Challenges in Implementing AI Technology Smart Farming in Agricultural Sector – A Literature Review

BackgroundNew artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.ObjectiveThis study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.MethodsWe created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.ResultsWe contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.ConclusionsFew tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals’ readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

Performance of an artificial intelligence tool with real-time clinical workflow integration - Detection of intracranial hemorrhage and pulmonary embolism.

This article examines the essence and characteristics of artificial intelligence (AI) and its applications in various agriculture segments. Special attention is paid to the challenges of implementing AI in crop production, animal husbandry, resource management, and analytical processes. The role of robotics is examined as a key factor in the digital transformation of the agricultural sector, facilitating the adoption of new production approaches. The article highlights the main advantages of AI in the agricultural sector, such as the automation of routine tasks, reduction of manual labor costs, increased production efficiency, and the creation of new products. The use of intelligent technologies optimizes resources and boosts productivity, contributing to the competitiveness of agricultural enterprises. The article also reviews global experiences in the implementation of AI and robotics in agriculture. Examples of successful use of these technologies by leading companies are provided, along with an analysis of the experience of Ukrainian agricultural enterprises. Positive aspects of AI implementation, such as increased efficiency and crop yields, are studied, while drawbacks and risks associated with adapting new technologies to the specific conditions of Ukrainian agriculture are also highlighted. The conclusions of the article emphasize that the use of AI is a promising direction for the development of the agricultural sector. AI technologies help address key challenges related to food security and sustainable development. Despite the challenges and risks, AI's potential to enhance agricultural production efficiency is significant, and the future of agriculture largely depends on the further development and implementation of these technologies. The widespread introduction of intelligent technologies can not only transform agricultural processes, but also make them more environmentally sustainable and economically profitable in the long term. Key words: artificial intelligence, agricultural sector, innovative technologies, agriculture, crop production, animal husbandry, robotics, machine intelligence.

In recent years, significant breakthroughs are observed in developing artificial intelligence (AI), which radically affects the most diverse areas of human life and activity. This review article examines the introduction of AI in agriculture using the example of China, which is a leader in the pace of introduction of AI into the national economy and seeks to head off the United States in the overall leadership in the development of AI technologies. Thanks to active work in this direction and significant financial investments in this area, China has managed to transform substantially its agricultural sector. The purpose of the article is to analyze the current trends and opportunities offered by the application of AI in the agricultural sector of the PRC economy. To this end, a series of difficulties that China faces in the development of agriculture is considered, as well as the main currently known areas of application of AI in agriculture and the types of technologies used. Information on Chinese companies using AI technologies in agriculture is summarized, including their specialization, technologies used and benefits gained. Early evidence shows that AI is being applied firstly to improve productivity and manufacturing performance, and secondly to address labor shortages and achieve manufacturing sustainability. Analysis of the situation allows us to conclude that AI can become the main driving force in the development of agriculture.

The Value of Artificial Intelligence for Healthcare Decision Making—Lessons Learned

Introduction Author Arthur C. Clarke famously argued that in science fiction literature “any sufficiently advanced technology is indistinguishable from magic” (Clarke). On 30 November 2022, technology company OpenAI publicly released their Large Language Model (LLM)-based chatbot ChatGPT (Chat Generative Pre-Trained Transformer), and instantly it was hailed as world-changing. Initial media stories about ChatGPT highlighted the speed with which it generated new material as evidence that this tool might be both genuinely creative and actually intelligent, in both exciting and disturbing ways. Indeed, ChatGPT is part of a larger pool of Generative Artificial Intelligence (AI) tools that can very quickly generate seemingly novel outputs in a variety of media formats based on text prompts written by users. Yet, claims that AI has become sentient, or has even reached a recognisable level of general intelligence, remain in the realm of science fiction, for now at least (Leaver). That has not stopped technology companies, scientists, and others from suggesting that super-smart AI is just around the corner. Exemplifying this, the same people creating generative AI are also vocal signatories of public letters that ostensibly call for a temporary halt in AI development, but these letters are simultaneously feeding the myth that these tools are so powerful that they are the early form of imminent super-intelligent machines. For many people, the combination of AI technologies and media hype means generative AIs are basically magical insomuch as their workings seem impenetrable, and their existence could ostensibly change the world. This article explores how the hype around ChatGPT and generative AI was deployed across the first six months of 2023, and how these technologies were positioned as either utopian or dystopian, always seemingly magical, but never banal. We look at some initial responses to generative AI, ranging from schools in Australia to picket lines in Hollywood. We offer a critique of the utopian/dystopian binary positioning of generative AI, aligning with critics who rightly argue that focussing on these extremes displaces the more grounded and immediate challenges generative AI bring that need urgent answers. Finally, we loop back to the role of schools and educators in repositioning generative AI as something to be tested, examined, scrutinised, and played with both to ground understandings of generative AI, while also preparing today’s students for a future where these tools will be part of their work and cultural landscapes. Hype, Schools, and Hollywood In December 2022, one month after OpenAI launched ChatGPT, Elon Musk tweeted: “ChatGPT is scary good. We are not far from dangerously strong AI”. Musk’s post was retweeted 9400 times, liked 73 thousand times, and presumably seen by most of his 150 million Twitter followers. This type of engagement typified the early hype and language that surrounded the launch of ChatGPT, with reports that “crypto” had been replaced by generative AI as the “hot tech topic” and hopes that it would be “‘transformative’ for business” (Browne). By March 2023, global economic analysts at Goldman Sachs had released a report on the potentially transformative effects of generative AI, saying that it marked the “brink of a rapid acceleration in task automation that will drive labor cost savings and raise productivity” (Hatzius et al.). Further, they concluded that “its ability to generate content that is indistinguishable from human-created output and to break down communication barriers between humans and machines reflects a major advancement with potentially large macroeconomic effects” (Hatzius et al.). Speculation about the potentially transformative power and reach of generative AI technology was reinforced by warnings that it could also lead to “significant disruption” of the labour market, and the potential automation of up to 300 million jobs, with associated job losses for humans (Hatzius et al.). In addition, there was widespread buzz that ChatGPT’s “rationalization process may evidence human-like cognition” (Browne), claims that were supported by the emergent language of ChatGPT. The technology was explained as being “trained” on a “corpus” of datasets, using a “neural network” capable of producing “natural language“” (Dsouza), positioning the technology as human-like, and more than ‘artificial’ intelligence. Incorrect responses or errors produced by the tech were termed “hallucinations”, akin to magical thinking, which OpenAI founder Sam Altman insisted wasn’t a word that he associated with sentience (Intelligencer staff). Indeed, Altman asserts that he rejects moves to “anthropomorphize” (Intelligencer staff) the technology; however, arguably the language, hype, and Altman’s well-publicised misgivings about ChatGPT have had the combined effect of shaping our understanding of this generative AI as alive, vast, fast-moving, and potentially lethal to humanity. Unsurprisingly, the hype around the transformative effects of ChatGPT and its ability to generate ‘human-like’ answers and sophisticated essay-style responses was matched by a concomitant panic throughout educational institutions. The beginning of the 2023 Australian school year was marked by schools and state education ministers meeting to discuss the emerging problem of ChatGPT in the education system (Hiatt). Every state in Australia, bar South Australia, banned the use of the technology in public schools, with a “national expert task force” formed to “guide” schools on how to navigate ChatGPT in the classroom (Hiatt). Globally, schools banned the technology amid fears that students could use it to generate convincing essay responses whose plagiarism would be undetectable with current software (Clarence-Smith). Some schools banned the technology citing concerns that it would have a “negative impact on student learning”, while others cited its “lack of reliable safeguards preventing these tools exposing students to potentially explicit and harmful content” (Cassidy). ChatGPT investor Musk famously tweeted, “It’s a new world. Goodbye homework!”, further fuelling the growing alarm about the freely available technology that could “churn out convincing essays which can't be detected by their existing anti-plagiarism software” (Clarence-Smith). Universities were reported to be moving towards more “in-person supervision and increased paper assessments” (SBS), rather than essay-style assessments, in a bid to out-manoeuvre ChatGPT’s plagiarism potential. Seven months on, concerns about the technology seem to have been dialled back, with educators more curious about the ways the technology can be integrated into the classroom to good effect (Liu et al.); however, the full implications and impacts of the generative AI are still emerging. In May 2023, the Writer’s Guild of America (WGA), the union representing screenwriters across the US creative industries, went on strike, and one of their core issues were “regulations on the use of artificial intelligence in writing” (Porter). Early in the negotiations, Chris Keyser, co-chair of the WGA’s negotiating committee, lamented that “no one knows exactly what AI’s going to be, but the fact that the companies won’t talk about it is the best indication we’ve had that we have a reason to fear it” (Grobar). At the same time, the Screen Actors’ Guild (SAG) warned that members were being asked to agree to contracts that stipulated that an actor’s voice could be re-used in future scenarios without that actor’s additional consent, potentially reducing actors to a dataset to be animated by generative AI technologies (Scheiber and Koblin). In a statement issued by SAG, they made their position clear that the creation or (re)animation of any digital likeness of any part of an actor must be recognised as labour and properly paid, also warning that any attempt to legislate around these rights should be strongly resisted (Screen Actors Guild). Unlike the more sensationalised hype, the WGA and SAG responses to generative AI are grounded in labour relations. These unions quite rightly fear the immediate future where human labour could be augmented, reclassified, and exploited by, and in the name of, algorithmic systems. Screenwriters, for example, might be hired at much lower pay rates to edit scripts first generated by ChatGPT, even if those editors would really be doing most of the creative work to turn something clichéd and predictable into something more appealing. Rather than a dystopian world where machines do all the work, the WGA and SAG protests railed against a world where workers would be paid less because executives could pretend generative AI was doing most of the work (Bender). The Open Letter and Promotion of AI Panic In an open letter that received enormous press and media uptake, many of the leading figures in AI called for a pause in AI development since “advanced AI could represent a profound change in the history of life on Earth”; they warned early 2023 had already seen “an out-of-control race to develop and deploy ever more powerful digital minds that no one – not even their creators – can understand, predict, or reliably control” (Future of Life Institute). Further, the open letter signatories called on “all AI labs to immediately pause for at least 6 months the training of AI systems more powerful than GPT-4”, arguing that “labs and independent experts should use this pause to jointly develop and implement a set of shared safety protocols for advanced AI design and development that are rigorously audited and overseen by independent outside experts” (Future of Life Institute). Notably, many of the signatories work for the very companies involved in the “out-of-control race”. Indeed, while this letter could be read as a moment of ethical clarity for the AI industry, a more cynical reading might just be that in warning that their AIs could effectively destroy the w

This comparative review explores the advancements and applications of Artificial Intelligence (AI) in agriculture, focusing on the developments in the United States (USA) and Africa. The integration of AI technologies in agriculture has witnessed significant progress globally, addressing challenges and transforming traditional farming practices. In the USA, precision agriculture and smart farming techniques driven by AI have become integral components of modern agricultural systems. These innovations include autonomous machinery, drone technology for crop monitoring, and predictive analytics for yield optimization. In contrast, the application of AI in African agriculture presents a distinct set of challenges and opportunities. The review delves into initiatives aimed at leveraging AI to enhance agricultural productivity, improve resource management, and address food security concerns in various African nations. These efforts include the deployment of AI for pest and disease detection, crop monitoring in remote areas, and the implementation of data-driven decision-making tools to support smallholder farmers. The comparative analysis sheds light on the disparities in AI adoption between the USA and Africa, emphasizing factors such as infrastructure, technological accessibility, and resource availability. Additionally, it explores collaborative efforts and partnerships that bridge the gap and contribute to the sustainable development of AI in African agriculture. As both regions navigate the complexities of implementing AI in agriculture, this review underscores the potential for technology to play a pivotal role in addressing global food challenges. The findings highlight the need for tailored approaches, policy frameworks, and international collaborations to ensure inclusive and equitable access to AI-driven innovations in agriculture, fostering a shared commitment to sustainable and technologically empowered farming practices.

Introduction. Modern artificial intelligence (AI) tools have significant didactic potential, allowing to transfer the learning process to a higher level in terms of solving cognitive tasks. At the same time, the degree and scope of AI implementation in the educational process will largely depend on the ability of teachers to integrate AI tools into the traditional process of teaching disciplines. Natural use of AI in pre-service teacher education programs is possible, on the one hand, by integrating AI into the practice of teaching students specialized disciplines, practical and research work at the university, and on the other hand, by developing students' competence in the field of teaching methods based on AI. The purpose of the study is to develop a structural model of pre-service teacher training based on AI technologies. Materials and methods. The following research methods were applied in the study: analysis of pedagogical and methodological literature on the integration of AI into education in general and teacher training in particular, a students’ survey on their experience in using AI while studying at the university. The materials used included academic papers (Articles and Reviews) from scientific journals indexed in the Web of Science (Core Collection) and Scopus (Q1, Q2). An online survey was conducted to determine in which disciplines and within which types of activities students currently use AI. The participants were 2nd–4th years students (N=245) enrolled in teacher training program at Derzhavin Tambov State University (Russian Federation). KEYWORDS Research results. A structural model of pre-service teacher training based on AI technologies has been developed. It includes five blocks: a) specialized disciplines; b) methodological disciplines; c) psychological and pedagogical disciplines and “Digital department” courses; d) internship at school; e) research. Within a specific discipline of each block, certain AI tools are used to solve educational and research goals. The survey showed the degree of total vs. authorized use of AI by students in the educational process: 73.4% vs. 19.6% of respondents, respectively, use AI tools in the study of specialized disciplines, 22.3% vs. 22.3% – when studying methodological disciplines, 7.5% vs. 0.4% – when studying psychology and pedagogy, 15.2% vs. 15.2% – during internship at schools and 89.4% vs. 4.2% – in research. The level of authorized use of AI in the educational process was very low. Conclusion. The novelty of the study is in the development of a universal structural model of pre-service teacher training based on AI technologies. It can serve as a basis for the development of particular models of pre-service teacher training with one or several training profiles in pedagogical universities.

Challenges of artificial intelligence in medicine and dermatology

Artificial intelligence (AI) has become an increasingly important tool in agriculture, providing farmers with innovative solutions to improve productivity, efficiency, and sustainability. Using AI-powered tools such as drones, sensors, and machine learning algorithms, farmers can gather and analyze data about soil health, crop growth, and weather patterns to make informed decisions and optimize their operations. AI has the potential to transform agriculture by enabling more precise and targeted applications of fertilizers, pesticides, and other inputs, reducing waste, and increasing yields. It also has the potential to reduce the environmental impact of agriculture by minimizing the use of harmful chemicals and promoting sustainable farming practices. Additionally, AI can assist in the automation of tedious and repetitive tasks, freeing up farmers to focus on more strategic decision-making and higher-value activities. This can also help to address labor shortages in the agriculture sector, particularly in countries with aging populations or where traditional agriculture work is seen as less desirable. Overall, the use of AI in agriculture has the potential to revolutionize the industry, making it more efficient, sustainable, and profitable while also ensuring that we can continue to feed a growing global population in a responsible and sustainable manner. Keyword: Artificial Intelligence, AI in Agriculture, AI and Agriculture, Sustainable Farming, AI application in Agriculture

Integrating Artificial Intelligence (AI) into the process of agriculture is changing the way this business is taking place. The work presented here concentrates on designing and realizing an advanced AI driven smart irrigation system for tackling the main issues like water scarcity and inefficient irrigation practices that diminish crop productivity and waste resources. The proposed smart irrigation system will leverage machine learning algorithms, predictive analytics, as well as other AI technologies and real time data in the soil moisture, weather conditions, crop health and water usage. It will use predictive models and ensure that exact, timely irrigation is used, hemmed in to specific crop need requirements in order to minimize wasted water and increase water use efficiency for optimum crop growth. The evaluations of the system effectiveness are by simulations and field trial as part of the research. Water consumption, crop yield, and resource utilization efficiency will be analyzed to the utmost degree. The expected outcomes are that a significant reduction in water usage will be realized, development of best practices for smart irrigation in agriculture, and increased crop yields. The purpose of this study is to modernize existing irrigation practices in order to secure food and sustainable agriculture. The findings offer great insights and provide a generalisable framework to deploy the AI based smart irrigation systems to bring benefits to the farmers, policymakers and other stakeholders in the agricultural domain.

The impacts of Artificial Intelligence (AI) tools on various aspects of second language (L2) education have been widely reported in the literature. However, the socio-emotional dangers of using AI technologies from the perspective of English as a foreign language (EFL) teachers have remained uncharted. To address the gap, this study adopted a qualitative design and drew on control value theory (CVT) and social constructivism to unveil the socio-emotional risks of AI-mediated L2 education. A sample of 33 Chinese EFL teachers participated in online semi-structured interviews. The results of thematic analysis showed six dangers in using AI. Specifically, 'social isolation and competition', 'bias and academic dishonesty', and 'reduced teacher-student interaction and rapport' were the common social dangers of using AI by L2 educators. Concerning emotional dangers, it was found that AI tools may lead to 'classroom anxiety and stress' and 'feeling of passiveness and lack of autonomy', and 'creativity and criticality reduction'. The findings are discussed, and theoretical and practical implications are listed for EFL teachers, students, and trainers, as well as AI tools' developers, to inform them of the socio-emotional consequences of using AI.

Rapid growth of population, diminishing natural resources, climate change, shrinking agricultural lands and unstable markets are making the global food systems rather insecure. Therefore, modern agriculture and food systems should be more productive in terms of output, efficient in operation, resilient to climate change and sustainable for the future generations. As a result, the need of a technological transformation is greater than ever before. Being a recent advancement in computer sciences, Artificial Intelligence (AI) has the capacity to address the challenges of this new paradigm. Hence, understanding the importance and applicability of AI in agriculture and food sector could be vital in the journey towards achieving global food security. This review focuses on the AI applications in relation to four pillars of food security (food availability, food accessibility, food utilization and stability) as defined by FAO, in detail. The AI technologies are being applied worldwide in all four pillars of food security even though it has been one of the slower adopted technologies compared to the rest. Nevertheless, it warrants exploring the capabilities of AI and their current impact on the food systems. It is eminent that AI technology has a key role to play in the future agriculture sector. The worldwide AI in agriculture market is expected to reach USD 2,075 million by 2024. Present article reveals how AI technologies could benefit global agriculture and food sector, and examines the ways by which AI can address the prominent issues in Sri Lankan agriculture sector such as labor scarcity, misuse of agrochemicals and inefficient food value chains. Though there are still many challenges and gaps to be addressed at research, policy, administrative and farmer levels, the immense potential of this novel technology should be exploited fast in the journey towards global food security.

Purpose This service evaluation investigated frontline staff attitudes towards artificial intelligence (AI) implementation in NHS learning disabilities services to address critical knowledge gaps in workforce perspectives. Despite growing NHS AI adoption, systematic understanding of staff concerns remains limited, particularly regarding vulnerable populations who face heightened risks around consent capacity, communication barriers and potential exploitation. This study aims to capture staff perceptions of AI benefits, concerns and implementation needs to inform evidence-based, ethically-grounded Trust-level digital strategy that prioritises patient safety while supporting workforce readiness for technological change. Design/methodology/approach This mixed-methods service evaluation used an online questionnaire (n = 68) and semistructured focus group to explore staff attitudes in NHS specialist learning disabilities services. Participants included clinical professionals and nonclinical operational staff recruited through team meetings and electronic communications during July 2025–August 2025. The quantitative survey assessed AI familiarity using five-point scales, examining comfort levels, concerns regarding vulnerable patients, perceived benefits and training needs. A 30-minute focus group conducted via MS Teams explored clinical experiences, safeguarding concerns and implementation barriers. Descriptive statistics analysed quantitative responses while thematic analysis examined qualitative data. The study received Trust Practice Audit Implementation Group approval with voluntary participation and informed consent protocols. Findings Most staff (57%) demonstrated basic AI understanding, with 16% already using AI tools. Attitudes were predominantly cautious: 40% expressed neutrality and 35% voiced concerns about implementation with learning disabilities patients. Administrative efficiency emerged as the primary recognised benefit (62%), with limited support for clinical applications. Training priorities emphasised both AI fundamentals (47%) and ethical reassurance regarding bias and safety (47%). Qualitative analysis revealed four themes: heightened vulnerability concerns around patients’ capacity to distinguish AI from human interactions, significant safeguarding and exploitation risks, pragmatic engagement and training needs and governance. Originality/value This service evaluation addresses a critical gap by examining frontline workforce perspectives on AI implementation in an intellectual disabilities’ services, a population often marginalised in digital health transformation. It reveals unique vulnerabilities absent from general health-care AI literature, particularly around reality testing, consent capacity and exploitation risks through AI interactions. Unlike broader NHS AI surveys focusing on technical feasibility or public trust, this research captures specialist staff concerns about safeguarding implications and therapeutic relationship preservation. Findings provide evidence-based guidance for developing population-specific governance frameworks rather than applying standard protocols unsuitable for vulnerable groups. The equal emphasis on technical training and ethical reassurance offers practical insights for staged implementation strategies that balance innovation with patient safety.

Artificial Intelligence (AI) is emerging as a transformative force in the agricultural sector by improving productivity, optimizing resource utilization, and promoting sustainable farming practices. The integration of AI technologies such as machine learning, computer vision, robotics, Internet of Things (IoT), and big data analytics has enabled precision agriculture, smart irrigation systems, yield prediction models, pest and disease detection, and supply chain optimization. In countries like India, where agriculture remains a major source of livelihood, AI has the potential to address structural issues such as low productivity, climate variability, fragmented landholdings, and market inefficiencies. AI-powered platforms assist farmers in real-time decision-making by analyzing weather data, soil conditions, crop health, and market trends. Despite its transformative potential, the adoption of AI in agriculture faces several challenges including high initial costs, inadequate digital infrastructure, lack of technical awareness, data privacy concerns, and limited access to quality datasets. This research paper examines the role of AI in agriculture, evaluates its economic and social implications, and identifies the challenges associated with its implementation. The study concludes that effective policy support, public-private partnerships, capacity-building initiatives, and digital infrastructure development are crucial to maximizing the benefits of AI in agriculture.