State Of Artificial Intelligence Research Articles

The State of Artificial Intelligence in Pediatric Surgery: A Systematic Review

BackgroundArtificial intelligence (AI) has been recently shown to improve clinical workflows and outcomes - yet its potential in pediatric surgery remains largely unexplored. This systematic review details the use of AI in pediatric surgery. MethodsNine medical databases were searched from inception until January 2023, identifying articles focused on AI in pediatric surgery. Two authors reviewed full texts of eligible articles. Studies were included if they were original investigations on the development, validation, or clinical application of AI models for pediatric health conditions primarily managed surgically. Studies were excluded if they were not peer-reviewed, were review articles, editorials, commentaries, or case reports, did not focus on pediatric surgical conditions, or did not employ at least one AI model. Extracted data included study characteristics, clinical specialty, AI method and algorithm type, AI model (algorithm) role and performance metrics, key results, interpretability, validation, and risk of bias using PROBAST and QUADAS-2. ResultsAuthors screened 8178 articles and included 112. Half of the studies (50%) reported predictive models (for adverse events [25%], surgical outcomes [16%] and survival [9%]), followed by diagnostic (29%) and decision support models (21%). Neural networks (44%) and ensemble learners (36%) were the most commonly used AI methods across application domains. The main pediatric surgical subspecialties represented across all models were general surgery (31%) and neurosurgery (25%). Forty-four percent of models were interpretable, and 6% were both interpretable and externally validated. Forty percent of models had a high risk of bias, and concerns over applicability were identified in 7%. ConclusionsWhile AI has wide potential clinical applications in pediatric surgery, very few published AI algorithms were externally validated, interpretable, and unbiased. Future research needs to focus on developing AI models which are prospectively validated and ultimately integrated into clinical workflows. Level of Evidence2A.

The State of Artificial Intelligence in Skin Cancer Publications.

Artificial intelligence (AI) in skin cancer is a promising research field to assist physicians and to provide support to patients remotely. Physicians' awareness to new developments in AI research is important to define the best practices and scope of integrating AI-enabled technologies within a clinical setting. To analyze the characteristics and trends of AI skin cancer publications from dermatology journals. AI skin cancer publications were retrieved in June 2022 from the Web of Science. Publications were screened by title, abstract, and keywords to assess eligibility. Publications were fully reviewed. Publications were divided between nonmelanoma skin cancer (NMSC), melanoma, and skin cancer studies. The primary measured outcome was the number of citations. The secondary measured outcomes were articles' general characteristics and features related to AI. A total of 168 articles were included: 25 on NMSC, 77 on melanoma, and 66 on skin cancer. The most common types of skin cancers were melanoma (134, 79.8%), basal cell carcinoma (61, 36.3%), and squamous cell carcinoma (45, 26.9%). All articles were published between 2000 and 2022, with 49 (29.2%) of them being published in 2021. Original studies that developed or assessed an algorithm predominantly used supervised learning (66, 97.0%) and deep neural networks (42, 67.7%). The most used imaging modalities were standard dermoscopy (76, 45.2%) and clinical images (39, 23.2%). Most publications focused on developing or assessing screening technologies with mainly deep neural network algorithms. This indicates the eminent need for dermatologists to label or annotate images used by novel AI systems.

The State of Artificial Intelligence in Precision Oncology: An Interview with Eric Topol

The State of Artificial Intelligence in Precision Oncology: An Interview with Eric Topol

The state of artificial intelligence in medical research: A survey of corresponding authors from top medical journals.

Natural Language Processing (NLP) is a subset of artificial intelligence that enables machines to understand and respond to human language through Large Language Models (LLMs)‥ These models have diverse applications in fields such as medical research, scientific writing, and publishing, but concerns such as hallucination, ethical issues, bias, and cybersecurity need to be addressed. To understand the scientific community's understanding and perspective on the role of Artificial Intelligence (AI) in research and authorship, a survey was designed for corresponding authors in top medical journals. An online survey was conducted from July 13th, 2023, to September 1st, 2023, using the SurveyMonkey web instrument, and the population of interest were corresponding authors who published in 2022 in the 15 highest-impact medical journals, as ranked by the Journal Citation Report. The survey link has been sent to all the identified corresponding authors by mail. A total of 266 authors answered, and 236 entered the final analysis. Most of the researchers (40.6%) reported having moderate familiarity with artificial intelligence, while a minority (4.4%) had no associated knowledge. Furthermore, the vast majority (79.0%) believe that artificial intelligence will play a major role in the future of research. Of note, no correlation between academic metrics and artificial intelligence knowledge or confidence was found. The results indicate that although researchers have varying degrees of familiarity with artificial intelligence, its use in scientific research is still in its early phases. Despite lacking formal AI training, many scholars publishing in high-impact journals have started integrating such technologies into their projects, including rephrasing, translation, and proofreading tasks. Efforts should focus on providing training for their effective use, establishing guidelines by journal editors, and creating software applications that bundle multiple integrated tools into a single platform.

The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector

The State of Artificial Intelligence and its Prospects in Pakistan's Medical Sector

The State of Artificial Intelligence and Machine Learning for Diagnosing, Differentiating and Predicting Prognosis in Dementia

The State of Artificial Intelligence and Machine Learning for Diagnosing, Differentiating and Predicting Prognosis in Dementia

The State of Artificial Intelligence in Epilepsy

The State of Artificial Intelligence in Epilepsy

Recent Advances in Melanoma Diagnosis and Prognosis Using Machine Learning Methods.

The purpose was to summarize the current role and state of artificial intelligence and machine learning in the diagnosis and management of melanoma. Deep learning algorithms can identify melanoma from clinical, dermoscopic, and whole slide pathology images with increasing accuracy. Efforts to provide more granular annotation to datasets and to identify new predictors are ongoing. There have been many incremental advances in both melanoma diagnostics and prognostic tools using artificial intelligence and machine learning. Higher quality input data will further improve these models' capabilities.

First Global Report on the State of Artificial Intelligence in Legal Practice

First Global Report on the State of Artificial Intelligence in Legal Practice

The state of artificial intelligence in pediatric urology

Review Context and ObjectiveArtificial intelligence (AI) and machine learning (ML) offer new tools to advance care in pediatric urology. While there has been interest in developing ML models in the field, there has not been a synthesis of the literature. Here, we aim to highlight the important work being done in bringing these advanced tools into pediatric urology and review their objectives, model performance, and usability.Evidence AcquisitionWe performed a comprehensive, non-systematic search on MEDLINE and EMBASE and combined these with hand-searches of publications which utilize ML to predict outcomes in pediatric urology. Each article was extracted for objectives, AI approach, data sources, model inputs and outputs, model performance, and usability. This information was qualitatively synthesized.Evidence SynthesisA total of 27 unique ML models were found in the literature. Vesicoureteral reflux, hydronephrosis, pyeloplasty, and posterior urethral valves were the primary topics. Most models highlight strong performance within institutional datasets and accurately predicted clinically relevant outcomes. Model validity was often limited without external validation, and usability was hampered by model deployment and interpretability.DiscussionCurrent ML models in pediatric urology are promising and have been applied to many major pediatric urology problems. These models still warrant further validation. However, with thoughtful implementation, they may be able to influence clinical practice in the near future.

Status of artificial intelligence in Pakistan and its implications in anesthesiology

Artificial intelligence (AI) refers to the technology that enables machine-based intelligent behavior. This descriptive article gives an overview of the implementation of artificial intelligence in the following disciplines within the field of clinical medicine: Anesthesiology, Cardiology, Pulmonology, Endocrinology, Nephrology, Gastroenterology, Neurology, Histopathology, and Radiology. In this article, we will discuss the employment of artificial intelligence in anesthesiology and provide an analysis of the several areas in which it is being utilized. We will also evaluate the current status of AI and its future in Pakistan. The importance of this manuscript is to highlight the benefits of artificial intelligence so that the benefits can be realized in Pakistan.
 Key words: Artificial intelligence; Anesthesiology; Clinical medicine; Endocrinology; Gastroenterology; Histopathology; Nephrology; Neurology; Pakistan; Radiology
 Citation: Khan MI. Status of artificial intelligence in Pakistan and its implications in anesthesiology. Anaesth. pain intensive care 2021;26(1):110-114.
 DOI: 10.35975/apic.v26i1.1776
 Received: September 12, 2021, Reviewed: November 24, 2021, Accepted: December 10, 2021

Conscious Empathic AI in Service

Recent advances in artificial intelligence (AI) have achieved human-scale speed and accuracy for classification tasks. Current systems do not need to be conscious to recognize patterns and classify them. However, for AI to advance to the next level, it needs to develop capabilities such as metathinking, creativity, and empathy. We contend that such a paradigm shift is possible through a fundamental change in the state of artificial intelligence toward consciousness, similar to what took place for humans through the process of natural selection and evolution. To that end, we propose that consciousness in AI is an emergent phenomenon that primordially appears when two machines cocreate their own language through which they can recall and communicate their internal state of time-varying symbol manipulation. Because, in our view, consciousness arises from the communication of inner states, it leads to empathy. We then provide a link between the empathic quality of machines and better service outcomes associated with empathic human agents that can also lead to accountability in AI services.

The Research and Development Thinking on the Status of Artificial Intelligence in Traditional Chinese Medicine.

With the rapid development and application of artificial intelligence (AI) in medical field, the diagnostic ways of human health and the social medical structures have changed. Based on the concept of holism and the theory of syndrome differentiation and treatment, TCM realizes comprehensive informatization and intelligence with the help of AI technology in data mining, intelligent diagnosis and treatment, intelligent learning, and decision-making. Furthermore, the intelligent research of TCM technology will further promote the improvement in TCM diagnosis and treatment rules and the leaping development of TCM intelligent instruments. In this article, we performed a systematic review of scientific literature about TCM and AI. Moreover, the practical problems of TCM intellectualization, the current situation and demand of TCM, and the influence of AI in the TCM field are discussed by searching for literature using TCM scientific databases, reference lists, expert consultation, and targeted websites. Finally, we look forward to the application prospects of AI and propose a possible future direction of intelligent TCM in the current health-care system in China.

The State of Artificial Intelligence in Marketing With Directions for Future Research

Today, artificial intelligence (AI) is becoming increasingly important in both industry and academics. To investigate AI in marketing, we have used bibliometric study, social network analysis (SNA), main path analysis, and content analysis to examine the top 10 authors, top 20 most cited articles, and top 11 milestone papers from our 628 articles sample. Bibliometric study identified leading authors, documents, universities, countries, and sources of these articles. By using SNA, we spotted an academic social network of crucial publications. Moreover, we recognized eleven milestone articles that constitute the main knowledge flow in AI marketing through main path analysis. Finally, we discussed future directions based on our findings. Our study is one among a few studies that have used bibliometric analysis methods to analyze and visualize the citation network of the AI-marketing interface.

ДИПЛОМАТИЯ ДАННЫХ РОССИИ: ЦЕЛИ, ТЕНДЕНЦИИ, ПРОГНОЗЫ

The article clearly demonstrates the current state of Russian data diplomacy and reveals the features of its formation in 2010–2021. Data diplomacy includes the use by the state of artificial intelligence (AI), big data analytics (Big Data) and other technological innovations to project influence on foreign audiences, block objectionable information on the Internet, and resolve other foreign policy problems. The first part of the study examines the stages of the development of digital diplomacy as an instrument of Russia’s foreign policy influence and analyzes the first steps of official Moscow to integrate Big Data into its diplomacy. The paper defines the existing concepts, methodological framework and successful cases of Russian Internet diplomacy. The next section identifies the trends, directions and projects of contemporary Russian diplomacy, as well as forecasts its further development. The authors state the interest of the Russian leadership in the development of data diplomacy as an analytical track of foreign policy forecasting and a set of means for integrating new tools into diplomatic activity. The findings of the study can be used in the practical aspect of the further development of Russian diplomacy and in the scientific aspect of studying its role in domestic foreign policy.

Artificial intelligence and medical education: A global mixed-methods study of medical students' perspectives.

ObjectiveMedical students, as clinicians and healthcare leaders of the future, are key stakeholders in the clinical roll-out of artificial intelligence-driven technologies. The authors aim to provide the first report on the state of artificial intelligence in medical education globally by exploring the perspectives of medical students.MethodsThe authors carried out a mixed-methods study of focus groups and surveys with 128 medical students from 48 countries. The study explored knowledge around artificial intelligence as well as what students wished to learn about artificial intelligence and how they wished to learn this. A combined qualitative and quantitative analysis was used.ResultsSupport for incorporating teaching on artificial intelligence into core curricula was ubiquitous across the globe, but few students had received teaching on artificial intelligence. Students showed knowledge on the applications of artificial intelligence in clinical medicine as well as on artificial intelligence ethics. They were interested in learning about clinical applications, algorithm development, coding and algorithm appraisal. Hackathon-style projects and multidisciplinary education involving computer science students were suggested for incorporation into the curriculum.ConclusionsMedical students from all countries should be provided teaching on artificial intelligence as part of their curriculum to develop skills and knowledge around artificial intelligence to ensure a patient-centred digital future in medicine. This teaching should focus on the applications of artificial intelligence in clinical medicine. Students should also be given the opportunity to be involved in algorithm development. Students in low- and middle-income countries require the foundational technology as well as robust teaching on artificial intelligence to ensure that they can drive innovation in their healthcare settings.

The State of Artificial Intelligence: Procurement versus Sales and Marketing

The State of Artificial Intelligence: Procurement versus Sales and Marketing

Current Status of Artificial Intelligence for Gastroenterology

Current Status of Artificial Intelligence for Gastroenterology

The intelligent machine: a new metaphor through which to understand both corporations and AI

This paper proposes to address the question of the legal status of artificial intelligence (AI) from a perspective that is unique to itself. Which means that, rather than attempting to place AI in the box of legal personhood—where many other nonhuman entities already reside, in a legal space where they are in a state of constant friction with humans—we will see whether these inhabitants can be placed in a different box: not that of legal personhood, but that of the intelligent machine. I accordingly argue that we have made a mistake in not clearly maintaining the original separation between legal persons and human persons: this is how legal persons—particularly as the concept applies to corporations—have so far been claiming and gaining the rights ascribed to humans, on the basis of their personhood. AI instead suggests the need to work outside the framework of this fiction: it suggests that we should stop using the fiction of the person for something that is not a person in the first place and in the most original and primary sense of this word. I propose a different metaphor, the metaphor of the intelligent machine, to this end drawing on the work of Dan-Cohen (2016), who advanced this metaphor to argue that it fits corporations better than the metaphor of the person. In conclusion, we should be able to see that this metaphor of the intelligent machine makes a better fit not only for corporations but also for AI and robotics.

DIGITALIZATION IN THE TRENDS OF LEGAL REGULATION AND LAW ENFORCEMENT AT THE PRESENT STAGE

The purpose of the research presented in this article is to study the prospects for the development of legal institutionalization of digital technologies in the context of public policy. This involves solving such research tasks as analyzing international requirements and trends in the legal regulation of digital processes and law enforcement, identifying problems and prospects of legal regulation in the context of digitalization, as well as describing trends in the development of law enforcement practice in the context of digitalization. The study made it possible to note a number of conclusions. The author notes that international requirements and trends in the legal regulation of digital processes and law enforcement create standards for the introduction of information technologies in public administration and the economy. Improving the competitiveness of domestic jurisdictions is one of the areas that reflect the effectiveness of measures to introduce digital technologies applied at the national level. Identification of problems and perspectives of legal regulation in the conditions of digitization has allowed the author to note that in the current circumstances, it is evident the further development of the recognition of digital technologies and actions as legal facts, identification of subjects, bases and procedure for the implementation of subjective rights and legal obligations in the information environment. It is emphasized that in the future it is necessary to solve questions about intellectual property, the legal status of artificial intelligence as an object of legal relations. As trends in the development of law enforcement practice in the context of digitalization, it is noted that we should expect the continuation of measures to digitalize legal proceedings, which require organizational decisions, including identification of the parties to the process, certification of credentials, obtaining certified judicial acts remotely. In the sphere of activity of Executive authorities, the introduction of electronic methods of exercising power for the provision of state and municipal services, as well as the implementation of state and municipal functions, continues.