Transformative Potential Research Articles

A Model for Collaborative Immersive Classroom Management Development Using Multi-user Virtual Reality

Classroom management has been ­­identified as a key challenge in becoming a teacher, as newly qualified teachers are overwhelmed by the complexity of professional teaching practice. This paper explores the transformative potential of immersive technologies in fostering a collaborative reflection and problem-solving space for the continued development of newly qualified teachers’ classroom management competencies – allowing newly qualified teachers to become better prepared for facing the unexpected, local, and situated challenges of becoming a teaching professional. Through iterative cycles of design-based research, we have developed a model of immersive collaborative classroom management training using multi-user Virtual Reality. The model consists of four phases: (1) 360-degree video recording, creating an immersive version of the classroom and the complex interactions that occur within it. (2) clip selection, giving teachers the ability to select clips that are relevant for them and their practice. (3) Multi-user Virtual Reality training, allowing teachers to get feedback on their selected clips and construct actionable knowledge together with local teaching supervisors (4) Debriefing, for ensuring the joint understanding and transfer of knowledge to practice. Through analysing teacher’s and supervisors’ meaning making processes of 18 selected video clips across five multi-user Virtual Reality training sessions, we uncover three main phases: Analysis, Abstraction, and Actionability. By being able to collaboratively watch, re-watch, and discuss selected video clips and moving between these three main phases, teachers and supervisors co-create actionable knowledge, creating an improved understanding of current practices, and supporting the ability to transform future practices. These findings contribute both theoretical insights and practical implications for educators and learning designers, emphasising the importance of creating social immersive reflective spaces in which real situations may be revisited in order to examine the intricacies of classroom practices, allowing newly qualified teachers to better engage with their profession.

Digital Technologies in Primary Mathematics Education: Insights from Future Teachers’ Portfolios

The integration of digital technology in education has become an essential competency for primary school teachers, particularly in mathematics education, where innovative tools can enhance learning outcomes and student engagement. This paper investigates the extent and nature of digital technology utilization among future mathematics teachers, focusing on tools like dynamic geometry software (GeoGebra), e-testing platforms (e.g., Kahoot), learning applications (e.g., Learning Apps), and immersive technologies such as augmented and virtual reality. Our research centers on the analysis of final portfolios submitted by graduating students from the Faculty of Education at the University of Ostrava in 2024. These portfolios, which are a culmination of the students’ educational strategies and experiences, offer a unique lens through which to assess the preparedness and innovation potential of future educators. Additionally, we surveyed the views of these students through questionnaires on the use of digital technologies in mathematics teaching. We specifically examined how these candidates plan to integrate digital technologies into their teaching practices and the innovative methods they propose to engage primary school students in mathematics. Through a qualitative study of 24 portfolios and a questionnaire survey of 15 students, we identified prevalent trends and inventive approaches in the use of digital tools. Our analysis highlights not only the variety of digital technologies incorporated into teaching strategies but also the depth of pedagogical integration, reflecting a sophisticated understanding of how to enhance learning environments effectively. The findings underscore the importance of digital literacy in teacher education and suggest that current training programs are effective in equipping future educators with the necessary skills to implement advanced technological tools. This study contributes to the ongoing discussion about digital competence in education, providing evidence of emerging trends and the transformative potential of digital technologies in primary mathematics education.

Employing structural equation modeling to discern teachers’ perspectives and their embrace of the Metaverse within the classroom setting

The rise of smart learning environments and the transformative potential of the Metaverse are increasingly evident across various sectors, particularly in education. While substantial attention has been directed toward student engagement within the Metaverse, the evolution of teaching methodologies enabled by this technology remains under-examined. This study investigates the influence of teachers’ prior experience with technology on their willingness to adopt the Metaverse in diverse educational contexts. Utilizing a sample of 311 educators from the Brazilian Amazon, partial least squares structural equation modeling (PLS-SEM) was employed to validate the theoretical model. The findings emphasize the significant relationship between previous technological experiences, the mediating role of social influence in adoption, and performance expectations. Furthermore, the results underscore the pivotal role of technological engagement in shaping educators’ perceptions and their readiness to adopt emerging innovations, such as the Metaverse.

Large language models for structured reporting in radiology: past, present, and future.

Structured reporting (SR) has long been a goal in radiology to standardize and improve the quality of radiology reports. Despite evidence that SR reduces errors, enhances comprehensiveness, and increases adherence to guidelines, its widespread adoption has been limited. Recently, large language models (LLMs) have emerged as a promising solution to automate and facilitate SR. Therefore, this narrative review aims to provide an overview of LLMs for SR in radiology and beyond. We found that the current literature on LLMs for SR is limited, comprising ten studies on the generative pre-trained transformer (GPT)-3.5 (n = 5) and/or GPT-4 (n = 8), while two studies additionally examined the performance of Perplexity and Bing Chat or IT5. All studies reported promising results and acknowledged the potential of LLMs for SR, with six out of ten studies demonstrating the feasibility of multilingual applications. Building upon these findings, we discuss limitations, regulatory challenges, and further applications of LLMs in radiology report processing, encompassing four main areas: documentation, translation and summarization, clinical evaluation, and data mining. In conclusion, this review underscores the transformative potential of LLMs to improve efficiency and accuracy in SR and radiology report processing. KEY POINTS: Question How can LLMs help make SR in radiology more ubiquitous? Findings Current literature leveraging LLMs for SR is sparse but shows promising results, including the feasibility of multilingual applications. Clinical relevance LLMs have the potential to transform radiology report processing and enable the widespread adoption of SR. However, their future role in clinical practice depends on overcoming current limitations and regulatory challenges, including opaque algorithms and training data.

Smartphones: A Catalyst for Tobacco Control Training in India

The tobacco crisis confronting low- and middle-income countries (LMICs) requires scale-up of effective tobacco control programs, such as Tobacco Free Teachers-Tobacco Free Society (TFT-TFS). We previously demonstrated the efficacy of TFT-TFS in increasing tobacco use cessation among teachers and schools’ adoption of tobacco control policies in Bihar, India. To scale TFT-TFS, we are now pioneering a smartphone-based mobile learning strategy to train principals (or their designees) in Madhya Pradesh to implement and monitor TFT-TFS in schools. Research underscores the transformative potential of mobile learning: a flexible, scalable, and cost-effective alternative to in-person training methods. Our study is a unique opportunity to test a training approach that could have implications for wider and more efficient delivery of other public health programs. The strategic use of gamification and digital storytelling enhances the TFT-TFS mobile training app. Gamification mechanics—including points, leaderboards, and digital badging—foster a dynamic learning environment to enhance learning outcomes. This approach propels continuous participant engagement toward understanding and implementation of TFT-TFS’s six monthly themes and four program components. The TFT-TFS smartphone training program weaves in elements of Indian culture, notably the symbolic use of colors and kites. The Indian kite festival celebrates communal harmony through healthy competition. Similarly, the app employs the idea of school kites, which are divided into six blank segments corresponding to the TFT-TFS monthly themes. Participants navigate each module, engaging with animated videos, digital assessments, and interactive activities. Upon a principal’s successful completion of each monthly theme’s program components, the school’s kite receives a new color, unlocking the subsequent theme. The TFT-TFS smartphone training showcases a unique interactive mobile learning application that integrates gamification and tests how to scale up an effective tobacco control program in schools in India and other LMICs. The TFT-TFS smartphone-based training app may also have relevance to other public health-related training efforts in resource-constrained areas.

Performance Evaluation of mmWave Communication for Real-Time UAV-Base Station Links

Introduction: In the context of modern communication networks, the increasing demand for high data rates, minimal latency, and improved reliability—especially in scenarios involving Unmanned Aerial Vehicles (UAVs)—calls for innovative solutions. Millimeter-wave (mmWave) communication emerges as a fitting choice due to its capacity to offer ample bandwidth and high data rates. Methods: This research paper presents a comprehensive study that compares the performance of mmWave communication links across four different frequencies (6, 28, 60, and 120 GHz) for UAV-to-Base Station (BS) communication. The investigation, conducted through simulations, adheres to atmospheric conditions such as rain and fog, different environments (urban and rural) scenarios. It evaluates the suitability of mmWave frequencies using critical performance metrics: latency, received power, Bit Error Rate (BER) and Signal-to-Interference plus Noise Ratio (SINR). These metrics play a fundamental role in assessing communication link quality. Results: Our findings reveal that leveraging higher mmWave frequencies—specifically 60 and 120 GHz—significantly enhances UAV-to-BS communication performance. Compared to lower frequencies, these higher bands exhibit reduced latency, higher data rates, and improved throughput. Consequently, mmWave frequencies beyond 60 GHz prove well-suited for UAV communication, facilitating efficient data exchange with minimal delay. Conclusion: Furthermore, the potential for reduced interference and enhanced reliability at these elevated frequencies makes them particularly advantageous in applications requiring seamless connectivity. Examples include UAV-assisted emergency response, surveillance, and remote sensing. These insights hold substantial implications for the design and deployment of UAV communication systems, highlighting mmWave technology’s transformative potential in the emerging era of aerial communication.

Perspectives on Photocatalytic Paper‐Based Batteries Fueled by Alcohol

AbstractAmid growing energy demands, innovative solutions are essential to power a variety of devices. Paper‐based microfluidic fuel cells (PμFCs), powered by eco‐friendly alcohols derived from biomass, present a promising option. While lightweight and disposable, they mainly rely on costly noble‐metal catalysts for high efficiency. In this context, the concept of photo paper batteries (pPBs), utilizing affordable sunlight‐activated semiconductors to drive alcohol oxidation on paper, opens the possibility to disposable and affordable energy solutions. Our review highlights recent progress in PμFCs fueled by alcohols like methanol, ethanol, ethylene glycol, and glycerol. We explore semiconductor advancements driving anodic reactions and envision the potential of pPBs. To achieve practical pPBs, refining semiconductors, electrode design, and microfluidic engineering are pivotal. This short review highlights the transformative potential of pPBs and beckons researchers to partake in shaping this new field.

Leveraging AI Tools for Discourse Analysis in Early Childhood Bilingual Education: Enhancing Language Development

The implementation of AI tools in education has become increasingly popular and brought transformative potential, particularly in the field of language education because of its powerful functions on designing immersive and personalized activities and offering real-time feedback. This study explores the role of AI tools in discourse analysis and the impact on enhancing bilingual skills among early childhood students. It investigates two key questions: (1) How do AI tools impact young childrens bilingual skills in early childhood classrooms? (2) How can discourse analysis of AI tools assist educators with bilingual teaching? A qualitative case study, including an in-depth interview with a bilingual educator, reveals insights into the effectiveness of AI tools in promoting bilingual language acquisition and engaging young learners in meaningful and enjoyable learning environment. Furthermore, the discourse analysis of AI tools offers immediate, actionable insights on each students academic performance, helping educators better understand the language learning process and the specific challenges students face. However, considerations and challenges of AI in education, such as privacy risks and misuse, still need to be considered. The findings of this study bridge the gap and highlight the potential for discourse analysis of AI tools in early childhood bilingual education. Further study can focus on exploring other AI tools in bilingual education and conducting long-term research with a large example size.

Transforming Zinc-Ion Batteries with DTPA-Na: A Synergistic SEI and CEI Engineering Approach for Exceptional Cycling Stability and Self-Discharge Inhibition.

Aqueous zinc-ion batteries (ZIBs) hold immense potential for large-scale energy storage, but their practical implementation faces significant challenges related to the zinc anode, including dendrite formation, corrosion, and hydrogen evolution. This study addresses these challenges by introducing diethylenetriamine pentaacetate sodium salt (DTPA-Na) as a novel electrolyte additive. DTPA-Na exhibits a unique dual functionality, enabling the formation of a robust, multi-layered solid electrolyte interphase (SEI) on the zinc anode and a stable cathode electrolyte interphase (CEI) on the MnOOH cathode. The engineered SEI effectively suppresses interfacial side reactions, facilitates uniform zinc deposition, and mitigates dendrite growth, while the CEI inhibits MnOOH dissolution and detrimental cathode side reactions. This synergistic SEI/CEI engineering approach significantly enhances ZIB performance, achieving remarkable cycling stability and self-discharge inhibition, as evidenced by the extended lifespan of Zn||Zn symmetrical cells (4400 hours at 0.5 mA/cm2) and the exceptional capacity retention of Zn||MnOOH full cells after prolonged rest (98.61 % after 720 hours).

Transforming higher education for the digital age

In this study, I explore the transformative potential of artificial intelligence (AI) and emerging technologies in higher education, focusing on case studies and pedagogical innovations that are reshaping the learning experience. Through an in-depth analysis of key initiatives—such as Stanford University's AI-driven personalized learning platform, the AI chatbot implemented at the University of Murcia, Knewton's adaptive learning system, and the intelligent tutoring platform developed by Pai et al.—the study highlights how AI enhances learner engagement, customizes educational experiences, and improves academic outcomes. The research also critically examines the ethical challenges and policy considerations associated with AI integration in educational settings. It emphasizes the need for clear guidelines to ensure responsible and equitable use of AI, particularly in addressing issues of fairness, student welfare, and access. The paper concludes by calling for further research into the long-term implications of AI on educational equity and ethical standards in higher education.

Towards Self-Conscious AI Using Deep ImageNet Models: Application for Blood Cell Classification

The exceptional performance of ImageNet competition winners in image classification has led AI researchers to repurpose these models for a whole range of tasks using transfer learning (TL). TL has been hailed for boosting performance, shortening learning time and reducing computational effort. Despite these benefits, issues such as data sparsity and the misrepresentation of classes can diminish these gains, occasionally leading to misleading TL accuracy scores. This research explores the innovative concept of endowing ImageNet models with a self-awareness that enables them to recognize their own accumulated knowledge and experience. Such self-awareness is expected to improve their adaptability in various domains. We conduct a case study using two different datasets, PBC and BCCD, which focus on blood cell classification. The PBC dataset provides high-resolution images with abundant data, while the BCCD dataset is hindered by limited data and inferior image quality. To compensate for these discrepancies, we use data augmentation for BCCD and undersampling for both datasets to achieve balance. Subsequent pre-processing generates datasets of different size and quality, all geared towards blood cell classification. We extend conventional evaluation tools with novel metrics—“accuracy difference” and “loss difference”—to detect overfitting or underfitting and evaluate their utility as potential indicators for learning behavior and promoting the self-confidence of ImageNet models. Our results show that these metrics effectively track learning progress and improve the reliability and overall performance of ImageNet models in new applications. This study highlights the transformative potential of turning ImageNet models into self-aware entities that significantly improve their robustness and efficiency in various AI tasks. This groundbreaking approach opens new perspectives for increasing the effectiveness of transfer learning in real-world AI implementations.

Usage and perception of electronic medical records (EMR) among medical students in southwestern Nigeria

BackgroundThe transformational potential of electronic medical records (EMRs) in global healthcare is evident, but their adoption in low- and middle-income countries, including Nigeria, remains low.ObjectiveThis study aimed to understand the perceptions of southwestern Nigerian medical students toward EMRs given the limited literature on this topic.MethodsA descriptive, cross-sectional online survey was conducted among 412 medical students from four institutions in southwestern Nigeria. Data on the students' demographics, exposure to, and perspectives on EMR was collected. Data was analyzed using R Version 3.6.1.ResultsMajority (79%) of respondents were in their clinical years. We found that 68.4% of the respondents had good perception (scores ≥ 16) about EMRs while 31.6% were deemed as having fair level of perception (scores < 16). Interestingly, 54.9% had never used an electronic medical record (EMR). Although 46.4% felt very comfortable with EMRs' postgraduation use, 16% felt very uncomfortable. Notably, 94.5% believed that health technology solutions could enhance Nigerian health outcomes. Furthermore, 81.8% felt that their medical education would benefit from greater EMR exposure, while 80.9% believed that EMRs are essential for adequate patient care. Males exhibited significantly more EMR experience and more positive attitudes toward EMRs. However, there was no significant association between the level of study, location of medical school, and respondents' experience or perceptions of EMRs.ConclusionMore than half of students surveyed reported that they have never used EMRs. Despite this, many students felt comfortable with the idea of using EMRs in their future practice. Observed gender differences in EMR use and attitudes highlight the need for targeted strategies to prepare all students, regardless of gender, for the digital transition in healthcare.

Revolutionizing Computer Science Education: Integrating Blockchain for Enhanced Learning and Future Readiness

Abstract: This research investigates the transformative potential of incorporating blockchain technology into computer science education. In light of the rapid evolution of the digital landscape, traditional educational frameworks often fail to meet the demands of the industry, resulting in a significant skills gap among graduates. This paper analyzes how the integration of blockchain can revolutionize computer science curricula by enhancing learning experiences and equipping students to navigate future technological challenges. The study highlights the advantages of blockchain in educational contexts, including increased security and transparency of academic records, streamlined credentialing processes, and the establishment of decentralized learning platforms that promote collaboration and innovation. It presents case studies of institutions that have successfully implemented blockchain, along with strategies for educators to effectively integrate this technology into their pedagogical approaches. Additionally, the research addresses the challenges and limitations associated with blockchain integration, such as the requisite learning curve and infrastructure demands. The findings indicate that the incorporation of blockchain in computer science education can significantly boost student engagement, provide verifiable skill sets, and align academic outcomes more closely with industry requirements. This study contributes to the ongoing discourse on educational innovation and offers a strategic framework for institutions aiming to utilize blockchain technology in preparing students for the future job market.

The FLASHFIT Interactive Device in Healthcare: Revolutionizing Patient Engagement and Monitoring

The study investigates the effectiveness of the FLASHFIT interactive device in enhancing patient engagement and monitoring within healthcare settings. As digital health solutions become increasingly vital, FLASHFIT aims to bridge the gap between patients and healthcare providers by promoting active participation in health management. This research employs a mixed-methods approach, combining quantitative data from patient usage metrics with qualitative feedback from users and healthcare professionals. Preliminary findings indicate that FLASHFIT significantly improves patient adherence to treatment plans and fosters a sense of autonomy in health management. Moreover, real-time monitoring capabilities facilitate timely interventions, enhancing overall patient outcomes. The study concludes that integrating interactive devices like FLASHFIT into healthcare practices empowers patients and streamlines communication between patients and providers, suggesting a transformative potential for future healthcare delivery models. Keywords: FLASHFIT interactive device, patient engagement and monitoring

Frontiers of Artificial Intelligence in Agricultural Sector: Trends and Transformations

Artificial intelligence (AI) in agriculture is transforming the sector by improving resources efficiency, sustainability and productivity. Our study examined a number of AI-related applications such as pest control, crop monitoring, precision farming and soil health evaluation. AI powered devices enables automated fertilization, harvesting and irrigation, and therefore, cutting down the labor expenses and resource waste. Predictive analytics in AI helps with crop yield and weather forecasts which ultimately improves the planning and risk management. The paper also discusses the challenges and limitations of AI adoption in agriculture, such as the need for reliable data, technical expertise and infrastructure investment. Ultimately, the findings highlights the AI can have positive transformative potential in creating resilient agricultural practices that can meet the demands of a growing global population while minimizing environmental impact. However, one of the biggest uses of AI is precision farming, which uses the technology to optimize inputs like water, fertilizer and pesticides by adjusting them to the unique requirements of the crop and the field. AI techniques also make it possible to detect the pests and diseases through picture recognition and predictive analytics, which ultimately minimizes the crop loss and allows for prompt interventions. Widespread use may be hampered by issues with data quality, model interpretability, expensive prices and system integration. Furthermore, issues with labor impact, regulatory frameworks and scalability complicate its adoption. In order to fully utilize AI in agriculture, researchers, farmers and policymakers must work together to overcome these challenges and develop workable and accessible solutions that are suited to a variety of agricultural environments. Present review also highlighted how AI involvement has the ability to revolutionize agricultural sector by developing resilient methods that can both minimize environmental effects and meet the needs of an expanding global population. The agriculture industry can set the path for a sustainable future by adopting AI advances and guaranteeing the environmental stewardship and goals of food safety and security.

Towards Desirable Futures

The 20th anniversary of The Journal of Community Informatics signifies a milestone in the evolution of community informatics (CI) as a field dedicated to empowering communities through the strategic use of information and communication technology (ICT). This article offers some personal reflections on the origins and evolution of CI, tracing its roots to seminal works by scholars such as Michael Gurstein. It also tells the story of how urban informatics was inspired by CI as a distinct field of scholarship to study the interplay between people, place, and technology in urban environments. Building on this foundation, the present challenges and opportunities facing CI are explored, including issues of digital inclusion, ethical implications of emerging technologies, and the transformative potential of ICTs for social change. Looking ahead, the article envisions desirable futures for CI grounded in a life-centred approach that acknowledges the interconnectedness of humans and non-humans within larger ecological systems. Embracing a more-than-human paradigm, CI is uniquely positioned to advocate for ecological justice, amplify the voices of marginalised human and non-human communities, and foster collaboration between humans and the environment to create and protect resilient and sustainable habitat for life on this planet. Through these efforts, CI can contribute to a more just, equitable, and sustainable future for all living beings, averting the planetary ecocide that threatens our shared existence.

Contribution of Janata Awash Grant for Poverty Reduction in Makwanpur, Nepal

This research paper deals with the Janata Awash Grant, a program resembling Universal Basic Income (UBI), implemented in Raksirang Rural Municipality of Makawanpur District in Nepal, which specifically focuses on poverty reduction, social justice, and inclusion for impoverished families. The main aim of this paper is to examine the effectiveness of the Janata Awash Grant (JAG) in reducing poverty and promoting social justice among the designated families. The research uses a mixed-method approach, employing an explanatory sequential research design, and utilizes both primary and secondary data sources. Forty households out of 200 grant recipients were randomly chosen for primary data collection. Additionally, key informant interviews were conducted with selected representatives and informed beneficiaries to gather qualitative insights. The results indicate significant improvements in the economic status of recipient families, leading to a decrease in poverty rates and an overall enhancement in quality of life. Qualitative analysis highlights the broader societal and ethical impacts of receiving a guaranteed income, emphasizing the transformative potential of UBI beyond its monetary benefits.

The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.

Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our understanding and ability to combat resistant pathogens. This review aims to explore the integration of machine learning (ML) and epidemiological approaches to understand, predict, and combat carbapenem-resistant pathogens. It examines how leveraging large datasets and advanced computational techniques can identify patterns, predict outbreaks, and inform targeted intervention strategies. The review synthesizes current knowledge on the mechanisms of carbapenem resistance, highlights the strengths and limitations of traditional epidemiological methods, and evaluates the transformative potential of ML. Real-world applications and case studies are used to demonstrate the practical benefits of combining ML and epidemiology. Technical and ethical challenges, such as data quality, model interpretability, and biases, are also addressed, with recommendations provided for overcoming these obstacles. By integrating ML with epidemiological analysis, significant improvements can be made in predictive accuracy, identifying novel patterns in disease transmission, and designing effective public health interventions. Case studies illustrate the benefits of interdisciplinary collaboration in tackling carbapenem resistance, though challenges such as model interpretability and data biases must be managed. The combination of ML and epidemiology holds great promise for enhancing our capacity to predict and prevent carbapenem-resistant infections. Future research should focus on overcoming technical and ethical challenges to fully realize the potential of these approaches. Interdisciplinary collaboration is key to developing sustainable strategies to combat antimicrobial resistance (AMR), ultimately improving patient outcomes and safeguarding public health.

Young people’s participation in urban landscape planning and transformation: a scoping review of interactive approaches

AbstractThis article presents a review of methodological advancements and transformative potential in participatory processes with young people in urban landscapes. It offers a framework understanding of current types of participatory processes in relation to urban landscape planning, and underlines lifeworld and action as two key components in transformative participatory processes with young people. The two-step scoping review of a literature sample (n = 44 studies) finds a prevalence of less-interactive approaches to young people’s participation in urban landscapes, and subsequently analyses openings for lifeworld and action in the more interactive approaches described (n = 17 studies). The interactive methods described demonstrate opportunities to facilitate young people’s own articulations of lifeworlds within the urban landscape, especially in extended processes deploying multiple creative methods. The relatively few examples of actions and interventions resulting from participatory processes points to the need for further development and the ambition to include young people in transforming urban landscapes towards increased sustainability.

A Scoping Review on the Application of Artificial Intelligence in Treating Rare Diseases

Rare diseases affect a small percentage of the population but often present significant challenges in diagnosis, treatment, and management due to their complex and heterogeneous nature. Recent advancements in artificial intelligence (AI) have shown promising potential to address these challenges by improving early diagnosis, personalized treatment strategies, and drug discovery. This scoping review explores the current landscape of AI applications in rare disease treatment. We examine the role of AI-driven tools in enhancing diagnostic accuracy, optimizing treatment pathways, and facilitating drug repurposing for rare conditions. Additionally, we highlight the limitations and ethical concerns associated with AI implementation, such as data privacy, the need for high-quality datasets, and algorithmic transparency. The findings suggest that while AI holds transformative potential, its integration into clinical practice for rare diseases requires multidisciplinary collaboration, ongoing research, and regulatory oversight.