Artificial intelligence: Its role in dermatopathology.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Artificial intelligence (AI) is not a new idea or field of research. However, recent advancements in computing technology as well as increasing worldwide experience in applying AI to various fields have enabled us to hope that applying it to the medical practice could improve outcomes. The aim of the article was to evaluate potential applications of artificial intelligence (AI) in the clinical practice of gastroenterology. AI algorithms can offer many benefits over the traditional diagnostic, prognostic, therapeutic scores, or non-AI enhanced procedures that we perform today. It can very easily process complex mathematical data, which would allow to account for many parameters and with highly sophisticated formulas to determine a conclusion, to a degree that would be impractical or impossible for a human to do alone in the clinical practice. This could allow for significantly more individualized treatments for each patient. As more data becomes available, it can be fed into the algorithm, which allows it to change based on the new data. However, we must be aware that most studies performed today are still early retrospective studies, and their utility still needs to be evaluated in future large scale prospective studies. Apart from the medical challenges, it must be noted that the impact of large-scale implementation of artificial intelligence will have challenges related to ethical and financial aspects as well.

This research aims to develop artificial intelligence (AI) algorithms in the context of facial recognition with a focus on increasing accuracy in difficult environmental conditions. Although facial recognition technology has made great progress, challenges such as poor lighting, variations in facial expressions, and head rotation are still problems that must be overcome. The research methodology involved collecting a wide dataset covering a wide variety of faces under various environmental conditions. This data is then processed and its features are extracted using computer image processing techniques. Furthermore, several deep neural network architectures, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were developed, trained, and evaluated for face recognition tasks. The expected result is the development of an AI algorithm that is able to overcome challenges in facial recognition with higher accuracy than existing methods. In particular, significant improvements in facial recognition accuracy are expected especially under low lighting conditions and variations in facial expressions. This research has a major impact in a variety of security applications, such as border surveillance, building access control, and corporate security. With higher facial recognition accuracy, security risks can be significantly reduced, resulting in safer and more efficient security solutions. In conclusion, this research aims to bring innovation in facial recognition technology through advanced AI approaches, with the potential to improve security in various contexts.

Pneumonia is a highly dangerous state that poses serious risks to the health of a patient. In contrast to common pneumonia, lung disease COVID-19 causes a large number of lethal outcomes. The pneumonia of a viral etiology caused by the RNA virus SARS-CoV-2 is visually hardly distinguishable from the bacterial pneumonia or inflammation caused by other viral infections. Now, COVID-19 can be diagnosed using PCR tests or X-rays of the thoracic cage. However, the results of a molecular study take a long time to prepare. In contrast, the radiological images of the thoracic cage can be obtained immediately after the radiological study. Although there exist guiding principles which help radiologists to differentiate COVID-19 from other types of infections, their assessments differ. In addition, doctors who are not radiologists can be assisted in better locating the disease, for instance, by a bounding box. Development of precise computer methods based on artificial intelligence can help medical workers in quickly determining the type of pneumonia and detecting the loci of inflammation. In this study a package of methods is developed to determine the type of pneumonia and detect the ground-glass loci using the appropriate architectures of neural networks, loss functions, augmentations at the training data generation stage, test time augmentation, and computer vision model ensembles. This task is successfully solved in the SIIM-FISABIO-RSNA COVID-19 Detection competition [17] and the proposed algorithm is in the top 10% of the best solutions.

Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

Fatty liver disease, also known as hepatic steatosis, poses a significant global health concern due to the excessive accumulation of fat within the liver. If left untreated, this condition can give rise to severe complications. Recent advances in artificial intelligence (AI, specifically AI-based ultrasound imaging) offer promising tools for diagnosing this condition. This review endeavors to explore the current state of research concerning AI's role in diagnosing fatty liver disease, with a particular emphasis on imaging methods. To this end, a comprehensive search was conducted across electronic databases, including Google Scholar and Embase, to identify relevant studies published between January 2010 and May 2023, with keywords such as "fatty liver disease" and "artificial intelligence (AI)." The article selection process adhered to the PRISMA framework, ultimately resulting in the inclusion of 13 studies. These studies leveraged AI-assisted ultrasound due to its accessibility and cost-effectiveness, and they hailed from diverse countries, including India, China, Singapore, the United States, Egypt, Iran, Poland, Malaysia, and Korea. These studies employed a variety of AI classifiers, such as support vector machines, convolutional neural networks, multilayer perceptron, fuzzy Sugeno, and probabilistic neural networks, all of which demonstrated a remarkable level of precision. Notably, one study even achieved a diagnostic accuracy rate of 100%, underscoring AI's potential in diagnosing fatty liver disease. Nevertheless, the review acknowledged certain limitations within the included studies, with the majority featuring relatively small sample sizes, often encompassing fewer than 100 patients. Additionally, the variability in AI algorithms and imaging techniques added complexity to the comparative analysis. In conclusion, this review emphasizes the potential of AI in enhancing the diagnosis and management of fatty liver disease through advanced imaging techniques. Future research endeavors should prioritize the execution of large-scale studies that employ standardized AI algorithms and imaging techniques to validate AI's utility in diagnosing this prevalent health condition.

BackgroundDespite the extensive hopes and expectations for value creation resulting from the implementation of artificial intelligence (AI) applications in healthcare, research has predominantly been technology-centric rather than focused on the many changes that are required in clinical practice for the technology to be successfully implemented. The importance of leaders in the successful implementation of innovations in healthcare is well recognised, yet their perspectives on the specific innovation characteristics of AI are still unknown. The aim of this study was therefore to explore the perceptions of leaders in healthcare concerning the innovation characteristics of AI intended to be implemented into their organisation.MethodsThe study had a deductive qualitative design, using constructs from the innovation domain in the Consolidated Framework for Implementation Research (CFIR). Interviews were conducted with 26 leaders in healthcare.ResultsParticipants perceived that AI could provide relative advantages when it came to care management, supporting clinical decisions, and the early detection of disease and risk of disease. The development of AI in the organisation itself was perceived as the main current innovation source. The evidence base behind AI technology was questioned, in relation to its transparency, potential quality improvement, and safety risks. Although the participants acknowledged AI to be superior to human action in terms of effectiveness and precision in some situations, they also expressed uncertainty about the adaptability and trialability of AI. Complexities such as the characteristics of the technology, the lack of conceptual consensus about AI, and the need for a variety of implementation strategies to accomplish transformative change in practice were identified, as were uncertainties about the costs involved in AI implementation.ConclusionHealthcare leaders not only saw potential in the technology and its use in practice, but also felt that AI’s opacity limits its evidence strength and that complexities in relation to AI itself and its implementation influence its current use in healthcare practice. More research is needed based on actual experiences using AI applications in real-world situations and their impact on clinical practice. New theories, models, and frameworks may need to be developed to meet challenges related to the implementation of AI in healthcare.

cancer remains a leading cause of morbidity and mortality worldwide, with nearly 20 million new cases and 9.7 million deaths reported in 2022. The increasing burden of cancer, driven by population growth and aging, necessitates innovative solutions to improve diagnosis, prognosis, and treatment outcomes. Artificial Intelligence has emerged as a transformative tool in oncology, offering significant potential in cancer detection, diagnosis, and personalized treatment strategies. This review explores the real-world applications of Artificial Intelligence in oncology, focusing on lung cancer and breast cancer, two of the most prevalent and deadly cancers globally. Artificial Intelligence-driven technologies, particularly in imaging, pathology, and genomics, have demonstrated remarkable success in enhancing early detection, diagnostic accuracy, and treatment planning. In lung cancer, Artificial Intelligence-powered imaging tools, such as deep learning models, have shown high sensitivity and specificity in detecting small pulmonary nodules, often missed by traditional methods. Similarly, in breast cancer, Artificial Intelligence has proven effective in mammography interpretation, reducing false positives and false negatives, and alleviating the workload of radiologists. Despite its promising potential, the integration of Artificial Intelligence into clinical practice faces several challenges, including issues related to data quality, algorithmic biases, and ethical considerations. The "black box" nature of many Artificial Intelligence systems poses a significant barrier to clinical acceptance, highlighting the need for explainable Artificial Intelligence to provide transparent and interpretable decision-making processes. Furthermore, the successful implementation of Artificial Intelligence in oncology requires robust regulatory frameworks and standardized protocols to ensure patient safety and data security. This review underscores the transformative potential of Artificial Intelligence in revolutionizing cancer care, emphasizing the importance of addressing key challenges to harness its full potential. By enhancing early detection, reducing diagnostic errors, and enabling personalized treatment strategies, Artificial Intelligence has the potential to significantly improve patient outcomes and reduce the global burden of cancer. However, its successful integration into clinical practice will depend on interdisciplinary collaboration, ethical considerations, and a commitment to responsible implementation.

Real-World Surveillance of FDA-Cleared Artificial Intelligence Models: Rationale and Logistics.

BackgroundThe increasing development of artificial intelligence (AI) systems in medicine driven by researchers and entrepreneurs goes along with enormous expectations for medical care advancement. AI might change the clinical practice of physicians from almost all medical disciplines and in most areas of health care. While expectations for AI in medicine are high, practical implementations of AI for clinical practice are still scarce in Germany. Moreover, physicians’ requirements and expectations of AI in medicine and their opinion on the usage of anonymized patient data for clinical and biomedical research have not been investigated widely in German university hospitals.ObjectiveThis study aimed to evaluate physicians’ requirements and expectations of AI in medicine and their opinion on the secondary usage of patient data for (bio)medical research (eg, for the development of machine learning algorithms) in university hospitals in Germany.MethodsA web-based survey was conducted addressing physicians of all medical disciplines in 8 German university hospitals. Answers were given using Likert scales and general demographic responses. Physicians were asked to participate locally via email in the respective hospitals.ResultsThe online survey was completed by 303 physicians (female: 121/303, 39.9%; male: 173/303, 57.1%; no response: 9/303, 3.0%) from a wide range of medical disciplines and work experience levels. Most respondents either had a positive (130/303, 42.9%) or a very positive attitude (82/303, 27.1%) towards AI in medicine. There was a significant association between the personal rating of AI in medicine and the self-reported technical affinity level (H4=48.3, P<.001). A vast majority of physicians expected the future of medicine to be a mix of human and artificial intelligence (273/303, 90.1%) but also requested a scientific evaluation before the routine implementation of AI-based systems (276/303, 91.1%). Physicians were most optimistic that AI applications would identify drug interactions (280/303, 92.4%) to improve patient care substantially but were quite reserved regarding AI-supported diagnosis of psychiatric diseases (62/303, 20.5%). Of the respondents, 82.5% (250/303) agreed that there should be open access to anonymized patient databases for medical and biomedical research.ConclusionsPhysicians in stationary patient care in German university hospitals show a generally positive attitude towards using most AI applications in medicine. Along with this optimism comes several expectations and hopes that AI will assist physicians in clinical decision making. Especially in fields of medicine where huge amounts of data are processed (eg, imaging procedures in radiology and pathology) or data are collected continuously (eg, cardiology and intensive care medicine), physicians’ expectations of AI to substantially improve future patient care are high. In the study, the greatest potential was seen in the application of AI for the identification of drug interactions, assumedly due to the rising complexity of drug administration to polymorbid, polypharmacy patients. However, for the practical usage of AI in health care, regulatory and organizational challenges still have to be mastered.

The article explores the prospects for the legal regulation of the application of artificial intelligence in the justice system of Ukraine. The study begins with an analysis of the Concept for the Development of Artificial Intelligence in Ukraine, approved by the Order of the Cabinet of Ministers of Ukraine dated 2 December 2020. It also explores the new provisions of the Code of Judicial Ethics, as amended by the Decision of the Congress of Judges as of 18 September 2024, regarding the use of artificial intelligence technologies by judges. The article notes the absence of a special law on the legal regulation of artificial intelligence in Ukraine. Reference is made to the ‘White Paper on Artificial Intelligence Regulation in Ukraine: Vision of the Ministry of Digital Transformation of Ukraine’ (version for consultation, June 2024), which provides for two stages in the introduction of artificial intelligence regulation in Ukraine: stage 1 (preparatory) and stage 2 (adoption of a law analogous to the EU Regulation on Artificial Intelligence, the Artificial Intelligence Act). The article also refers to sectoral recommendations developed for the use of artificial intelligence in various fields, in particular, “Recommendations on the Responsible Use of Artificial Intelligence for Lawyers”, “Recommendations on the Responsible Use of Artificial Intelligence by Public Servants”, “Recommendations on the Responsible Use of AI: Intellectual Property Rights Issues”, “Recommendations on the Responsible Development of Systems Using Artificial Intelligence Technologies”, “Recommendations on the Responsible Implementation and Use of Artificial Intelligence Technologies in Higher Education Institutions”, “Instructional and Methodological Recommendations on the Introduction and Use of Artificial Intelligence Technologies in General Secondary Education Institutions”, “Recommendations on the Responsible Use of Artificial Intelligence in the Field of Advertisement and Marketing Communications”, “Recommendations on the Responsible Use of Artificial Intelligence in the Media”. The provisions of the Concept of the UJICS, approved by Order No. 178 of the State Judicial Administration of Ukraine dated 30 April 2025, regarding the use of artificial intelligence in the functioning of the UJICS (Unified Judicial Information and Communication System) subsystems, are analyzed in detail. The importance of developing and adopting the recommendations (guidelines) or a model regulation on the use of artificial intelligence in the Ukrainian justice system, particularly in courts, is emphasized. The prospects and necessity of developing and adopting a special law on the regulation of artificial intelligence in Ukraine, analogous to the EU Regulation on Artificial Intelligence (the Artificial Intelligence Act), is noted. Such a law should, among other things, include provisions on the use of artificial intelligence in the justice system. In addition, the importance of guiding, in particular by the provisions of the General Data Protection Regulation when developing provisions of Ukrainian legislation on the use of artificial intelligence in Ukraine’s justice system is noted. It is concluded in the article that the effective implementation of artificial intelligence in the justice system of Ukraine should be based on an appropriate regulatory framework that takes into account both the opportunities and risks associated with the use of artificial intelligence.

Globalisation of the strategy for the development and implementation of artificial intelligence (AI) in Ukraine and/or deepening the direction in nominal areas of social life, i.e. individuals in society, respectively, in the fields of art, design, medicine, commercial activity, science and education, banking and deep military and national strategy and national security in cadence formats such as ChatGPT, i.e: • Artificial intelligence – AI; • Artificial Neural Networks – ANN – computing systems. The dynamic position in the context of the emergence of artificial intelligence (AI) and its further application in the educational process in relation to the first stage, i.e. the species concept on the part of educators and students of education from among the convicts in the status of a special contingent, respectively, was remade in relation to the nominal field of vocational training in the context of penitentiary education, In other words, artificial intelligence (AI) in the form of a contact line requested a penitentiary trajectory for the format of the social and creative model teacher-education student from among prisoners in the status of a special contingent. Accordingly, the special contingent in the form of students from the prison population in the status of a special contingent needs to be fluent in digital technologies in accordance with the requirements of modern society, to improve and enhance the nominal abilities that will be needed for further employment in the digital world. The motivation of the present regarding the project perspective of artificial intelligence (AI) in penitentiary education in the status of a special contingent, that is, the involvement of the social and creative model of teacher-educator from among the convicts of imprisonment in the status of a special contingent is characterized by the intense development of artificial intelligence (AI) technologies and the introduction of. The generation of the relationship between artificial intelligence (AI) and education goes beyond the application of artificial intelligence (AI) during the acquisition of theoretical training, i.e., taking into account the study of artificial intelligence (AI) technologies and the professional training of students from among prisoners of imprisonment in the status of a special contingent within the framework of penitentiary education.

The author of the article considers the main directions of application of artificial intelligence in financial management. The article reviews the main scientific sources and positions of practitioners on the strategic prospects of applying artificial intelligence tools in financial management. The research is based on the methodology of systematic review and synthesis of scientific sources and industry reports, which is the most justified for this area of ​​research. The article analyzes key indicators of the artificial intelligence market in the financial sector according to data from leading analytical companies, which demonstrates the impressive growth dynamics of the global artificial intelligence market by 2030-2034. The author systematizes the main challenges faced by the implementation of artificial intelligence in financial management, such as: ethical and regulatory lags, scaling problems, environmental impact and data bias. Based on the analysis of market trends, the evolutionary path of using artificial intelligence tools has been determined, which indicates the growth of market maturity, which is moving from the stage of "technology for the sake of technology" to the stage of "solutions for the sake of business tasks". The author identified strategic vectors for the implementation of artificial intelligence technology in financial management, among which three main ones were conceptually identified: increasing operational efficiency, risk management and cybersecurity, personalization and customer experience. Specific areas of application in which the implementation of artificial intelligence has the most significant results in financial management have also been systematized: fraud detection, creditworthiness assessment, forecasting needs, democratization of financial services, development of virtual assistants, etc. The article pays attention to comparing the main models of generative artificial intelligence and their possibilities of use as tools in financial management, in particular, advantages, limitations and the most optimal type of use.

Artificial Intelligence: The Future of Maxillofacial Prognosis and Diagnosis?

The development of computer technology is becoming more and more mature. The application of artificial intelligence to the medical field has made important contributions to medical diagnosis and auxiliary detection. Using the powerful computing power of computers to replace humans in the automatic diagnosis of complex diseases has been enthusiastic by the majority of scientific researchers. In the current medical imaging diagnosis system, the location of the lesion is mainly found by observing the two‐dimensional medical image sequence, and the signs of biological information cannot be accurately displayed. Through medical image analysis technology, two‐dimensional images will be divided into three‐dimensional models after image segmentation, image recognition, and three‐dimensional imaging. In this way, it seems that the doctor can “hold the image volume data,” which can greatly improve the scientific and accurate diagnosis. Sex—this article applies artificial intelligence to medical imaging, combined with embedded technology, RFID technology and signal processing technology, and applies the new coronavirus pneumonia image to the artificial intelligence environment after processing, assisting doctors in diagnosis of the disease, and providing relevant information about patients record and manage the diagnosis and diagnosis, save and accumulate the experience and knowledge of famous doctors through the expert system, and then perform corresponding operations and analysis. Through the medical image intelligent analysis system, the safety risk of medical imaging artificial intelligence diagnosis is reduced from 81% to 11%, which greatly reduces the hidden safety hazards for doctors and patients, reduces the workload of doctors, and also reduces the cost of medical care by 79% It is reduced to 20%, which reduces the waiting time of patients and achieves the purpose of improving the accuracy of diagnosis.