AI is transforming medical practice and redefining the competencies that future healthcare professionals need to master. Despite international recommendations, the integration of AI into Medicine curricula in Spain had not been systematically evaluated until now. A cross-sectional study (July-September 2025) including Spanish universities offering the official degree in Medicine, according to the 'Register of Universities, Centers and Degrees (Registro de Universidades, Centros y Títulos RUCT)'. Curricula and publicly available institutional documentation were reviewed to identify courses and competencies related to AI in the 2025-2026 academic year. The analysis was performed using descriptive statistics. Of the 52 universities analyzed, ten (19.2%) offer specific AI courses, whereas 36 (69.2%) include no related content. Most of the identified courses are elective, with a credit load ranging from three to six ECTS, representing on average 1.17% of the total 360 credits of the degree. The University of Jaén is the only institution offering a compulsory course with AI content. The territorial analysis reveals marked disparities: Andalusia leads with 55.5% of its universities incorporating AI training, while several communities lack any initiative in this area. The integration of AI into the medical degree in Spain is incipient, fragmented, and uneven, with a low weight in ECTS. The limited training load and predominance of elective courses restrict the preparation of future physicians to practice in a healthcare environment increasingly mediated by AI. The findings support the establishment of minimum standards and national monitoring of indicators.

Phishing, whether through email, SMS, or malicious websites, poses a major threat to organizations by using social engineering to trick users into revealing sensitive information. It not only compromises company's data security but also incurs significant financial losses. In this paper, we investigate whether the remarkable performance of Large Language Models (LLMs) can be leveraged for particular task like text classification, particularly detecting malicious content and compare its results with state-of-the-art Deberta V3 (DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing) model. We systematically assess the potential and limitations of both approaches using comprehensive public datasets comprising diverse data sources such as email, HTML, URL, SMS, and synthetic data generation. Additionally, we demonstrate how LLMs can generate convincing phishing emails, making it harder to spot scams and evaluate the performance of both models in this context. Our study delves further into the challenges encountered by DeBERTa V3 during its training phases, fine-tuning methodology and transfer learning processes. Similarly, we examine the challenges associated with LLMs and assess their respective performance. Among our experimental approaches, the transformer-based DeBERTa method emerged as the most effective, achieving a test dataset (HuggingFace phishing dataset) recall (sensitivity) of 95.17% closely followed by GPT-4 providing a recall of 91.04%. We performed additional experiments with other datasets on the trained DeBERTa V3 model and LLMs like GPT 4 and Gemini 1.5. Based on our findings, we provide valuable insights into the effectiveness and robustness of these advanced language models, offering a detailed comparative analysis that can inform future research efforts in strengthening cybersecurity measures for detecting and mitigating phishing threats.

The rapid progress in the field of natural language processing (NLP) systems and the expansion of large language models (LLMs) have opened up numerous opportunities in the field of education and instructional methods. These advancements offer the potential for tailored learning experiences and immediate feedback, all delivered through accessible and cost-effective services. One notable application area for this technological advancement is in the realm of solving mathematical problems. Mathematical problem-solving not only requires the ability to decipher complex problem statements but also the skill to perform precise arithmetic calculations at each step of the problem-solving process. However, the evaluation of the arithmetic capabilities of large language models remains an area that has received relatively little attention. In response, we introduce an extensive mathematics dataset called "MathQuest" sourced from the 11th and 12th standard Mathematics NCERT textbooks. This dataset encompasses mathematical challenges of varying complexity and covers a wide range of mathematical concepts. Utilizing this dataset, we conduct fine-tuning experiments with three prominent LLMs: LLaMA-2, WizardMath, and MAmmoTH. These fine-tuned models serve as benchmarks for evaluating their performance on our dataset. Our experiments reveal that among the three models, MAmmoTH-13B emerges as the most proficient, achieving the highest level of competence in solving the presented mathematical problems. Consequently, MAmmoTH-13B establishes itself as a robust and dependable benchmark for addressing NCERT mathematics problems.

Before anyone knew big data existed, it had already taken over the globe. Big data had amassed an enormous amount of stored information by the time the term was coined. If properly examined, it might provide insightful knowledge about the sector to which that particular data belonged. The task of sorting through all of that data, parsing it (turning it into a format more easily understood by a computer), and analyzing it to enhance commercial decision-making processes was quickly found to be too much for human minds to handle. Writing algorithms with artificial intelligence would be necessary to complete the challenging task of extracting knowledge from complex data. As businesses expand their big data and artificial intelligence capabilities in the upcoming years, data professionals and individuals with a master's in business analytics or data analytics are anticipated to be in high demand.

In recent years, the emergence of big data and artificial intelligence technology has made Internet finance a brand new development model in the new era. As an emerging financial format, Internet finance plays an important role in providing people with convenient and efficient services. However, due to the late start in this regard and the imperfect related policies and regulations, China is currently still in the development stage, resulting in its risk management system not being mature and complete and lacking uniformity. There are also many regulatory deficiencies, which are not conducive to the healthy, stable, and continuous growth and progress of Internet finance. In the new situation, it is of great significance to strengthen the research on the security of China's Internet finance. Therefore, how to effectively manage Internet financial risks in the context of big data and artificial intelligence has become a topic of research. This study uses questionnaire analysis and data analysis to understand the distribution of risks and the importance of risk response measures through questionnaire surveys. According to the survey results, in the eyes of most interviewees, the ratios of operational risk, credit risk, platform operation risk, and lack of law and reputation risk in high-risk areas are 0.15, 0.3, 0.29, 0.51, and 0.1, respectively. The risks of these first-level indicators need to be particularly important and need to be effectively avoided to manage Internet financial risks. In addition, the most important risk response measures are the construction of information security, followed by the improvement of relevant laws and regulations. In their view, only from these aspects can we effectively control risks internally and externally.

A year ago, the economic situation in LatAm seemed dire. But in the second half of 2021 and well into 2022, the business and economic outlook for Latin America has not only bounced back, but in many areas is surging forward. The picture, of course, is uneven. Exciting new technologies are being adapted swiftly, while sharp, sudden increases in some consumer prices reflect now-familiar supply chain constraints. Meanwhile, the region's rich natural resources and the upside ripple effects of high energy prices and commodity exports – thanks to some nearshoring efforts – are enabling continuity and growth, offsetting some of the inflationary pressures we are seeing elsewhere.

AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make'intelligent' machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large datasets to mine information from them. BD technology includes capturing and storing the data, and then analyzing it to make strategic decisions and improve business outcomes. Most companies deploy bigdata and AI in silos to structure their existing data sets and to develop machines which can think for themselves.

By combining artificial intelligence (AI) and big data, organizations can see and predict upcoming trends in key sectors including business, technology, finance and healthcare. AI is the simulation of human intelligence by computers. By applying machine learning algorithms, we can make'intelligent' machines, which can employ cognitive reasoning to make decisions based on the data fed to them. Big Data, on the other hand, is a blanket term for computational strategies and techniques applied to large sets of data to mine information from them. Big data technology includes capturing and storing the data, and then analyzing data to make strategic decisions and improve business outcomes. Most companies deploy big data and AI in silos to structure their existing data sets and to develop machines which can think for themselves.

These iterative design modifications and rapid prototyping are essential and have to be planned early and not during large-scale trials to avoid undermining the whole project. This step might be compared to a phase 1 or 2 trial for drug development. However, before using the new AI tool at the bedside, we need to have a large trial demonstrating an efficacy on valuable outcomes, as we are used to with phase 3 trials before adopting a new therapeutical approach. However, this stage cannot be escaped. Large-scale clinical trials are complicated and expensive activities that require meticulous preparation.

Undoubtedly, we all are accommodated with AI-driven technology keeping in view evolving advancements with rapid pace. Internet access and adoption are progressing swiftly and the number of online buyers is increasing with the speed of light every year. E-commerce is becoming the most famous and stepped up their game especially during the pandemic of COVID-19. It is estimated that 1.92 billion people are purchasing goods and services online considering technological advancements and also avoiding physical contact. Let's have a deep insight into how artificial intelligence is merged with big data for customers' due-diligence, risk assessment, and to leverage the power of data accurately and efficiently.