Challenges Of Artificial Intelligence Research Articles

Artificial intelligence methods used in various aquaculture applications: A systematic literature review

AbstractThis research article presents a systematic literature review on the current state‐of‐the‐art artificial intelligence (AI) methodologies used in aquaculture applications. As the demand for seafood continues to grow, the aquaculture industry faces numerous challenges, including disease management, feeding optimization, water quality monitoring, and extraction of aquaculture area. To address these challenges effectively and sustainably, AI techniques have been increasingly applied in aquaculture systems over recent years. This review aims to analyze various AI methodologies utilized within different aspects of aquacultural practices. By examining existing studies and identifying trends and gaps in research areas related to AI integration into aquaculture practices, this paper provides valuable insights for further advancements. The purpose was to synthesize current knowledge on application and its challenges in implementing AI technologies within the commercial aquaculture industry. Specifically, the review is to identify and analyze peer‐reviewed studies reporting on applications of AI technologies in aquaculture industry, to classify and summarize the key findings from the selected studies in aquaculture operations through AI, and to evaluate and discuss any challenges reported regarding the implementation and adoption of AI solutions in commercial aquaculture. The overall goal was to comprehensively assess these via a systematic literature review process. Challenges of AI technologies and methods were identified in the research literature for applying AI to optimize commercial aquaculture practices and production. An exhaustive search of a scholarly database from Scopus, was performed and papers published in English between 2020 and 2024 were considered for inclusion. After a rigorous screening process involving over 116 studies, 57 highly relevant works were identified and analyzed according to key themes involving demonstrated AI applications, employed methodologies and challenges that are expected when applying such methods. The findings revealed that AI‐driven tools such as computer vision, machine learning, and predictive modeling hold much potential for enhancing sustainability, efficiency, and productivity within aquaculture operations through applications like disease monitoring, environmental management, and production optimization. However, the review also uncovered substantial challenges that will continue limiting widespread adoption, including restricted access to representative data, prohibitive expenses, technical complexities, lack of social acceptance, and data privacy and security concerns. This comprehensive synthesis of the current evidence available provides an empirical foundation upon which further progress can be built by identifying priority areas requiring additional research efforts to fully address challenges on the responsible integration of suitable solutions for the commercial aquaculture industry globally.

Cyborging HRM theory: from evolution to revolution – the challenges and trajectories of AI for the future role of HRM

PurposeHuman Resource Management (HRM) is a critical organizational function, which has continued to evolve. We aim to explore how different HRM will be in the workplace of the future and why, from both strategic and practical perspectives. We present and discuss core HRM practices, such as recruitment, selection and training, as well as peripheral activities, such as monitoring health and safety, and diversity management, reflecting on how they may transform in the workplace of the future.Design/methodology/approachThis is a conceptual thought piece, building on the Substitution, Augmentation, Modification and Redefinition (SAMR) model, to offer a futuristic view of HRM in the era of AI.FindingsDiscussing the contemporary challenges of Artificial Intelligence, which we predict will lead to what we term Cyborging HRM.Practical implicationsThis study can help HR managers and practitioners to be prepared for AI-embedded HRM systems in the future. For academics, it offers an innovative framework to establish future writing on HRM in the AI era.Originality/valueAI is pushing HRM and the profession will have to undergo a revolutionary rather than evolutionary transformation in order to remain a necessary and valuable function for organizations. Our elaboration of the SAMR model and suggested implications for the future transformation of HRM should be worthwhile to organizations, management and the wider society.

СУЧАСНИЙ ІНТЕРНЕТ-МАРКЕТИНГ З ВИКОРИСТАННЯМ ТЕХНОЛОГІЙ ШТУЧНОГО ІНТЕЛЕКТУ

The article discusses the essence and features of modern Internet marketing based on the use of artificial intelligence (AI) technologies. The article examines how AI can help online marketers collect and analyze consumer data, segment and personalize audiences, create and distribute effective content, optimize and automate advertising campaigns, and measure and improve marketing results. The article also analyzes the latest trends in the use of AI technologies by Ukrainian and foreign companies and identifies their advantages.The investigation identified effective AI tools in online marketing, such as chatbots, recommender systems, text generators, sentiment analysers, face and speech recognition systems, etc. The article presents a comparison of AI tools in online marketing of Ukrainian and foreign companies. Foreign companies use modern products that have no analogues in Ukraine. This is due to the popularity of the trend of using AI technologies in foreign companies. In Ukraine, this trend is developing and we can see it in the use of chatbots and CRM systems. They have been implemented by such companies as PrivatBank and Promodo. Despite this, the development of foreign companies is progressive. Previously, only large corporations could afford to use AI technologies, but now young startups are catching up with the trend. AI combined with human capabilities can actually turn business into a universal tool for profit. However, this path is not without obstacles, so the article highlights the challenges of AI that may affect the economic, legal, ethical, and social spheres of life. Scenarios for preventing possible problems allow for the effective use of AI's potential. The article suggests measures that should help society adapt to changes in the socio-economic environment associated with the displacement of humans by AI. It is important to understand the advantages and disadvantages of modern online marketing tools to ensure effective solutions to business problems.

Ethical Artificial Intelligence Framework Theory (EAIFT): A New Paradigm for Embedding Ethical Reasoning in AI Systems

The rapid development of artificial intelligence (AI) has created several ethical issues, including bias, a lack of transparency, and privacy concerns, demanding the incorporation of ethical governance directly into AI systems. This study introduces the Ethical Artificial Intelligence Framework Theory (EAIFT), a novel approach to incorporating ethical reasoning into AI. It emphasizes real-time oversight, open decision-making, bias detection, and the ability to change ethical and legal norms. EAIFT advocates for establishing "ethical AI watchdogs" that automatically monitor and ensure the ethical operation of AI systems, together with dynamic compliance algorithms that can adapt to regulatory changes. The paradigm also encourages transparency and explainability to build user trust and detect and correct biases to ensure fairness. This paper employs a qualitative methodology that combines stakeholder interviews, content analysis, and expert commentary to evaluate EAIFT's potential to increase ethical accountability in various areas, including healthcare, banking, and criminal justice. The findings suggest that EAIFT outperforms existing ethical frameworks by proactively reducing biases, increasing transparency, and ensuring adherence to ethical standards. While presenting a comprehensive and adaptable technique, the study also acknowledges limitations in empirical testing and the need for additional research to widen EAIFT's applicability to future ethical challenges in artificial intelligence. The paper suggests future research subjects, such as empirical testing in different scenarios, a more in-depth examination of ethical risks, and the inclusion of the framework into new AI technologies to promote responsible AI governance by societal norms and values.

Introduction to the Special Issues

Abstract Artificial intelligence (AI) is rapidly reshaping our world. As AI systems become increasingly autonomous and integrated into various sectors, fundamental ethical issues such as accountability, transparency, bias, and privacy are exacerbated or morph into new forms. This introduction provides an overview of the current ethical landscape of AI. It explores the pressing need to address biases in AI systems, protect individual privacy, ensure transparency and accountability, and manage the broader societal impacts of AI on labor markets, education, and social interactions. It also highlights the global nature of AI's challenges, such as its environmental impact and security risks, stressing the importance of international collaboration and culturally sensitive ethical guidelines. It then outlines three unprecedented challenges AI poses to copyright and intellectual property rights; individual autonomy through AI's “hypersuasion”; and our understanding of authenticity, originality, and creativity through the transformative impact of AI-generated content. The conclusion emphasizes the importance of ongoing critical vigilance, imaginative conceptual design, and collaborative efforts between diverse stakeholders to deal with the ethical complexities of AI and shape a sustainable and socially preferable future. It underscores the crucial role of philosophy in identifying and analyzing the most significant problems and designing convincing and feasible solutions, calling for a new, engaged, and constructive approach to philosophical inquiry in the digital age.

The Pivotal Role of Libraries in Sustainable AI Development

Artificial Intelligence (AI) has gained exceptional public and media coverage since the launch of the ChatGPT platform, a generative conversational intelligence, in November 2022. Nevertheless, AI is already an integral part of our digital daily lives, as we navigate through social networks, use our GPS, or consult recommendations on e-commerce websites. Due to its pervasive influence across all sectors of our societies, AI is gradually becoming a pivotal subject in terms of regulation, societal direction, and legislation. As early as 2021, UNESCO published a report presenting avenues for ethical considerations in AI. In June 2023, the European Union also established a regulatory framework outlining requirements and obligations for AI usage. As a digital manifestation and given the "new ways in which its use influences human thinking, interaction and decision-making and affects education, human, social and natural sciences, culture, and communication and information" (UNESCO. General Conference, 41st, 2021), public libraries have a role to play in enabling residents within their communities to grasp this technology. Their role is all the more significant as AI generates concerns and distrust (Gillath et al., 2021) among populations when "libraries also continue to enjoy a high level of trust and appreciation in most of their communities" (Arlitsch \& Newell, 2017). Understanding AI thus constitutes a new cornerstone for accessing the necessary information to advance sustainable development, as outlined in the Lyon Declaration (2014). Moreover, comprehension of AI aligns with the ethical concerns articulated by UNESCO in terms of explainability and transparency and aligns with several Sustainable Development Goals (SDGs) of the 2030 Agenda. These goals include quality education (4), industry, innovation and infrastructure (9), reduced inequalities (10), sustainable cities and communities (11), as well as responsible consumption and production (12). The role of public libraries in advancing the goals of the 2030 Agenda is beyond dispute (IFLA, 2016), and various digital literacies are already integral to their actions. As AI is predominantly developed by global economic giants and permeates all of our practices, "Shouldn’t [libraries] be the bastions of information literacy and information privacy in an AI world?" (Cox et al., 2018). Thus, to what extent can public libraries take on this subject to promote and offer relevant literacy? For this study, we will conduct a cross-analysis among three European countries—Spain, France, and Italy—to provide insights into the diverse ways in which AI influences professional practices. Through a literature review and semi-structured interviews, the objective is to delineate the challenges of Artificial Intelligence within the framework of the Agenda 2030 program. Subsequently, we will delve into the specificity of AI Literacy in comparison to Information Literacy, a practice already adopted by libraries. Finally, we will analyze the current and prospective role of AI in libraries to propose avenues for implementing concrete actions.

Machine learning models for personalised healthcare on marketable generative-AI with ethical implications

Personalised healthcare, underpinned by a deep understanding of individual patient variability, demands innovative solutions. Machine Learning (ML) models offer a promising avenue to achieve this by facilitating the development of enhanced Digital Twins (DTs). This research proposes a novel framework for creating DTs tailored to individual patients, considering not only physical attributes but also the intricate interplay of social, and biological factors. By capturing this comprehensive patient profile, ML-powered DTs have the potential to revolutionise healthcare by enabling predictive, preventive, and personalised care strategies, ultimately leading to improved patient outcomes and the development of a marketable, trust-worthy product. Current healthcare solutions lack personalisation, often failing to consider individual differences in disease presentation and response to treatment. Traditional DTs in healthcare often adopt a disease-centric or organ-specific approach, thereby restricting their capacity to deliver comprehensive, personalised care. To address this limitation, we propose a holistic Artificial Intelligence (AI) framework centered on ML models. Initially focusing on diabetes, our research aims to enhance diagnosis, treatment, and predictive capabilities through personalised insights, thereby optimising patient outcomes and care management. The benefits identified with our ML model are early disease prevention and risk stratification, optimised treatment planning and therapy selection, enhanced patient-physician communication and shared decision-making, reduced healthcare costs and improved resource allocation. Our models are designed to optimise patient care while prioritising safety and societal benefit. To ensure this, we have conducted a thorough assessment of potential ethical implications. Key challenges identified include data privacy and security, algorithmic bias, diagnostic accuracy, data interoperability and standardisation, integration with existing healthcare systems, ethical management of sensitive patient data, refinement of ML methodologies, addressing legal and ethical AI challenges, and suggesting robust ethical guidelines. A comprehensive evaluation of accuracy, reliability, and associated risks will be conducted prior to full-scale integration into the healthcare ecosystem to establish a robust ethical framework for our research models.

Yabancı Dil Öğretiminde Öğretmen ve Öğrenci Merkezli Yaklaşımlar: Sosyal Formların Etkili Kullanımı

In today's world, where information is easily accessible and the capacity of artificial intelligence challenges the limits of human intellect, fundamental changes are required in education systems. However, traditional educational approaches in Turkey still treated students as passive recipients of information, which can negatively impact the development of skills such as critical thinking, problem-solving and creative idea generation. Particularly in the teaching of social sciences, humanities and Turkish as a foreign language, student-centered and goal-oriented approaches that rely on the effective use of social forms play a crucial role in enhancing students' language skills and engaging them in the active learning process. The purpose of this study is to introduce student-centered teaching, social forms and goal-oriented teaching approaches and to examine how these approaches can create a transformation in education. Specifically, the positive effects of these methods on improving language skills, critical thinking, creative problem-solving, and effective communication abilities will be emphasized. As a method, literature review and theoretical analysis will be used. Existing studies on these approaches will be examined and the effectiveness of student-centered teaching methods in the context of social forms and goal-oriented strategies used in education will be evaluated. Additionally, a comparative analysis of these methods with traditional approaches will be conducted. The results show that student-centered teaching, social forms, and goal-oriented teaching strategies deeply enhance students' language skills and encourage them to think more independently, critically and creatively. These findings indicate that Turkey's education system needs to adopt innovative approaches to better align with the demands of the current information age. The results of this study provide a significant foundation for the development and implementation of new pedagogical strategies in the fields of social sciences and language teaching. Moreover, these innovative approaches, supported by PISA results, could play a critical role in improving Turkey's educational performance. Keywords: Foreign Language Teaching, Student-Centered Approaches, Goal-Oriented Instruction, Social Forms, Plenum, Partner Work, Group Work, Individual Work

Application of AI integrated semi-autonomous robotics in spine surgery: A review

Spine surgery is a high-risk surgery associated with high precision and accuracy. Artificial intelligence (AI) and semi-autonomous robotics can benefit the surgery. This paper explores the impact of AI and robotics on improving surgical precision, accuracy, and safety with details on preoperative planning, optimal screw placement, and intraoperative real-time monitoring. In addition, the potential of AI-driven simulation spaces for training and skill assessment was evaluated. Various AI models and robotic systems have improved surgical outcomes by providing precise guidance and reducing human error. This review aims to summarize the recent developments and highlight the challenges of AI and semi-autonomous robotics in spine surgery.

Human-centered Artificial Intelligence Development

Few researchers provide a wider vision of artificial feet, hands, mouths, eyes, ears, and brains. This limits our vision of them and their significant impacts on the modern Industrial Revolution and Artificial Intelligence (AI) history. This article presents a novel perspective on human-centered social development starting from artificial feet. After briefly reviewing AI, this article explores the age of AI and artificial feet, hands, mouths, eyes, ears, and brains. It also applies AI to artificial feet and artificial brains. The research reveals that artificial feet are one of the origins of the Industrial Revolution and a real foundation of AI. The study demonstrates that artificial feet and brains liberate our body and society, whereas from artificial brains to artificial feet is control of our body and society. This article also looks at AI's trends and challenges. The approach in this article will facilitate the research and development of big data, analytics, and intelligences.

Artificial Intelligence in Dermatopathology: a systematic review.

Medical research, driven by advancing technologies like Artificial Intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores into AI's role, challenges, opportunities, and future potential in enhancing dermatopathological diagnosis and care. Adhering to PRISMA and Cochrane Handbook standards, this systematic review explored AI's function in dermatopathology. It employed an interdisciplinary method, encompassing diverse study types and comprehensive database searches. Inclusion criteria encompassed peer-reviewed articles from 2000 to 2023, with a focus on practical AI use in dermatopathology. Numerous studies have investigated AI's potential in dermatopathology. We reviewed 112 papers. Notable applications include AI classifying histopathological images of nevi and melanomas, although challenges exist regarding subtype differentiation and generalizability. AI achieved high accuracy in melanoma recognition from formalin-fixed paraffin-embedded samples but faced limitations due to small datasets. Deep learning algorithms showed diagnostic accuracy for specific skin conditions, but challenges persisted, such as small sample sizes and the need for prospective validation. This systematic review underscores AI's potential in enhancing dermatopathology for better diagnosis and patient care. Addressing challenges like limited datasets and potential biases is essential. Future directions involve expanding datasets, conducting validation studies, promoting interdisciplinary collaboration, and creating patient-centred AI tools to enhance dermatopathology's accuracy, accessibility, and patient-focused care.

Artificial intelligence in the United Arab Emirates public sector: a systematic literature review

<p><span lang="EN-US">This systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.</span></p>

The Use of AI in Predicting Patient Outcomes

Artificial intelligence (AI) has emerged as a revolutionary tool in healthcare, significantly improving the ability to predict patient outcomes. From chronic disease management to personalized treatments, AI can analyze vast datasets and provide timely, accurate predictions that guide clinical decision-making. Traditional statistical models have been useful but often limited in handling complex data relationships. AI, through machine learning (ML) and deep learning (DL), transcends these limitations, offering improved predictions across diseases like cancer, tuberculosis, and HIV/AIDS. This paper investigates current AI methods, advantages, challenges, and ethical considerations associated with its use in healthcare, highlighting the transformative potential of AI in outcome prediction while addressing concerns around data privacy, regulatory constraints, and model transparency. Keywords: Artificial Intelligence, Machine Learning, Patient Outcomes, Clinical Decision Support Systems, Predictive Analytics.

Impact of AI on Manufacturing and Quality Assurance in Medical Device and Pharmaceuticals Industry

Global health and well-being largely depend on the pharmaceutical and medical device industries. Manufacturing and quality assurance (QA) processes are crucial to maintaining product efficacy, safety, and regulatory compliance in these sectors. Artificial intelligence (AI) integration presents ground-breaking opportunities to enhance these processes. This study aims to systematically assess the impact of AI on manufacturing and QA in these pharmaceutical and medical device industries. It examines the benefits, challenges, and ethical and legal implications of integrating AI. It offers a thorough understanding of how AI technology can and has been successfully integrated to enhance business operations. An extensive literature analysis was carried out to investigate AI's application, role, benefits, and challenges in manufacturing and quality assurance processes in both industries. Research was also conducted on emerging trends, future developments, and regulatory issues. Increased productivity, early detection of defects, safer and higher-quality goods, improved regulatory compliance, reduced costs, and more flexibility and scalability are some advantages of AI technologies. However, significant obstacles are also to overcome, such as high capital costs, data quality and availability issues, legacy system integration, ethical concerns about bias and data privacy, difficulties with regulatory compliance, and a lack of AI-skilled workers. Case studies show how AI has been utilized to guarantee regulatory compliance and optimize processes. AI integration has much to offer the pharmaceutical and medical device industries in terms of improved manufacturing and quality assurance procedures. By addressing restrictions and seizing novel opportunities, these industries can use AI's transformative potential to support innovation, enhance product quality and safety, ensure regulatory compliance, and improve global health outcomes.

Artificial Intelligence, Cyber Stalking, And The Future Of Digital Privacy: A Legal Perspective

In today’s rapidly evolving digital landscape, artificial intelligence (AI) has become a transformative force, significantly impacting various aspects of human life. While AI offers numerous benefits, it also presents significant challenges, particularly in the realm of cybercrime. Among the most pressing concerns are cyber stalking and digital privacy. this paper discusses the concept ,significance and challenges of AI in today's world from a legal aspects.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. ObjectivesThis study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. MethodsWe employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. ResultsSix themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. ConclusionsThe findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Carting the Significance of Artificial Intelligence in English Language Teaching

The use of educational technologies in English language teaching (ELT) has become widely accepted in the post-pandemic era, and, for better or worse, some of these technologies rely on artificial intelligence (AI). Artificial intelligence is on the rise as witnessed in the growing popularity of dialogue systems like Alexa. However, some key criteria still need to be met before it can serve as a substitute for a real-life language teacher: spontaneity, creativity and shared knowledge. As an area of technological growth and increasing financial investment, we are likely to see more AI-driven technologies in teaching and learning in the post-pandemic ELT world. ELT will not be immune to this development, and it behooves us as language teachers to be familiar with AI's current benefits and challenges, so that we can better prepare for that future. This article describes how AI is currently used in ELT and explores some of the opportunities and challenges that AI can provide for learners, teachers and institutions.

AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI

This article addresses the ethical challenges posed by generative artificial intelligence (AI) tools in higher education and explores the first responses of universities to these challenges globally. Drawing on five key international documents from the UN, EU, and OECD, the study used content analysis to identify key ethical dimensions related to the use of generative AI in academia, such as accountability, human oversight, transparency, or inclusiveness. Empirical evidence was compiled from 30 leading universities ranked among the top 500 in the Shanghai Ranking list from May to July 2023, covering those institutions that already had publicly available responses to these dimensions in the form of policy documents or guidelines. The paper identifies the central ethical imperative that student assignments must reflect individual knowledge acquired during their education, with human individuals retaining moral and legal responsibility for AI-related wrongdoings. This top-down requirement aligns with a bottom-up approach, allowing instructors flexibility in determining how they utilize generative AI especially large language models in their own courses. Regarding human oversight, the typical response identified by the study involves a blend of preventive measures (e.g., course assessment modifications) and soft, dialogue-based sanctioning procedures. The challenge of transparency induced the good practice of clear communication of AI use in course syllabi in the first university responses examined by this study.

Streamlined Deep Learning Models for Move Prediction in Go-Game

Due to the complexity of search space and move evaluation, the game of Go has been a long-standing challenge for artificial intelligence (AI) to achieve a high level of proficiency. It was not until DeepMind proposed the deep neural network and tree search algorithm AlphaGo in 2014 that an efficient learning algorithm was developed, marking a significant milestone in AI technology. In light of the key technologies in AI Computer Go, this work examines move prediction across different Go rankings and sophisticatedly develops two deep learning models by combining and extending the feature extraction methods of AlphaGo. Specifically, effective modules for neural networks are proposed to guide learning through complicated Go situations based on the Inception module in GoogLeNet and the Convolutional Block Attention Module (CBAM). Subsequently, the two models are combined by ensemble learning to improve generalization, and these streamlined models significantly reduce the number of model parameters to the scale of one hundred thousand. Experimental results show that our models achieve prediction accuracies of 46.9% and 50.8% on two different Go datasets, outperforming conventional models by significant margins. This work not only advances AI development in the Go-game but also offers an innovative approach to related studies.

Score Images as a Modality: Enhancing Symbolic Music Understanding through Large-Scale Multimodal Pre-Training.

Symbolic music understanding is a critical challenge in artificial intelligence. While traditional symbolic music representations like MIDI capture essential musical elements, they often lack the nuanced expression in music scores. Leveraging the advancements in multimodal pre-training, particularly in visual-language pre-training, we propose a groundbreaking approach: the Score Images as a Modality (SIM) model. This model integrates music score images alongside MIDI data for enhanced symbolic music understanding. We also introduce novel pre-training tasks, including masked bar-attribute modeling and score-MIDI matching. These tasks enable the SIM model to capture music structures and align visual and symbolic representations effectively. Additionally, we present a meticulously curated dataset of matched score images and MIDI representations optimized for training the SIM model. Through experimental validation, we demonstrate the efficacy of our approach in advancing symbolic music understanding.