AHF patients compared to healthy controls (p < 0.05). When XGBoost was used in conjunction with LASSO to diagnose AHF, the resulting model had an AUC of 87.9%, an F1 score of 87.4%, a Brier score of 0.036, and an F1 score of 87.4%. PDW, age, RDW-SD, and PLT were identified as the most crucial risk factors in differentiating AHF. The results of this study showed that XAI combined with ML could successfully diagnose AHF. SHAP descriptions show that advanced age, low platelet count, high RDW-SD, and PDW are the primary hematological parameters for the diagnosis of AHF.

Pathways to democratized healthcare: Envisioning human-centered AI-as-a-service for customized diagnosis and rehabilitation

The ongoing digital revolution in the healthcare sector, emphasized by bodies like the US Food and Drug Administration (FDA), is paving the way for a shift towards person-centric healthcare models. These models consider individual needs, turning patients from passive recipients to active participants. A key factor in this shift is Artificial Intelligence (AI), which has the capacity to revolutionize healthcare delivery due to its ability to personalize it. With the rise of software in healthcare and the proliferation of the Internet of Things (IoT), a surge of digital data is being produced. This data, alongside improvements in AI’s explainability, is facilitating the spread of person-centric healthcare models, aiming at improving health management and patient experience.This paper outlines a human-centered methodology for the development of an AI-as-a-service platform with the goal of broadening access to personalized healthcare. This approach places humans at its core, aiming to augment, not replace, human capabilities and integrate in current processes. The primary research question guiding this study is: “How can Human-Centered AI principles be considered when designing an AI-as-a-service platform that democratizes access to personalized healthcare?” This informed both our research direction and investigation.Our approach involves a design fiction methodology, engaging clinicians from different domains to gather their perspectives on how AI can meet their needs by envisioning potential future scenarios and addressing possible ethical and social challenges. Additionally, we incorporate Meta-Design principles, investigating opportunities for users to modify the AI system based on their experiences. This promotes a platform that evolves with the user and considers many different perspectives.

Large language models and generative AI in telehealth: a responsible use lens.

This scoping review aims to assess the current research landscape of the application and use of large language models (LLMs) and generative Artificial Intelligence (AI), through tools such as ChatGPT in telehealth. Additionally, the review seeks to identify key areas for future research, with a particular focus on AI ethics considerations for responsible use and ensuring trustworthy AI. Following the scoping review methodological framework, a search strategy was conducted across 6 databases. To structure our review, we employed AI ethics guidelines and principles, constructing a concept matrix for investigating the responsible use of AI in telehealth. Using the concept matrix in our review enabled the identification of gaps in the literature and informed future research directions. Twenty studies were included in the review. Among the included studies, 5 were empirical, and 15 were reviews and perspectives focusing on different telehealth applications and healthcare contexts. Benefit and reliability concepts were frequently discussed in these studies. Privacy, security, and accountability were peripheral themes, with transparency, explainability, human agency, and contestability lacking conceptual or empirical exploration. The findings emphasized the potential of LLMs, especially ChatGPT, in telehealth. They provide insights into understanding the use of LLMs, enhancing telehealth services, and taking ethical considerations into account. By proposing three future research directions with a focus on responsible use, this review further contributes to the advancement of this emerging phenomenon of healthcare AI.

Artificial Intelligence-assisted colonoscopy and colorectal cancer screening: Where are we going?

Colorectal cancer is a significant global health concern, necessitating effective screening strategies to reduce its incidence and mortality rates. Colonoscopy plays a crucial role in the detection and removal of colorectal neoplastic precursors. However, there are limitations and variations in the performance of endoscopists, leading to missed lesions and suboptimal outcomes. The emergence of artificial intelligence (AI) in endoscopy offers promising opportunities to improve the quality and efficacy of screening colonoscopies. In particular, AI applications, including computer-aided detection (CADe) and computer-aided characterization (CADx), have demonstrated the potential to enhance adenoma detection and optical diagnosis accuracy. Additionally, AI-assisted quality control systems aim to standardize the endoscopic examination process. This narrative review provides an overview of AI principles and discusses the current knowledge on AI-assisted endoscopy in the context of screening colonoscopies. It highlights the significant role of AI in improving lesion detection, characterization, and quality assurance during colonoscopy. However, further well-designed studies are needed to validate the clinical impact and cost-effectiveness of AI-assisted colonoscopy before its widespread implementation.

이미지 자동 생성 인공지능의 원리와 현황

This study identifies the principles of image generation artificial intelligence, examines its performance and limitations, and examines the innovative changes that have occurred in image production methods. Specifically, we reveal the structure of the Generative Adversarial Network (GAN), trace its technical improvement process in detail, and classify the types of subsequent models developed later. The tasks performed by GAN’s successor models are diverse. GAN is steadily evolving, completing amazing tasks that were impossible with existing generative models, such as high-resolution image generation, image substitution such as drawings and photos, and face synthesis. The principles of GAN are, first, data learning through adversarial competition between generator and discriminator, second, calculation of accurate probability distribution through Maximum Likelihood Estimation, and third, calculation of fine features of images through deep convolution structure. The most important characteristic of GAN as a generative model is its ability to create realistic images similar to photographs. Humans have been producing images using hands and tools, but with the invention of photography, humans began producing images using camera devices. Now, generative artificial intelligence is becoming another means of image production as technology continues to improve rapidly.

Trustworthy Artificial Intelligence -based federated architecture for symptomatic disease detection

The recent viral outbreaks have had a significant impact on interpersonal relationships, particularly in enclosed spaces. Detecting and preventing the transmission of diseases such as COVID-19 has become a top priority. These diseases are typically identifiable through the symptoms they cause in humans. However, the collection of personal and health data for use in Artificial Intelligence models can give rise to ethical, security, and privacy issues. Therefore, it is necessary to have architectures that maintain the principles of Trustworthy Artificial Intelligence by design. This work proposes a decentralised architecture based on Federated Learning for symptomatic disease detection using the edge computing paradigm, storing the information in the device that collected it, and the foundations of Trustworthy Artificial Intelligence. The architecture is designed to be robust, secure, transparent, and responsible while maintaining data privacy. The proposed approach can be used with medical information capture systems with different user profiles.