Artificial intelligence (AI) is bringing changes to radiology and radiologic technology, enabling the development of programs and algorithms that facilitate diagnosis and decisions. It is essential to understand how AI can improve patient outcomes, increase the efficiency of investigations and reduce costs. Machine and deep learning have proven to be extremely useful in the detection and characterization of lesions, improving routine imaging techniques and facilitating the work of radiologists by reducing workload and improving the quality of reporting. The practical application of artificial intelligence in radiology and radiologic technology has been slowed down by the lack of integrated solutions and well-structured data archives, as well as challenges such as non-transparency of decision-making systems and large amounts of quality data needed to train artificial intelligence models. There are concerns about the potential impact of AI on the work of radiologists and radiologic technologists, which may hinder the development and implementation of this technology. Textual data derived from image reports can provide valuable healthcare insights. Natural language processing (NLP), a subset of artificial intelligence, offers promising solutions for handling unstructured text in these reports, opening a new era in extracting information from medical images and related reports. Due to the challenges in training experts for the application of AI in healthcare, a multidisciplinary approach will have to be used and investments in collaboration and education will have to be made. There are outstanding issues of liability and regulation regarding data storage and privacy, particularly in the case of cloud storage. The concern of the workforce and their lack of education about artificial intelligence represents an obstacle to its adoption, but also offers an opportunity for the technological advancement of the profession.

ABSTRACT This study investigates the effects of government venture capitals (GVCs) backing on the productivity of startups compared to private venture capitals (PVCs). Based on a sample of 1,149 artificial intelligence startups in South Korea, we find that startups backed by GVCs show better productivity than those backed by PVCs. In examining the underlying mechanism of such higher performance by GVC-backing, we find that GVC-backed startups are more likely to acquire proprietary data and join R&D testbed programmes, thus leading to better productivity compared with PVC-backed startups. We interpret our findings as evidence of the positive abilities of GVCs as intermediaries for their portfolio startups.

<p style="text-align:justify">This article investigates artificial intelligence (AI) implementation in higher education (HE) from experts' perspectives. It emphasises the view of AI's involvement in administrative activities in higher education, experts' opinions concerning the influence of the incorporation of AI on learning and teaching, and experts' views on applying AI specifically to assessment, academic integrity, and ethical considerations. The study used a qualitative method based on an unstructured qualitative interview with open-ended questions. The participants were thirteen individuals currently involved with higher education institutions and had various talents related to AI and education. Findings stress that implementing AI technology in administrative roles within higher education institutions is essential since it cuts costs, addresses problems efficiently and effectively, and saves time. The findings also revealed that AI plays a vital role in learning and teaching by speeding up the learning process, engaging learners and tutors, and personalising learning depending on the learner's needs within an entirely intelligent environment. AI can produce an accurate, objective, and suitable level of assessment. AI aids students in developing a stronger sense of integrity in their academic work by guiding them through AI-powered applications. AI must adhere to ethical laws and policies, ensuring its potential negative aspects are not overlooked or left unchecked.</p>

ABSTRACT In artificial intelligence, decision-making is a widely studied field, and distance measure plays a crucial role in decision science. Several distance measures for Intuitionistic Fuzzy sets (IFSs) have been developed to handle uncertain situations. However, some of these measures lack properties of distance measures or produce unreasonable results. This study introduces a new distance measure between IFSs that satisfies the necessary properties and resolves counter-intuitive cases. The proposed distance measure is verified to be more reasonable through numerical examples, and it is applied to pattern recognition and medical diagnosis problems.

Colorectal cancer (CRC) is a leading global health concern, and early identification and precise prognosis play a vital role in enhancing patient results. Endoscopy is a minimally invasive imaging technique that is crucial for the screening, diagnosis, and treatment of CRC. This editorial discusses the importance of advances in endoscopic techniques, the integration of artificial intelligence, and the potential of novel technologies in enhancing the diagnosis and management of CRC.

In the 19th century, the sphere of interest of the science of “ecology” was formulated. Over the course of a century and a half, the semantics of its tasks has undergone significant changes. However, the original definition of ecology given by E.G. Haeckel, as a science about the interaction of living and inanimate nature, has again become relevant with the technological development of mankind. This development led to the emergence of an alternative habitat - the technosphere. The technosphere, within the framework of environmental ideas, is considered as a formation deeply hostile to nature in its essence. Nature is considered as a certain ideal that creates favorable conditions for the existence of living organisms in it. The experience of natural disasters and cataclysms, accompanied by the mass death of living organisms, indicates that this is an idealized idea. The existence of modern man outside the technosphere is practically impossible. The techno-sphere takes on the task of providing humanity with protection from pathogens. An example of such control is the use of DDT against vectors of malaria and other diseases. The technosphere solves problems in relation to humanity that are similar to providing the body with food, solving the problems of energy supply and movement over various distances – both people and cargo. The technosphere is evolving, using the achievements of developing sciences and technologies, reducing the specific impact on the natural environment while meeting the needs of the individual. For the further development of human ecology at the intersection of natural and artificial, the solution of traditional management problems is required: strict control of raw materials, technologies and products used, and their rational distribution. This is important since the human population is steadily growing and a significant part of the planet’s population finds itself in conditions of food and energy shortages, while the other part has an abundance of resources.