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The growing interest of business in the use of artificial intelligence is determined by the enormous optimization, analytical, predictive and generative capabilities. A pressing issue in these conditions is determining the features of the use of artificial intelligence in specific tasks, operations and business processes of the company. The article reveals aspects of the use of artificial intelligence to personalize user content in the company’s marketing offers. An analysis of modern research on personalization and the use of artificial intelligence tools in marketing is carried out. A list of areas, tools and operations necessary to personalize a company’s marketing based on artificial intelligence is identified. Based on the results of the study, a list of tasks in the company’s marketing is provided that can be solved by modern artificial intelligence tools.

Artificial intelligence (AI) has advanced significantly in recent decades, impacting sectors such as medicine, education, and entertainment. In contrast, natural intelligence (NI) is the result of over a billion years of evolution, culminating in the Homo sapiens brain approximately one hundred thousand years ago. This development has enabled the creation of a culture of accumulated knowledge, facilitating technological and scientific advancements. The combination of AI and NI holds the promise of overcoming cognitive limitations and addressing complex problems, provided it is managed ethically. Key areas of this integration include cognitive augmentation, health, education, resolution of global challenges, redefinition of NI boundaries, and subsequent ethical challenges. While both NI and AI share learning and adaptation capabilities, human intelligence is distinguished by consciousness, emotion, and creativity, facets that AI has yet to fully replicate. The future of this interaction will depend on proper management that allows for the coexistence and mutual benefit of humans and machines.

Advancements in spine surgery have dramatically enhanced minimally invasive techniques, prominently through integrating robotic systems. Although pedicle screw placement remains the most widespread application of this technology, new developments are emerging to create innovative future avenues for these tools. This review explores the promising applications of robotic technology in minimally invasive spinal procedures, ranging from assistance with laminectomies and vertebroplasty to pain management and treatment of spinal tumors. We also discuss the potential for integrating artificial intelligence and augmented reality with robotic systems. If the current trajectory of research and innovation continues, there is promise in creating fully autonomous robotic systems that can revolutionize spine surgery by processing, planning, and performing procedures without heavy reliance on the surgeon., (Copyright © Congress of Neurological Surgeons 2025. All rights reserved.)

Artificial intelligence (AI) experience among nurses in perioperative settings is crucial for effective healthcare delivery. This study aimed to assess AI literacy levels and associated characteristics among perioperative nurses in Türkiye. This cross-sectional study was conducted between March 15 and April 15, 2024, and included 505 perioperative nurses. Data were collected online using the "Nurse Information Form" and "AI Literacy Scale." Snowball sampling technique was used to enlist individuals. The mean literacy score was 44.35 ± 5.88, which means moderate proficiency. Key elements associated with high literacy levels included being male, familiarity with and use of AI applications, youth, seeing AI as a tool to alleviate workload, and using information technology tools. The results show that although perioperative nurses have a moderate level of AI literacy, their use of AI tools is minimal. The findings underline the need for professional development in AI integration and the inclusion of relevant materials in nursing education programs., (© 2025 The Author(s). Nursing & Health Sciences published by John Wiley & Sons Australia, Ltd.)

The integration of artificial intelligence (AI) into pharmacy education offers transformative opportunities but also introduces significant challenges. This commentary explores whether AI will reshape or deform pharmacy education by analyzing its effects on personalized learning, complex concept comprehension, simulation-based clinical training, interprofessional education, and administrative efficiency. While AI-driven tools provide adaptive learning experiences, immersive visualizations, and streamlined administrative processes, concerns persist about overreliance on technology, skill atrophy, ethical and legal challenges, erosion of humanistic skills, inequities stemming from the digital divide, and faculty preparedness. To address these risks while harnessing AI's potential, a balanced approach is essential. Key strategies include integrating AI into curricula alongside traditional teaching methods, fostering digital literacy and critical thinking, enhancing humanistic education, supporting faculty development, ensuring equitable access, and establishing ethical frameworks. By thoughtfully implementing these strategies, pharmacy educators can prepare students to thrive in an AI-driven healthcare landscape while preserving core professional competencies., Competing Interests: Declaration of competing interest Nothing to declare., (Copyright © 2024 Elsevier Inc. All rights reserved.)

As artificial intelligence (AI) continues to reshape education, concerns about the authenticity of student work have escalated, particularly in relation to written assignments influenced by AI-powered tools. This article explores the role of the oral examination as a valuable method for assessing true student understanding and considers its potential for broader use across various educational levels. We propose that oral exams could be effectively integrated into undergraduate courses, providing a means to verify student comprehension in an era of AI-generated content. By adopting oral assessments, educators can enhance academic integrity while fostering essential skills such as critical thinking and effective communication in today's technology-driven world.

Background: Solid organ transplantation (SOT) is vital for end-stage organ failure but faces challenges like organ shortage and rejection. Artificial intelligence (AI) offers potential to improve outcomes through better matching, success prediction, and automation. However, the evolution of AI in SOT research remains underexplored. This study uses bibliometric analysis to identify trends, hotspots, and key contributors in the field., Methods: 821 articles from the Web of Science Core Collection were exported for analysis. Microsoft Excel 2021 was used for descriptive statistics. VOSviewer, CiteSpace, Scimago Graphica, and Biblioshiny were used for bibliometric analysis. The ggalluvial package in R was utilized to create Sankey diagrams, and top articles were selected based on citation count., Results: This analysis reveals the rapid expansion of AI in SOT. Key areas include robotic surgery, organ allocation, outcome prediction, immunosuppression management, and precision medicine. Robotic surgery has improved transplant outcomes. AI algorithms optimize organ matching and enhance fairness. Machine learning models predict outcomes and guide treatment, while AI-based systems advance personalized immunosuppression. AI in precision medicine, including diagnostics and imaging, is crucial for transplant success., Conclusion: This study highlights AI's transformative potential in SOT, with significant contributions from countries like the USA, Canada, and the UK. Key institutions such as the University of Toronto and the University of Pittsburgh have played vital roles. However, practical challenges like ethical issues, bias, and data integration remain. Fostering international and interdisciplinary collaborations is crucial for overcoming these challenges and accelerating AI's integration into clinical practice, ultimately improving patient outcomes., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Abstract: Artificial intelligence (AI), defined as algorithms built to reproduce human behavior, has various applications in health care such as risk prediction, medical image classification, text analysis, and complex disease diagnosis. Due to the increasing availability and volume of data, especially from electronic health records, AI technology is expanding into all fields of nursing and medicine. As the health care system moves toward automation and computationally driven clinical decision-making, nurses play a vital role in bridging the gap between the technological output, the patient, and the health care team. We explore the nurses' role in translating AI-generated output to patients and identify considerations for ensuring informed consent and shared decision-making throughout the process. A brief review of AI technology and informed consent, an identification of power dynamics that underly informed consent, and descriptions of the role of the nurse in various relationships such as nurse-AI, nurse-patient, and patient-AI are covered. Ultimately, nurses and physicians bear the responsibility of upholding and safeguarding the right to informed choice, as it is a fundamental aspect of safe and ethical patient-centered health care., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Introduction: Generative artificial intelligence (AI) is revolutionizing healthcare, necessitating corresponding advancements in nursing education to ensure that future nurses are equipped for a technologically driven environment. This article explores the imperative integration of generative AI literacy in nursing education., Implications for Nurse Educators: The article delves into the practical challenges and opportunities presented by generative AI in nursing. It underscores the need for educators to adapt curricula and teaching methods to effectively incorporate generative AI learning, ensuring students are proficient in generative AI technologies and aware of their ethical implications., Generative Ai Literacy: Defined as a core educational requirement, this section highlights the skills and knowledge that nurse educators must impart. It encompasses the ability to critically assess AI-generated content, understand the underlying technologies, and responsibly apply this knowledge in clinical settings., Conclusion: The article concludes by emphasizing the urgency of integrating generative AI literacy into nursing education. It advocates for a proactive approach to curriculum development and calls for global collaboration and standardization in AI education to address the diverse and evolving needs of healthcare., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Chronic wounds are a common condition that affects a patient's quality of life and drives the cost of health care delivery high. Vigilant wound assessment and close monitoring using adequate and reliable methods and technology are vital to wound care management. Wound assessment, including visual evaluation of tissue and surrounding skin, and measurements are essential in developing an appropriate care plan. Usually, this visual evaluation is accomplished by photographic images, measuring guides, and depth probing. Artificial intelligence may offer ways to increase assessment accuracy, enhance treatment plans, optimize patient-clinician face time, and increase clinicians' access to improve wound care outcomes., (Copyright © 2024 Elsevier Inc. All rights reserved.)

The widespread adoption of artificial intelligence (AI) tools in academic settings has the potential to revolutionize learning experiences, enhance educational outcomes, and streamline academic processes. The aim of this research was to explore the perceptions of Lebanese health sciences students regarding the use of generative AI in higher education. A qualitative descriptive research design informed by descriptive phenomenology was employed. Semi-structured interviews were carried out among 23 health sciences students at one major private university in Beirut. Inductive thematic analysis was conducted over the period of 3 months. The inductive thematic analysis generated two themes, and eight subthemes highlighting the benefits and concerns in using AI; customized, self-paced, and autonomous learning, improved language and writing skills, development of innovative concepts, enhanced efficiency, accuracy of information, overreliance on AI, equitable access, unclear policies. Results from this study emphasized the importance of combined efforts across sectors to close access gaps, encourage inclusiveness, and develop well framed policies that enable students to utilize these new technologies for their maximum benefits., (© 2025 John Wiley & Sons Australia, Ltd.)

As in general pathology, digitalization is also inexorably making its way into dermatopathology. This article examines the current state of digitalization in German dermatopathology laboratories based on the authors' own experiences, the current study situation, and a survey of members of the Dermatological Histology Working Group (ADH). Experiences with the establishment of a digital laboratory workflow, artificial intelligence (AI)-based assistance systems, and whole slide images (WSI)-based training programs are discussed. Digitalization in dermatopathology is an opportunity to simplify and accelerate processes, but there are some hurdles to overcome., Competing Interests: Einhaltung ethischer Richtlinien. Interessenkonflikt: S.A. Braun, P. Schmidle, N. Duschner und J. Schaller geben an, dass kein Interessenkonflikt besteht. Für diesen Beitrag wurden von den Autor/-innen keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien., (© 2025. The Author(s), under exclusive licence to Springer Medizin Verlag GmbH, ein Teil von Springer Nature.)

Background and Objective: While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management., Databases and Data Treatment: This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management., Results: From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems., Conclusion: There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI., Significance: This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum., (© 2024 The Author(s). European Journal of Pain published by John Wiley & Sons Ltd on behalf of European Pain Federation ‐ EFIC ®.)

With rapid advances in digital technology and artificial intelligence, the medical field is undergoing numerous changes, with innovations that are making medicine increasingly precise and personalized. Computational analysis, which uses advanced computing techniques to analyze biomedical images or data, is playing an increasingly important role and has already been adopted in some specialties, such as oncopathology, to improve diagnostic speed, refine prognoses, and identify targeted treatments while predicting therapeutic responses. Nephropathology will not escape these technological advances, which will likely transform its approach in the coming years. This review explores the progress of image analysis in nephropathology., Competing Interests: Les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Artificial intelligence (AI) has been increasingly used in delivering mental healthcare worldwide. Within this context, the traditional role of mental health nurses has been changed and challenged by AI-powered cutting-edge technologies emerging in clinical practice. The aim of this integrative review is to identify and synthesise the evidence of AI-based applications with relevance for, and potential to enhance, mental health nursing practice. Five electronic databases (CINAHL, PubMed, PsycINFO, Web of Science and Scopus) were systematically searched. Seventy-eight studies were identified, critically appraised and synthesised following a comprehensive integrative approach. We found that AI applications with potential use in mental health nursing vary widely from machine learning algorithms to natural language processing, digital phenotyping, computer vision and conversational agents for assessing, diagnosing and treating mental health challenges. Five overarching themes were identified: assessment, identification, prediction, optimisation and perception reflecting the multiple levels of embedding AI-driven technologies in mental health nursing practice, and how patients and staff perceive the use of AI in clinical settings. We concluded that AI-driven technologies hold great potential for enhancing mental health nursing practice. However, humanistic approaches to mental healthcare may pose some challenges to effectively incorporating AI into mental health nursing. Meaningful conversations between mental health nurses, service users and AI developers should take place to shaping the co-creation of AI technologies to enhance care in a way that promotes person-centredness, empowerment and active participation., (© 2025 The Author(s). International Journal of Mental Health Nursing published by John Wiley & Sons Australia, Ltd.)

Expansion of artificial intelligence (AI) in the field of medicine is changing the paradigm of clinical practice at a rapid pace. Incorporation of AI in medicine offers new tools as well as challenges, and physicians and learners need to adapt to assimilate AI into practice and education. AI can expedite early diagnosis and intervention with real-time multimodal monitoring. AI assistants can decrease the clerical burden of heath care improving the productivity of work force while mitigating burnout. There are still no regulatory parameters for use of AI and regulatory framework is needed for the implementation of AI systems in medicine to ensure transparency, accountability, and equitable access., Competing Interests: Disclosure The authors have nothing to disclose., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Purpose of Review: Though simple in its fundamental mechanism - a critical disruption of local blood supply - stroke is complicated by the intricate nature of the neural substrate, the neurovascular architecture, and their complex interactions in generating its clinical manifestations. This complexity is adequately described by high-resolution imaging with sensitivity not only to parenchymal macrostructure but also microstructure and functional tissue properties, in conjunction with detailed characterization of vascular topology and dynamics. Such descriptive richness mandates models of commensurate complexity only artificial intelligence could plausibly deliver, if we are to achieve the goal of individually precise, personalized care., Recent Findings: Advances in machine vision technology, especially deep learning, are delivering higher fidelity predictive, descriptive, and inferential tools, incorporating increasingly rich imaging information within ever more flexible models. Impact at the clinical front line remains modest, however, owing to the challenges of delivering models robust to the noisy, incomplete, biased, and comparatively small-scale data characteristic of real-world practice., Summary: The potential benefit of introducing AI to stroke, in imaging and elsewhere, is now unquestionable, but the optimal approach - and the path to real-world application - remain unsettled. Deep generative models offer a compelling solution to current obstacles and are predicted powerfully to catalyse innovation in the field., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Artificial intelligence (AI) is significantly transforming surgery by enhancing precision, decision-making, and patient outcomes. This bibliometric analysis examines AI's impact on surgery, highlighting research trends, key contributors, and evolving themes from 1998 to 2024. Utilizing data from the Web of Science Core Collection and analyzed through the Bibliometrix tool, the study reviews publication trends, author impact, institutional contributions, country-specific research activities, and keyword frequency. A total of 821 articles were examined, revealing a 14.53% annual growth rate in publications, increasing from one in 1998 to 328 in 2023. Influential contributors include 10 157 authors, notably HASHIMOTO DA and ITO M. Prominent institutions such as Harvard University and Stanford University, along with leading countries like the USA and China, play major roles in this field. High-frequency keywords identify core research areas: surgery, artificial intelligence, classification, diagnosis, and outcomes. Thematic evolution shows a shift from foundational concepts to advanced applications and interdisciplinary collaborations. AI integration into surgical practices is revolutionizing the field, driving advancements in precision, efficiency, and patient care. The study underscores significant research growth, influential contributors, and key trends, emphasizing the importance of continued interdisciplinary collaboration and innovation. Future research should focus on enhancing AI applications, addressing data quality and security challenges, and expanding into diverse surgical contexts to further improve surgical outcomes and patient care. AI in surgery is a rapidly evolving and promising field for innovation, with its full potential reliant on enhanced collaboration across disciplines., (Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc.)

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the International Anesthesia Research Society.)

Background: Over the past 5 years, managed care pharmacy has been shaped by a global pandemic, advancements in generative artificial intelligence (AI), Medicare drug price negotiation policies, and significant therapeutic developments. Collective intelligence methods can be used to anticipate future developments in practice to help organizations plan and develop new strategies around those changes., Objective: To identify emerging trends in managed care pharmacy., Methods: In this sequential mixed-method study, we invited experts to participate in a multidisciplinary advisory panel to develop a survey with 5 overarching domains. The qualitative analysis for our advisory panel meetings used a thematic analysis approach. To analyze the cross-sectional survey results, we used descriptive statistics and exploratory bivariate statistics to test for possible relationships with survey respondent demographics and likelihood predictions. To assess respondent opinions on the overall likelihood of an event occurring in the next 5 years, we combined "Highly likely/Somewhat likely" responses and compared with "Highly unlikely/Somewhat unlikely" responses., Results: Following our advisory panel focus groups, a total of 53 scenarios were developed for inclusion in the quantitative survey under the domains of (1) information technology, (2) therapeutics and diagnostics, (3) payment models, (4) pharmacy operations, and (5) public policy. A total of 1,238 individuals were invited to participate in the survey. Of eligible participants, 201 complete survey responses were received for a final response rate of 16.2%. Survey participants rated increased use of glucagon-like peptide-1 receptor agonists by at least 25%, at least 1 major data breach, more than 10 new orphan drug approvals, and AI use in more than half of prior authorization reviews as the most likely scenarios to occur in the next 5 years. Respondents identified the following broad issues as those most likely to impact their organizations (employers) in the next 5 years: federal and state policy changes impacting managed care, cell and gene therapies, impact of AI on managed care operations, and emerging payment models., Conclusions: This study provides valuable insights into the emerging trends that are expected to shape managed care pharmacy over the next 5 years. The integration of advanced technologies, such as AI, along with the increasing focus on specialty therapeutics, represents both opportunities and challenges for managed care organizations. However, areas with lower consensus highlight the need for caution in strategic planning.

Large Language Models (LLMs) are a type of generative artificial intelligence (AI) that produce realistic-sounding language in response to text prompts, giving AI the capability to simulate human discourse in various domains, including medical education.1 The pace of technological advancement is staggering, which comes with promise and peril. This Last Page summarizes some potential LLM uses in medical education.2 However, AI is rapidly advancing, and these uses will likely evolve as well., (Copyright © 2024 the Association of American Medical Colleges.)

Addiction medicine is confronting challenges brought about by changes in our modern world, including shifts in the drug market and the increasing digitalization of our environments. This article highlights some of these challenges and explores potential strategies to address them. The field is also advancing through robust research momentum in psychedelic-assisted psychotherapy. Additionally, the rapid growth of non-invasive brain stimulation techniques and artificial intelligence-driven research demonstrates the integration of cutting-edge technologies into addiction medicine, with the potential to transform clinical practice in the years ahead., Competing Interests: Les auteur-e-s n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Background: Artificial intelligence (AI) has already revolutionized the analysis of image, text, and tabular data, bringing significant advances across many medical sectors. Now, by combining with wearable inertial measurement units (IMUs), AI could transform health care again by opening new opportunities in patient care and medical research., Objective: This systematic review aims to evaluate the integration of AI models with wearable IMUs in health care, identifying current applications, challenges, and future opportunities. The focus will be on the types of models used, the characteristics of the datasets, and the potential for expanding and enhancing the use of this technology to improve patient care and advance medical research., Methods: This study examines this synergy of AI models and IMU data by using a systematic methodology, following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, to explore 3 core questions: (1) Which medical fields are most actively researching AI and IMU data? (2) Which models are being used in the analysis of IMU data within these medical fields? (3) What are the characteristics of the datasets used for in this fields?, Results: The median dataset size is of 50 participants, which poses significant limitations for AI models given their dependency on large datasets for effective training and generalization. Furthermore, our analysis reveals the current dominance of machine learning models in 76% on the surveyed studies, suggesting a preference for traditional models like linear regression, support vector machine, and random forest, but also indicating significant growth potential for deep learning models in this area. Impressively, 93% of the studies used supervised learning, revealing an underuse of unsupervised learning, and indicating an important area for future exploration on discovering hidden patterns and insights without predefined labels or outcomes. In addition, there was a preference for conducting studies in clinical settings (77%), rather than in real-life scenarios, a choice that, along with the underapplication of the full potential of wearable IMUs, is recognized as a limitation in terms of practical applicability. Furthermore, the focus of 65% of the studies on neurological issues suggests an opportunity to broaden research scope to other clinical areas such as musculoskeletal applications, where AI could have significant impacts., Conclusions: In conclusion, the review calls for a collaborative effort to address the highlighted challenges, including improvements in data collection, increasing dataset sizes, a move that inherently pushes the field toward the adoption of more complex deep learning models, and the expansion of the application of AI models on IMU data methodologies across various medical fields. This approach aims to enhance the reliability, generalizability, and clinical applicability of research findings, ultimately improving patient outcomes and advancing medical research., (©Ricardo Smits Serena, Florian Hinterwimmer, Rainer Burgkart, Rudiger von Eisenhart-Rothe, Daniel Rueckert. Originally published in JMIR mHealth and uHealth (https://mhealth.jmir.org), 29.01.2025.)

Ophthalmology is notable for significant technological innovations (e.g laser, microscopy…) that allow for diagnostic and therapeutic advances every year. This article presents the main significant therapeutic progress and highlights the growing use of artificial intelligence in diagnosis of eye disease. We include a focus on emergent virus-mediated ocular conditions that have garnered attention lately, as the number of patients with viral infections is increasing worldwide., Competing Interests: les auteurs n’ont déclaré aucun conflit d’intérêts en relation avec cet article.

Histopathologic evaluation of liver biopsy has played a longstanding role in the diagnosis and management of liver disease. However, the utility of liver biopsy has been questioned by some, given the improved imaging modalities, increased availability of noninvasive serologic tests, and development of artificial intelligence over the past several years. In this review, we discuss the current and future role of liver biopsy in both non-neoplastic and neoplastic liver diseases in the era of improved noninvasive laboratory, radiologic, and digital technologies., (Copyright © 2025 The Author(s). Published by Wolters Kluwer Health, Inc. on behalf of the American Association for the Study of Liver Diseases.)

Part 2 explores the transformative potential of artificial intelligence (AI) in addressing the complexities of headache disorders through innovative approaches, including digital twin models, wearable healthcare technologies and biosensors, and AI-driven drug discovery. Digital twins, as dynamic digital representations of patients, offer opportunities for personalized headache management by integrating diverse datasets such as neuroimaging, multiomics, and wearable sensor data to advance headache research, optimize treatment, and enable virtual trials. In addition, AI-driven wearable devices equipped with next-generation biosensors combined with multi-agent chatbots could enable real-time physiological and biochemical monitoring, diagnosing, facilitating early headache attack forecasting and prevention, disease tracking, and personalized interventions. Furthermore, AI-driven advances in drug discovery leverage machine learning and generative AI to accelerate the identification of novel therapeutic targets and optimize treatment strategies for migraine and other headache disorders. Despite these advances, challenges such as data standardization, model explainability, and ethical considerations remain pivotal. Collaborative efforts between clinicians, biomedical and biotechnological engineers, AI scientists, legal representatives and bioethics experts are essential to overcoming these barriers and unlocking AI's full potential in transforming headache research and healthcare. This is a call to action in proposing novel frameworks for integrating AI-based technologies into headache care., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Competing interests: All authors are Junior Editors of The Journal of Headache and Pain. IP also serves as Head of Imaging Section of the SN Comprehensive Clinical Medicine journal and as the Guest Editor in The Journal of Headache and Pain. Out of this work, LP has been employed by Lundbeck in the past two years. RR has received travel grants, honoraria for talks or advisory boards from AbbVie, Lilly, Lundbeck, Novartis, Pfizer and Teva. MW-P reports personal fees from AbbVie, Pfizer, Polpharma and Teva for speaker activities. ERB declares no further conflicts of interest., (© 2024. The Author(s).)

Artificial intelligence (AI) has emerged as a significant area of interest in medicine, with the potential to influence various aspects of health care. However, the real benefits of applications of AI to medicine often become obscured by the considerable attention, and hype, around its capabilities. To better understand AI's role in medicine, it is important to contextualize its development and review the stages of AI evolution that have contributed to its present state., Competing Interests: Disclosure: No financial disclosures reported. Figure 1 was created by DALL-E, an AI image generator., (Copyright 2025 by the Missouri State Medical Association.)

The pharmacy profession stands at a pivotal juncture as it faces unprecedented technological advancements that will rapidly reshape the healthcare landscape by 2050. This commentary explores the implications of technological advancements for pharmacy practice and how it may impact the workforce in the coming decades. The advent of digital health technologies (DHTs), artificial intelligence (AI), and automation is poised to transform patient engagement and care delivery. The Covid-19 pandemic has accelerated this shift, and traditional pharmacy roles have evolved to meet these expectations. As AI and DHT adoption increase, it will push the profession beyond its historical focus on logistics and medication dispensing. Several potential scenarios may unfold by 2050, ranging from a diminished workforce overshadowed by technology to an evolved profession that leverages novel technologies to enhance clinical services and patient care. To prepare for these changes, the profession must address key challenges, including advancing education and training to incorporate new competencies, establishing sustainable financial models for emerging roles, embracing advocacy and navigating regulatory constraints to expand scopes of practice, and overcoming societal resistance to change. By embracing continuous learning and adaptability, pharmacists can seize the opportunity to redefine their roles, ensuring they remain indispensable in an evolving healthcare environment., Competing Interests: Disclosure Timothy Dy Aungst, PharmD, has served as a consultant for Otsuka Pharmaceuticals, GoodRx, and serves as an advisor for The Digital Health Alliance., (Copyright © 2024 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

This paper focuses on the implications of Artificial Intelligence (AI) for the nursing workforce, examining both the opportunities presented by AI in relieving nurses of routine tasks and enabling better patient care, and the potential challenges it poses. The discussion highlights the freeing of nurses' time from administrative duties, allowing for more patient interaction and professional development, while also acknowledging concerns about job displacement. Ethically integrating AI into patient care and the need for nurses' proactive engagement with AI-including involvement in its development and integration in nursing education-are emphasized. Finally, the paper asserts the necessity for nurses to become active participants in AI's evolution within health care to ensure the enhancement of patient care and the advancement of nursing roles., Competing Interests: Declaration of Competing Interest The authors declare no conflicts of interest., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Advances in artificial intelligence (AI) and machine learning systems promise faster, more efficient, and more personalized care. While many of these models are built on the premise of improving access to the timely screening, diagnosis, and treatment of cardiovascular disease, their validity and accessibility across diverse and international cohorts remain unknown. In this mini-review article, we summarize key obstacles in the effort to design AI systems that will be scalable, accessible, and accurate across distinct geographical and temporal settings. We discuss representativeness, interoperability, quality assurance, and the importance of vendor-agnostic data types that will be available to end-users across the globe. These topics illustrate how the timely integration of these principles into AI development is crucial to maximizing the global benefits of AI in cardiology., Competing Interests: Declaration of interest The authors acknowledge funding from the National Institutes of Health under awards 1F32HL170592-01 (E.K.O.), K23HL153775 (R.K.), and R01HL167858 (R.K.), and the Doris Duke Charitable Foundation under award 2022060 (R.K.). E.K.O. is a co-founder of Evidence2Health LLC, a co-inventor in patent applications (US17/720,068, 63/619,241, 63/177,117, 63/580,137, 63/606,203, WO2018078395A1, WO2020058713A1), has been an ad hoc consultant for Caristo Diagnostics, Ltd, and has received royalty fees from technology licensed through the University of Oxford. R.K. is an Associate Editor of JAMA. He receives research support, through Yale, from Bristol-Myers Squibb, Novo Nordisk, and BridgeBio. He is a co-inventor of U.S. Patent Applications 63/177,117, 63/428,569, 63/346,610, 63/484,426, 63/508,315, 63/580,137, 63/606,203, and 63/562,335. He is a co-founder of Evidence2Health and Ensight-AI, Inc., (Copyright © 2024 Hellenic Society of Cardiology. Publishing services by Elsevier Inc. All rights reserved.)

Generative artificial intelligence (AI) tools powered by large language models (LLMs) are poised to transform medical education. Generative AI's broad knowledge base and powerful natural language processing allow educators to use it for numerous tasks that previously required significant human effort. In this AM Last Page, we provide several examples of tasks that AI can accomplish as well as a basic framework for educators to develop their own prompts., (Copyright © 2024 the Association of American Medical Colleges.)

Purpose of Review: As the surge in large language models (LLMs) and generative artificial intelligence (AI) applications in ophthalmology continue to expand, this review seeks to update physicians of the current progress, to catalyze further work to harness its capabilities to enhance healthcare delivery in ophthalmology., Recent Findings: Generative AI applications have shown promising performance in Ophthalmology. Beyond native LLMs and question-answering based tasks, there has been increasing work in employing novel LLM techniques and exploring wider use case applications., Summary: In this review, we first look at existing LLM use case applications specific to Ophthalmology, followed by an overview of commonly used LLM techniques. We finally focus on the emerging trends of the generative AI space with an angle from ophthalmology., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

The proliferation of Generative Artificial Intelligence (Generative AI) has led to an increased reliance on AI-generated content for designing and deploying digital health interventions. While generative AI has the potential to facilitate and automate healthcare, there are concerns that AI-generated content and AI-generated health advice could trigger, perpetuate, or exacerbate prior traumatic experiences among vulnerable populations. In this discussion article, I examined how generative-AI-powered digital health interventions could trigger, perpetuate, or exacerbate emotional trauma among vulnerable populations who rely on digital health interventions as complementary or alternative sources of seeking health services or information. I then proposed actionable strategies for mitigating AI-generated trauma in the context of digital health interventions. The arguments raised in this article are expected to shift the focus of AI practitioners against prioritizing dominant narratives in AI algorithms into seriously considering the needs of vulnerable minority groups who are at the greatest risk for trauma but are often invisible in AI data sets, AI algorithms, and their resultant technologies., (© 2024 The Author(s). Nursing Inquiry published by John Wiley & Sons Ltd.)

The rapid evolution of highly adaptable and reusable artificial intelligence models facilitates the implementation of digital twinning and has the potential to redefine cardiovascular risk prevention. Digital twinning combines vast amounts of data from diverse sources to construct virtual models of an individual. Emerging artificial intelligence models, called generalist AI, enable the processing of different types of data, including data from electronic health records, laboratory results, medical texts, imaging, genomics, or graphs. Among their unprecedented capabilities are an easy adaptation of a model to previously unseen medical tasks and the ability to reason and explain output using precise medical language derived from scientific literature, medical guidelines, or knowledge graphs. The proposed combination of a digital twinning approach with generalist AI is a path to accelerate the implementation of precision medicine and enhance early recognition and prevention of cardiovascular disease. This proposed strategy may extend to other domains to advance predictive, preventive, and precision medicine and also boost health research discoveries., (Copyright © 2024 Hellenic Society of Cardiology. All rights reserved.)

Paradox is living phenomenon that provides insights into straight thinking and diverse human experiences important to the discipline of nursing from a nursing philosophical theory-based approach. The author here delves into the metaphorical experience of living on the edge and the paradoxical concepts that assist the discipline in its thinking about artificial intelligence. Possible ethical implications of utilizing artificial intelligence from a humanbecoming ethos of understanding is utilized. The metaphorical implications for future disciplinary priorities are presented., Competing Interests: Declaration of Conflicting InterestsThe author declared no potential conflicts of interest with respect to the authorship and/or publication of this review.

Background: Recent advancements in artificial intelligence (AI) have significantly impacted dermatology, particularly in diagnosing skin diseases. However, aesthetic dermatology faces unique challenges due to subjective evaluations and the lack of standardized assessment methods., Aims: This review aims to explore the current state of AI in dermatology, evaluate its application in diagnosing skin conditions, and discuss the limitations of traditional evaluation methods in aesthetic dermatology. Additionally, the review proposes strategies for future integration of AI to address existing challenges., Methods: A comprehensive review of AI applications in dermatology was conducted, in both diagnostic and aesthetic fields. Traditional methods such as subjective surveys and hardware devices were analyzed and compared with emerging AI technologies. The limitations of current AI models were evaluated, and the need for standardized evaluation methods and diverse datasets was identified., Results: AI has shown great potential in diagnosing skin diseases, particularly skin cancer. However, in aesthetic dermatology, traditional methods remain subjective and lack standardization, therefore limiting their effectiveness. Emerging AI applications in this field show promise, but they have significant limitations due to biased datasets and inconsistent evaluation methods., Conclusions: To develop the potential of AI in aesthetic dermatology, it is crucial to create standardized evaluation methods, collect diverse datasets reflecting various ethnicities and ages, and educate practitioners on AI's utility and limitations. Addressing these challenges will improve diagnostic accuracy, better patient outcomes, and help integrate AI effectively into clinical practice., (© 2024 The Author(s). Journal of Cosmetic Dermatology published by Wiley Periodicals LLC.)

Background: Artificial intelligence (AI) acts as the state-of-the-art in periodontitis diagnosis in dentistry. Current diagnostic challenges include errors due to a lack of experienced dentists, limited time for radiograph analysis, and mandatory reporting, impacting care quality, cost, and efficiency., Objective: This review aims to evaluate the current and future trends in AI for diagnosing periodontitis., Methods: A thorough literature review was conducted following PRISMA guidelines. We searched databases including PubMed, Scopus, Wiley Online Library, and ScienceDirect for studies published between January 2018 and December 2023. Keywords used in the search included "artificial intelligence," "panoramic radiograph," "periodontitis," "periodontal disease," and "diagnosis.", Results: The review included 12 studies from an initial 211 records. These studies used advanced models, particularly convolutional neural networks (CNNs), demonstrating accuracy rates for periodontal bone loss detection ranging from 0.76 to 0.98. Methodologies included deep learning hybrid methods, automated identification systems, and machine learning classifiers, enhancing diagnostic precision and efficiency., Conclusions: Integrating AI innovations in periodontitis diagnosis enhances diagnostic accuracy and efficiency, providing a robust alternative to conventional methods. These technologies offer quicker, less labor-intensive, and more precise alternatives to classical approaches. Future research should focus on improving AI model reliability and generalizability to ensure widespread clinical adoption.

Abstract: The rapid rise of artificial intelligence (AI) is transforming society; yet, the education of health care providers in this field is lagging. In health care, where AI promises to facilitate diagnostic accuracy, and allow for personalized treatment, bridging the knowledge and skill gaps for providers becomes vital. This article explores the challenges of AI education, such as the emergence of self-proclaimed experts during the pandemic, and the need for comprehensive training in AI language, mechanics, and ethics. It advocates for a new breed of health care professionals who are both practitioners and informaticians, who are capable through initial training or through continuing professional development of harnessing AI's potential. Interdisciplinary collaboration, ongoing education, and incentives are proposed to ensure health care benefits from AI's trajectory. This perspective article explores the hurdles and the imperative of creating educational programming designed specifically to help health care professionals augment their practice with AI., Competing Interests: Disclosures: The authors declare no conflict of interest., (Copyright © 2024 The Alliance for Continuing Education in the Health Professions, the Association for Hospital Medical Education, and the Society for Academic Continuing Medical Education.)

The integration of artificial intelligence (AI) in healthcare delivery represents a transformative opportunity to enhance the lives of people living with disabilities. AI-driven technologies, such as assistive devices, conversational agents, and rehabilitation tools, can mitigate health disparities, improve diagnostic accuracy, and facilitate effective communication with healthcare providers, fostering more equitable healthcare environments. This commentary explores these applications while addressing the ethical challenges and limitations associated with AI deployment. Specific challenges, such as algorithmic bias, privacy risks with patient data, and the complexity of designing inclusive technologies, are discussed to provide a balanced perspective. For example, biased diagnostic tools may lead to inequitable care, and privacy breaches can compromise sensitive data. Key areas of focus include personalised care through AI-powered systems, the design of inclusive AI technologies incorporating continuous feedback loops and partnerships with advocacy groups, and the development of AI-enabled robotics for physical assistance. This commentary paper emphasises the importance of addressing these limitations alongside advancing ethical AI practices and ensuring continuous user involvement to meet the diverse needs of people living with disabilities, ultimately promoting greater independence and participation in society. Consequently, while AI holds transformative potential in advancing equitable and inclusive healthcare for people with disabilities, addressing ethical challenges, overcoming limitations, and fostering user-centred design are essential to fully realise its benefits and ensure these innovations promote autonomy, accessibility, and well-being., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

INTRODUCTION: The continued growth of urbanization presents new challenges. This, in turn, will lead to pressure for sustainable environment initiatives, with demands for more and better infrastructure in the diminishing space available and improved quality of life for city dwellers at a more affordable cost. Smart Cities are part of the solution to the growing challenges of urbanization. The adoption of new technologies like artificial intelligence (AI) is transforming cities, making them smarter, faster, and predicting opportunities for improvement. OBJECTIVES: This study is conducting a detailed bibliometric survey to investigate the applications and trends of Artificial Intelligence research for different areas of smart cities and emphasizing the potential effects and challenges of AI adaptation in smart cities over the past 30.5 years. METHODS: For this study, the Scopus database was used to collect a total of 1925 documents published between 1991-2021 (July). The bibliometric analysis includes document types, subject categorization, document growth, as well as top contributing sources, countries, authors, and funding sponsors. It also analyses keywords, abstracts, titles, and characteristics of most cited documents. RESULTS: The analyzed findings of this research study reflect not only the significance of AI technology for various applications within numerous sectors in the smart city but also major obstacles in AI research for various sectors of smart cities. CONCLUSION: The research demonstrates that AI has the ability to construct today's and tomorrow's smart cities, but that each region's potentials, conditions, and circumstances must be addressed in order to achieve a smooth internet city development. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) into healthcare represents a paradigm shift with the potential to enhance patient care and streamline clinical operations. This commentary explores the Canadian perspective on key organizational considerations for nurse executives, emphasizing the critical role they play in fostering the establishment of AI governance structures and advancing the front-line adoption of AI in nursing practice. The discussion delves into five domains of consideration, analyzing recent developments and implications for nursing executives. This commentary helps nurse leaders who are getting started in AI become familiar with necessary information and understand Canada's landscape., (Copyright © 2025 Longwoods Publishing.)

Objective: This umbrella review of reviews aims to synthesise current evidence on AI's utilisation in developing education within health sciences disciplines., Design: An umbrella review of reviews, review of reviews, based on Joanna Briggs Institute guidelines., Data Selection: CINAHL, ERIC(ProQuest), PubMed, Scopus, and Medic were systematically searched in December 2023 with no time limit. The inclusion and exclusion criteria were defined according to the PCC framework: Participants(P), Concept(C), and Context (C). Two independent researchers screened 6304 publications, and 201 reviews were selected in the full-text phase., Data Extraction: All the reviews that met inclusion criteria were included in the analysis. The reference lists of included reviews were also searched. Included reviews were quality appraised. The results were analysed with narrative synthesis., Results of Data Synthesis: Seven reviews published between 2019 and 2023 were selected for analysis. Five key domains were identified: robotics, machine learning and deep learning, big data, immersive technologies, and natural language processing. Robotics enhances practical medical, dental and nursing education training. Machine learning personalises learning experiences and improves diagnostic skills. Immersive technologies provide interactive simulations for practical training., Conclusion: This umbrella review of reviews highlights the potential of AI in health sciences education and the need for continued investment in AI technologies and ethical frameworks to ensure effective and equitable integration into educational practices., Competing Interests: Declaration of competing interest Given her role as Editor of the target journal, Professor Kristina Mikkonen had no involvement in the peer-review of this article and has no access to information regarding its peer-review. An independent editor was given full responsibility for the editorial process of this article., (Copyright © 2025 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: Nursing education increasingly emphasizes academic writing and communication, critical for delivering quality patient care and professional advancement. Rapidly emerging artificial intelligence (AI) tools such as ChatGPT and Copilot are transforming educational methodologies, and a focus is being placed on embedding AI literacy to effectively bridge the gap between theoretical knowledge and clinical practice. These technologies have the potential to reshape nursing education in a technology-driven health-care landscape., Aim: This study investigated the effectiveness of AI literacy and the application of ChatGPT and Copilot in academic nursing report writing. It assessed the level of AI literacy of nursing students, examined the integration of basic AI concepts into a curriculum, and analyzed the impact of these tools compared with traditional teaching methods., Methods: The study adopted a sample of 203 senior nursing students from Southern Taiwan to compare an AI-enhanced teaching approach using ChatGPT and Copilot with conventional methods. The curriculum, centered on the "Writing Case Reports and Seminars" course, employed the Analyze, Design, Develop, Implement, Evaluate model and incorporated scaffolding techniques to synergistically integrate clinical skills with academic learning. AI literacy was measured using the Meta AI Literacy Scale (MAILS). Summative assessments, adhering to the Taiwan Nursing Association standards, focused on individual and group case report evaluations., Findings: Following an 18-week AI intervention, the experimental group demonstrated significant improvements in all dimensions of the MAILS. A ChatGPT usage of 100 % was found, with a notable enhancement discovered in the "Nursing Plan" section of case reports. Although the experimental group outperformed the control group in overall case report evaluations, the connections between identified problems and proposed plans were weaker and nursing interventions tended to be less individualized for the experimental group., Conclusions: The incorporation of AI tools such as ChatGPT and Copilot into a scaffolding teaching framework significantly boosted students' AI literacy and performance in summative assessments. Effective AI training for students, supervised use of these tools, and continuous professional development for educators are paramount to successful implementation. Addressing the current limitations of AI has the potential to further improve academic writing, foster critical thinking, and ensure responsible application in patient care, ultimately leading to higher-quality and more effective nursing education., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2025 Elsevier Ltd. All rights reserved.)

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student's specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI's knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners., Competing Interests: Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

The integration of technology into health professions assessment has created multiple possibilities. In this paper, we focus on the challenges and opportunities of integrating technologies that are used during clinical activities or that are completed by raters after a clinical encounter. In focusing on technologies that are more proximal to practice, we identify tradeoffs with different data collection approaches. To maximize the benefits of integrating technology in workplace-based assessment, we describe the importance of using preexisting frameworks from the fields of assessment design, implementation research, and clinical artificial intelligence governance., Competing Interests: Dr. Krumm receives research support from the Society for Improving Medical Professional Learning (SIMPL)., (Copyright: © 2024 The Author(s).)

Mobile health has the potential to revolutionize health care delivery and patient engagement. In this work, we discuss how integrating Artificial Intelligence into digital health applications focused on supply chain operation, patient management, and capacity building, among other use cases, can improve the health system and public health performance. We present the Causal Foundry Artificial Intelligence and Reinforcement Learning platform, which allows the delivery of adaptive interventions whose impact can be optimized through experimentation and real-time monitoring. The system can integrate multiple data sources and digital health applications. The flexibility of this platform to connect to various mobile health applications and digital devices, and to send personalized recommendations based on past data and predictions, can significantly improve the impact of digital tools on health system outcomes. The potential for resource-poor settings, where the impact of this approach on health outcomes could be decisive, is discussed. This framework is similarly applicable to improving efficiency in health systems where scarcity is not an issue.

Objective: The main aim of this study was to explore and understand the emerging developments, changes, and patterns shaping the future of the pharmaceutical industry. The pharmaceutical industry is evolving rapidly due to scientific breakthroughs, societal changes, and technological advancements. Companies can adapt, and innovate to stay competitive and advance healthcare objectives by understanding the changes and patterns shaping the future., Methods: We conducted a thorough Scoping Review across PubMed, Embase, Web of Science, and Scopus, focusing on future trends in the pharmaceutical industry. Studies were carefully selected based on predefined criteria, ensuring a comprehensive and relevant collection., Results: We identified and selected 24 studies that met our predefined criteria from a pool of 3617 studies. These studies served as the foundation for a detailed exploration of trends within the pharmaceutical industry. This compilation offers a profound understanding of the diverse forces propelling change, including technological advancements, regulatory shifts, evolving market dynamics, and regional nuances., Conclusions: This research highlights transformative trends such as artificial intelligence, 3D printing, blockchain technology, and digital integration revolutionizing drug discovery, development, manufacturing, and delivery. However, successful implementation hinges on addressing challenges through practical strategies, interdisciplinary collaborations, and region-specific considerations. Future research should explore implementation approaches, facilitating the effective translation of these innovations into tangible healthcare benefits. This forward-looking analysis empowers the pharmaceutical industry to strategically position itself for long-term success in addressing evolving global needs., (© 2024. The Author(s), under exclusive licence to Tehran University of Medical Sciences.)

AI is revolutionizing medical diagnostics around the world, innovating in a variety of contexts, from leading US hospitals to facilities in developing countries. Below we present examples of AI implementations in medical diagnostics from different regions, taking into account the effectiveness and results of these solutions and forecasts for the development of this technology. Regarding the future of artificial intelligence in medical diagnostics, the article considered potential innovations such as the development of deep learning algorithms and integration with 5G technologies and the Internet. Attention is paid to the possibilities of further personalization of healthcare and to the challenges related to the need to adapt legal regulations and data management. It also indicates the directions of future research that may contribute to the further development of AI in medical diagnostics and the improvement of the quality of healthcare not only in Poland, but around the world.

Background: Continuous waveform monitoring is standard-of-care for patients at risk for or with critically illness. Derived from waveforms, heart rate, respiratory rate and blood pressure variability contain useful diagnostic and prognostic information; and when combined with machine learning, can provide predictive indices relating to severity of illness and/or reduced physiologic reserve. Integration of predictive models into clinical decision support software (CDSS) tools represents a potential evolution of monitoring., Methods: We perform a review and analysis of the multidisciplinary steps required to develop and rigorously evaluate predictive clinical decision support tools based on monitoring., Results: Development and evaluation of waveform-based variability-derived predictive models involves a multistep, multidisciplinary approach. The stepwise processes involves data science (data collection, waveform processing, variability analysis, statistical analysis, machine learning, predictive modelling), CDSS development (iterative research prototype evolution to commercial tool), and clinical research (observational and interventional implementation studies, followed by feasibility then definitive randomized controlled trials), and poses unique challenges (including technical, analytical, psychological, regulatory and commercial)., Conclusions: The proposed roadmap provides guidance for the development and evaluation of novel predictive CDSS tools with potential to help transform monitoring and improve care., Competing Interests: Declarations. Conflict interest: Dr. Andrew Seely is the founder and CEO of Therapeutic Monitoring Systems (TMS) which holds licensing rights to several patents related to variability-derived clinical decision support, where both Andrew Seely and Christophe Herry are patent authors related to this work. Dr. A. Seely discloses the conflict of interest (CoI) in all grants, publications, presentations, and other public communication, and submits a CoI management agreement to Ottawa Hospital Research Institute annually., (© 2024. The Author(s).)

Lung cancer (LC) remains one of the leading causes of cancer-related mortality worldwide. With recent technological advances, artificial intelligence (AI) has begun to play a crucial role in improving diagnostic and treatment methods. It is crucial to understand how AI has integrated into LC research and to identify the main areas of focus. The aim of the study was to provide an updated insight into the role of AI in LC research, analyzing evolving topics, geographical distribution, and contributions to journals. The study explores research trends in AI applied to LC through a novel approach combining latent Dirichlet allocation (LDA) topic modeling with the HJ-Biplot statistical technique. A growing interest in AI applications in LC oncology was observed, reflected in a significant increase in publications, especially after 2017, coinciding with the availability of computing resources. Frontiers in Oncology leads in publishing AI-related LC research, reflecting rigorous investigation in the field. Geographically, China and the United States lead in contributions, attributed to significant investment in R&D and corporate sector involvement. LDA analysis highlights key research areas such as pulmonary nodule detection, patient prognosis prediction, and clinical decision support systems, demonstrating the impact of AI in improving LC outcomes. DL and AI emerge as prominent trends, focusing on radiomics and feature selection, promising better decision-making in LC care. The increase in AI-driven research covers various topics, including data analysis methodologies, tumor characterization, and predictive methods, indicating a concerted effort to advance LC research. HJ-Biplot visualization reveals thematic clustering, illustrating temporal and geographical associations and highlighting the influence of high-impact journals and countries with advanced research capabilities. This multivariate approach offers insights into global collaboration dynamics and specialization, emphasizing the evolving role of AI in LC research and diagnosis., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Javier De La Hoz-M et al.)