Maternal health disparities are recognized as a significant public health challenge, with pronounced disparities evident across racial, socioeconomic, and geographic dimensions. Although healthcare technologies have advanced, these disparities remain primarily unaddressed, indicating that enhanced analytical approaches are needed.

This review aims to evaluate the impact of machine learning (ML) and statistical methods on identifying and addressing maternal health disparities and to outline future research directions for enhancing these methodologies.

Following the PRISMA guidelines, the review of studies employing ML and statistical methods to analyze maternal health disparities within the United States was conducted. Publications between January 1, 2012, and February 2024 were systematically searched through PubMed, Web of Science, and ScienceDirect. Inclusion criteria targeted studies conducted within the U.S., peer-reviewed articles published during the period, research covering the postpartum period up to one year post-delivery, and studies incorporating both maternal and infant health data with a focus primarily on maternal outcomes.

A total of 147 studies met the inclusion criteria for this analysis. Among these, 129 (88 %) utilized statistical methods in health sciences to analyze correlations, treatment effects, and public health initiatives, thus providing vital, actionable insights for policy and clinical decisions. Meanwhile, 18 articles (12 %) applied ML techniques to explore complex, nonlinear relationships in data. The findings indicate that while ML and statistical methods offer valuable insights into the factors contributing to health disparities, there are limitations regarding dataset diversity and methodological precision. Most studies concentrate on racial and socioeconomic inequalities, with fewer addressing the geographical aspects of maternal health. This review emphasizes the necessity for broader dataset utilization and methodology improvements to enhance the findings' predictive accuracy and applicability.

ML and statistical methods show great potential to transform maternal healthcare by identifying and addressing disparities. Future research should focus on broadening dataset diversity, improving methodological precision, and enhancing interdisciplinary efforts.

Artificial Intelligence (AI) has significantly reshaped Primary Health Care (PHC), offering various possibilities and complexities across all functional dimensions. The objective is to review and synthesize available evidence on the opportunities, challenges, and requirements of AI implementation in PHC based on the Primary Care Evaluation Tool (PCET).

We conducted a systematic review, following the Cochrane Collaboration method, to identify the latest evidence regarding AI implementation in PHC. A comprehensive search across eight databases- PubMed, Web of Science, Scopus, Science Direct, Embase, CINAHL, IEEE, and Cochrane was conducted using MeSH terms alongside the SPIDER framework to pinpoint quantitative and qualitative literature published from 2000 to 2024. Two reviewers independently applied inclusion and exclusion criteria, guided by the SPIDER framework, to review full texts and extract data. We synthesized extracted data from the study characteristics, opportunities, challenges, and requirements, employing thematic-framework analysis, according to the PCET model. The quality of the studies was evaluated using the JBI critical appraisal tools.

In this review, we included a total of 109 articles, most of which were conducted in North America (n = 49, 44%), followed by Europe (n = 36, 33%). The included studies employed a diverse range of study designs. Using the PCET model, we categorized AI-related opportunities, challenges, and requirements across four key dimensions. The greatest opportunities for AI integration in PHC were centered on enhancing comprehensive service delivery, particularly by improving diagnostic accuracy, optimizing screening programs, and advancing early disease prediction. However, the most challenges emerged within the stewardship and resource generation functions, with key concerns related to data security and privacy, technical performance issues, and limitations in data accessibility. Ensuring successful AI integration requires a robust stewardship function, strategic investments in resource generation, and a collaborative approach that fosters co-development, scientific advancements, and continuous evaluation.

Successful AI integration in PHC requires a coordinated, multidimensional approach, with stewardship, resource generation, and financing playing key roles in enabling service delivery. Addressing existing knowledge gaps, examining interactions among these dimensions, and fostering a collaborative approach in developing AI solutions among stakeholders are essential steps toward achieving an equitable and efficient AI-driven PHC system.

Registered in Open Science Framework (OSF) ( https://doi.org/10.17605/OSF.IO/HG2DV ).

Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citation metrics, prominent authors and journals, leading institutions, and significant contributing countries.

The 50 most cited articles on AI in ECG were published between 2000 and 2020 across 25 journals. The mean citations per article were 488.0, with the highest citations count being 1870. 'IEEE Transactions on Biomedical Engineering' and 'Computers in Biology and Medicine' published the highest number of articles, while Rajendra Acharya U and RS Tan were the most contributing authors. The USA and China had a total of 14 publications, and Singapore was the country with most collaborations.

This bibliometric analysis provides clinicians and researchers with an overview of evolution and progression of AI in the domain of ECG. Improved collaborations among different countries and institutions are essential for achieving advancements in the utilization of AI in ECG.

Interpreting skin findings can be challenging for both laypersons and clinicians. Large language models (LLMs) offer accessible decision support, yet their diagnostic capabilities for dermatological images remain underexplored. This study evaluated the diagnostic performance of LLMs based on image interpretation of common dermatological diseases.

A total of 500 dermatological images, encompassing four prevalent skin conditions (psoriasis, vitiligo, erysipelas and rosacea), were used to compare seven multimodal LLMs (GPT-4o, GPT-4o mini, Gemini 1.5 Pro, Gemini 1.5 Flash, Claude 3.5 Sonnet, Llama3.2 90B and 11B). A standardized prompt was used to generate one top diagnosis.

The highest overall accuracy was achieved by GPT-4o (67.8 %), followed by GPT-4o mini (63.8 %) and Llama3.2 11B (61.4 %). Accuracy varied considerably across conditions, with psoriasis with the highest mean LLM accuracy of 59.2 % and erysipelas demonstrating the lowest accuracy (33.4 %). 11.0 % of all images were misdiagnosed by all LLMs, whereas 11.6 % were correctly diagnosed by all models. Correct diagnoses by all LLMs were linked to clear, disease-specific features, such as sharply demarcated erythematous plaques in psoriasis. Llama3.2 90B was the only LLM to decline diagnosing images, particularly those involving intimate areas of the body.

LLM performance varied significantly, emphasizing the need for cautious usage. Notably, a free, locally hostable model correctly identified the top diagnosis for approximately two-thirds of all images, demonstrating the potential for safer, locally deployed LLMs. Advancements in model accuracy and the integration of clinical metadata could further enhance accessible and reliable clinical decision support systems.

Online health information is widely available, but a substantial portion of it is inaccurate or misleading, including exaggerated, incomplete, or unverified claims. Such misinformation can significantly influence public health decisions and pose serious challenges to health care systems. With advances in artificial intelligence and natural language processing, pretrained large language models (LLMs) have shown promise in identifying and distinguishing misleading health information, although their effectiveness in this area remains underexplored.

This study aimed to evaluate the performance of 4 mainstream LLMs (ChatGPT-3.5, ChatGPT-4, Ernie Bot, and iFLYTEK Spark) in the identification of online health information, providing empirical evidence for their practical application in this field.

Web scraping was used to collect data from rumor-refuting websites, resulting in 2708 samples of online health information, including both true and false claims. The 4 LLMs' application programming interfaces were used for authenticity verification, with expert results as benchmarks. Model performance was evaluated using semantic similarity, accuracy, recall, F1-score, content analysis, and credibility.

This study found that the 4 models performed well in identifying online health information. Among them, ChatGPT-4 achieved the highest accuracy at 87.27%, followed by Ernie Bot at 87.25%, iFLYTEK Spark at 87%, and ChatGPT-3.5 at 81.82%. Furthermore, text length and semantic similarity analysis showed that Ernie Bot had the highest similarity to expert texts, whereas ChatGPT-4 showed good overall consistency in its explanations. In addition, the credibility assessment results indicated that ChatGPT-4 provided the most reliable evaluations. Further analysis suggested that the highest misjudgment probabilities with respect to the LLMs occurred within the topics of food and maternal-infant nutrition management and nutritional science and food controversies. Overall, the research suggests that LLMs have potential in online health information identification; however, their understanding of certain specialized health topics may require further improvement.

The results demonstrate that, while these models show potential in providing assistance, their performance varies significantly in terms of accuracy, semantic understanding, and cultural adaptability. The principal findings highlight the models' ability to generate accessible and context-aware explanations; however, they fall short in areas requiring specialized medical knowledge or updated data, particularly for emerging health issues and context-sensitive scenarios. Significant discrepancies were observed in the models' ability to distinguish scientifically verified knowledge from popular misconceptions and in their stability when processing complex linguistic and cultural contexts. These challenges reveal the importance of refining training methodologies to improve the models' reliability and adaptability. Future research should focus on enhancing the models' capability to manage nuanced health topics and diverse cultural and linguistic nuances, thereby facilitating their broader adoption as reliable tools for online health information identification.

The objective of this study was to evaluate the concordance between therapeutic recommendations proposed by a multidisciplinary team meeting and those generated by a large language model (ChatGPT) for colorectal cancer. Although multidisciplinary teams represent the 'standard' for decision-making in cancer treatment, they require significant resources and may be susceptible to human bias. Artificial intelligence, particularly large language models such as ChatGPT, has the potential to enhance or optimize the decision-making processes. The present study examines the potential for integrating artificial intelligence into clinical practice by comparing multidisciplinary team decisions with those generated by ChatGPT.

A retrospective, single-centre study was conducted involving consecutive patients with newly diagnosed colorectal cancer discussed at our multidisciplinary team meeting. The pre- and post-therapeutic multidisciplinary team meeting recommendations were assessed for concordance compared with ChatGPT-4.

One hundred consecutive patients with newly diagnosed colorectal cancer of all stages were included. In the pretherapeutic discussions, complete concordance was observed in 72.5%, with partial concordance in 10.2% and discordance in 17.3%. For post-therapeutic discussions, the concordance increased to 82.8%; 11.8% of decisions displayed partial concordance and 5.4% demonstrated discordance. Discordance was more frequent in patients older than 77 years and with an American Society of Anesthesiologists classification ≥ III.

There is substantial concordance between the recommendations generated by ChatGPT and those provided by traditional multidisciplinary team meetings, indicating the potential utility of artificial intelligence in supporting clinical decision-making for colorectal cancer management.

Artificial Intelligence (AI)-based tools have shown potential to optimize clinical workflows, enhance patient quality and safety, and facilitate personalized treatment. However, transitioning viable AI solutions to clinical implementation remains limited. To understand the challenges of bringing AI into clinical practice, we explored the experiences of healthcare professionals, researchers, and Policy and Governance Experts in hospitals. We conducted a qualitative study with thirteen semi-structured interviews (mean duration 52.1 ± 5.4 minutes) with healthcare professionals, researchers, and Policy and Governance Experts, with prior experience on AI development in hospitals. The interview guide was based on value, application, technology, governance, and ethics from the Innovation Funnel for Valuable AI in Healthcare, and the discussions were analyzed through thematic analysis. Six themes emerged: (1) demand-pull vs. tech-push: AI development focusing on innovative technologies may face limited success in large-scale clinical implementation. (2) Focus on generating knowledge, not solutions: Current AI initiatives often generate knowledge without a clear path for implementing AI models once proof-of-concept is achieved. (3) Lack of multidisciplinary collaboration: Successful AI initiatives require diverse stakeholder involvement, often hindered by late involvement and challenging communication. (4) Lack of appropriate skills: Stakeholders, including IT departments and healthcare professionals, often lack the required skills and knowledge for effective AI integration in clinical workflows. (5) The role of the hospital: Hospitals need a clear vision for integrating AI, including meeting preconditions in infrastructure and expertise. (6) Evolving laws and regulations: New regulations can hinder AI development due to unclear implications but also enforce standardization, emphasizing quality and safety in healthcare. In conclusion, this study highlights the complexity of AI implementation in clinical settings. Multidisciplinary collaboration is essential and requires facilitation. Balancing divergent perspectives is crucial for successful AI implementation. Hospitals need to assess their readiness for AI, develop clear strategies, standardize development processes, and foster better collaboration among stakeholders.

This paper examines the integration of artificial intelligence (AI) into cancer screening programmes, focusing on the associated equity challenges and resource allocation implications. While AI technologies promise significant benefits-such as improved diagnostic accuracy, shorter waiting times, reduced reliance on radiographers, and overall productivity gains and cost-effectiveness-current interventions disproportionately favour those already engaged in screening. This neglect of non-attenders, who face the worst cancer outcomes, exacerbates existing health disparities and undermines the core objectives of screening programmes.Using breast cancer screening as a case study, we argue that AI interventions must not only improve health outcomes and demonstrate cost-effectiveness but also address inequities by prioritising non-attenders. To this end, we advocate for the design and implementation of cost-saving AI interventions. Such interventions could enable reinvestment into strategies specifically aimed at increasing engagement among non-attenders, thereby reducing disparities in cancer outcomes. Decision modelling is presented as a practical method to identify and evaluate these cost-saving interventions. Furthermore, the paper calls for greater transparency in decision-making, urging policymakers to explicitly account for the equity implications and opportunity costs associated with AI investments. Only then will they be able to balance the promise of technological innovation with the ethical imperative to improve health outcomes for all, particularly underserved populations. Methods such as distributional cost-effectiveness analysis are recommended to quantify and address disparities, ensuring more equitable healthcare delivery.

The health and social care needs of children and adults with disabilities are often neglected in many low- and middle-income countries. International opinion favours the creation of community-based supports rather than the institutional and clinic-based care that has dominated to date. However, models of care that are reliant on community leadership have been slow to develop within and across less affluent countries. Moreover, the managerial models inherent in institutional-based care are likely to be inadequate in such settings. This descriptive study aimed to explore the leadership qualities required in initiating and sustaining community-based supports. Face-to-face interviews were conducted with a purposeful sample of 16 leaders of projects in Africa, Asia, and South America. They included people with sensorial, physical, and intellectual disabilities as well as non-disabled leaders of local and national projects plus others whose leadership was at a regional or international level. Two main questions were addressed: what are the qualities required to function as a community leader and how can these qualities be nurtured in low resourced settings? The insights gained would inform the preparation and training of community leaders. Thematic content analysis identified three core themes: first, personal qualities such as empathy with an understanding of the personal circumstances of persons in need of support; second, communicating clearly the vision and values informing their work; and thirdly, building and mobilising community support from families and neighbours. The nurturing of leadership comes through mentoring and coaching, the empowerment of others, networking opportunities, and the development of inter-personal and communication skills. These themes were commonly expressed across the 16 leaders from all the participating nations and at all levels of responsibility, which suggests a universality of approach in relation to people with disabilities. The findings are in marked contrast to current practices in health and social care that have valued professional expertise over lived experience, knowledge, and technical skills over compassion and empathy, and the provision of person-centred "treatments" over developing community and personal self-reliance. Nonetheless, the challenges involved in establishing and sustaining new styles of leadership are many and will not be quickly resolved.

The aim of this study was to explore healthcare leaders' perceptions of the contribution of artificial intelligence (AI) to person-centred care (PCC).

The study had an explorative qualitative approach. Individual interviews were conducted from October 2020 to May 2021 with 26 healthcare leaders in a county council in Sweden. An abductive qualitative content analysis was conducted based on McCormack and McCance's framework of PCC. The four constructs (i.e. prerequisites, care environment, person-centred processes and expected outcomes) constituted the four categories for the deductive analysis. The inductive analysis generated 11 subcategories to the four constructs, representing how AI could contribute to PCC.

Healthcare leaders perceived that AI applications could contribute to the four PCC constructs through (a) supporting professional competence and establishing trust among healthcare professionals and patients (prerequisites); (b) including AI's ability to facilitate patient safety, enable proactive care, provide treatment recommendations and prioritise healthcare resources (the care environment); (c) including AI's ability to tailor information and promote the process of shared decision making and self-management (person-centred processes); and (d) including improving care quality and promoting health outcomes (expected outcomes).

The healthcare leaders perceived that AI applications could contribute to PCC at different levels of healthcare, thereby enhancing the quality of care and patients' health.

Artificial intelligence is the future of clinical practice and is increasingly utilized in medical management and clinical research. The release of ChatGPT3 in 2022 brought generative AI to the headlines and rekindled public interest in software agents that would complete repetitive tasks and save time. Artificial intelligence/machine learning underlies applications and devices which are assisting clinicians in the diagnosis, monitoring, formulation of prognosis, and treatment of patients with a spectrum of neuromuscular diseases. However, these applications have remained in the research sphere, and neurologists as a specialty are running the risk of falling behind other clinical specialties which are quicker to embrace these new technologies. While there are many comprehensive reviews on the use of artificial intelligence/machine learning in medicine, our aim is to provide a simple and practical primer to educate clinicians on the basics of machine learning. This will help clinicians specializing in neuromuscular and electrodiagnostic medicine to understand machine learning applications in nerve and muscle ultrasound, MRI imaging, electrical impendence myography, nerve conductions and electromyography and clinical cohort studies, and the limitations, pitfalls, regulatory and ethical concerns, and future directions. The question is not whether artificial intelligence/machine learning will change clinical practice, but when and how. How future neurologists will look back upon this period of transition will be determined not by how much changed or by how fast clinicians embraced this change but by how much patient outcomes were improved.

The increasing complexity and interconnectedness of healthcare data present numerous opportunities to improve prediction, diagnosis, and treatment. Graph-structured data, which represents entities and their relationships, is well-suited for modeling these complex connections. However, effectively utilizing this data often requires strong and efficient learning algorithms, especially when dealing with limited labeled data. Self-supervised learning (SSL) has emerged as a powerful paradigm for leveraging unlabeled data to learn effective representations. This paper presents a comprehensive review of SSL approaches specifically designed for graph-structured data in healthcare applications. We explore the challenges and opportunities associated with healthcare data and assess the effectiveness of SSL techniques in real-world healthcare applications. Our discussion encompasses various healthcare settings, such as disease prediction, medical image analysis, and drug discovery. We critically evaluate the performance of different SSL methods across these tasks, highlighting their strengths, limitations, and potential future research directions. To the best of our knowledge, this is the first comprehensive review of SSL applied to graph data in healthcare, providing valuable guidance for researchers and practitioners looking to leverage these techniques to enhance outcomes and drive progress in the field.

Artificial intelligence (AI) and novel technologies, such as remote sensors, robotics, and decision support algorithms, offer the potential for improving the health and well-being of older adults, but the priorities of key partners across the technology innovation continuum are not well understood.

To examine the priorities and suggested applications for AI and novel technologies for older adults among key partners.

This qualitative study comprised individual interviews using grounded theory conducted from May 24, 2023, to January 24, 2024. Recruitment occurred via referrals through the Johns Hopkins Artificial Intelligence and Technology Collaboratory for Aging Research. Participants included adults aged 60 years or older or their caregivers, clinicians, leaders in health systems or insurance plans (ie, payers), investors, and technology developers.

To assess priority areas, older adults, caregivers, clinicians, and payers were asked about the most important challenges faced by older adults and their caregivers, and investors and technology developers were asked about the most important opportunities associated with older adults and technology. All participants were asked for suggestions regarding AI and technology applications. Payers, investors, and technology developers were asked about end user engagement, and all groups except technology developers were asked about suggestions for technology development. Interviews were analyzed using qualitative thematic analysis. Distinct priority areas were identified, and the frequency and type of priority areas were compared by participant groups to assess the extent of overlap in priorities across groups.

Participants included 15 older adults or caregivers (mean age, 71.3 years [range, 65-93 years]; 4 men [26.7%]), 15 clinicians (mean age, 50.3 years [range, 33-69 years]; 8 men [53.3%]), 8 payers (mean age, 51.6 years [range, 36-65 years]; 5 men [62.5%]), 5 investors (mean age, 42.4 years [range, 31-56 years]; 5 men [100%]), and 6 technology developers (mean age, 42.0 years [range, 27-62 years]; 6 men [100%]). There were different priorities across key partners, with the most overlap between older adults or caregivers and clinicians and the least overlap between older adults or caregivers and investors and technology developers. Participants suggested novel applications, such as using reminders for motivating self-care or social engagement. There were few to no suggestions that addressed activities of daily living, which was the most frequently reported priority for older adults or caregivers. Although all participants agreed on the importance of engaging end users, engagement challenges included regulatory barriers and stronger influence of payers relative to other end users.

This qualitative interview study found important differences in priorities for AI and novel technologies for older adults across key partners. Public health, regulatory, and advocacy strategies are needed to raise awareness about these priorities, foster engagement, and align incentives to effectively use AI to improve the health of older adults.

Effective management of pediatric myopia, which includes treatments like corrective lenses and low-dose atropine, requires accurate clinical decisions. However, the complexity of pediatric refractive data, such as variations in visual acuity, axial length, and patient-specific factors, pose challenges to determining optimal treatment. This study aims to evaluate the performance of three large language models in analyzing these refractive data.

A dataset of 100 pediatric refractive records, including parameters like visual acuity and axial length, was analyzed using ChatGPT-3.5, ChatGPT-4o, and Wenxin Yiyan, respectively. Each model was tasked with determining whether intervention was needed and subsequently recommending a treatment (eyeglasses, orthokeratology lens, or low-dose atropine). The recommendations were compared to professional optometrists' consensus, rated on a 1-5 Global Quality Score (GQS) scale, and evaluated for clinical safety utilizing a three-tier accuracy assessment.

ChatGPT-4o outperformed both ChatGPT-3.5 and Wenxin Yiyan in determining intervention needs, with an accuracy of 90%, significantly higher than Wenxin Yiyan (p < 0.05). It also achieved the highest GQS of 4.4 ± 0.55, surpassing the other models (p < 0.001), with 85% of responses rated as "good" ahead of ChatGPT-3.5 (82%) and Wenxin Yiyan (74%). ChatGPT-4o made only eight errors in recommending interventions, fewer than ChatGPT-3.5 (12) and Wenxin Yiyan (15). Additionally, it performed better with incomplete or abnormal data, maintaining higher quality scores.

ChatGPT-4o showed better accuracy and clinical safety, making it a promising tool for decision support in pediatric ophthalmology, although expert oversight is still necessary.

This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks. Predictive analytics further enable personalized treatment plans by forecasting organ rejection, infection risks, and patient recovery trajectories, thereby supporting early intervention strategies and long-term patient management. AI also optimizes operational efficiency within transplant centers by predicting organ demand, scheduling surgeries efficiently, and managing inventory to minimize wastage, thus streamlining workflows and enhancing resource allocation. Despite these advancements, several challenges hinder the widespread adoption of AI and ML in organ transplantation. These include data privacy concerns, regulatory compliance issues, interoperability across healthcare systems, and the need for rigorous clinical validation of AI models. Addressing these challenges is essential to ensuring the reliable, safe, and ethical use of AI in clinical settings. Future directions for AI and ML in transplantation medicine include integrating genomic data for precision immunosuppression, advancing robotic surgery for minimally invasive procedures, and developing AI-driven remote monitoring systems for continuous post-transplantation care. Collaborative efforts among clinicians, researchers, and policymakers are crucial to harnessing the full potential of AI and ML, ultimately transforming transplantation medicine and improving patient outcomes while enhancing healthcare delivery efficiency.

Noninvasive prediction and identification of the H3K27M mutation play an important role in optimizing therapeutic strategies and improving outcomes in gliomas. In this systematic review and meta-analysis, we aimed to evaluate the performance of machine learning (ML)-based models in predicting H3K27M mutation in gliomas.

Literature records were retrieved on September 16th, 2024, in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

A total of 15 studies were included in our study. Our meta-analysis demonstrated a pooled AUC, sensitivity, and specificity of 0.87 (95% CI: 0.77-0.97), 92% (95% CI: 83%-96%), and 89% (95% CI: 86%-91%)), respectively. The subgroup meta-analysis revealed that despite the higher sensitivity of the deep learning (DL) models, the sensitivity is not superior to ML (P = 0.6). In contrast, the ML-based pooled specificity was significantly higher (P < 0.01). The meta-analysis revealed a 78.1 (95% CI: 33.3 - 183.5). The SROC curve indicated an AUC of 0.921, and the estimated sensitivity is 0.898 concurrent with the false positive rate of 0.126, which indicates high sensitivity with a low false positive rate.

Our systematic review and meta-analysis demonstrated that ML-based magnetic resonance imaging (MRI) radiomics models are associated with promising diagnostic performance in predicting H3K27M mutation in gliomas.

Methods to adopt artificial intelligence (AI) in healthcare clinical practice remain unclear. The potential for rapid integration of AI-enabled technologies across healthcare settings coupled with the growing digital divide in the health sector highlights the need to examine AI use by health professionals, especially in allied health disciplines with emerging AI use such as physiotherapy, occupational therapy, speech pathology, podiatry and dietetics. This protocol details the methodology for a scoping review on the use of AI-enabled technology in sectors of the allied health workforce. The research question is 'How is AI used by sectors of the allied health workforce to improve patient safety, quality of care and outcomes, and what is the quality of evidence supporting this use?'

The review will follow the Joanna Briggs Institute scoping review guidelines. Databases will be searched from 17 to 24 March 2025 and will include PubMed/Medline, Embase, PsycINFO and Cummulative Index to Nursing and Allied Health Literature databases. Dual screening against inclusion criteria will be applied for study selection. Peer-reviewed articles reporting primary research in allied healthcare published in English within the last 10 years will be included. Studies will be evaluated using the Quality Assessment with Diverse Studies tool. The review will map the existing literature and identify key themes related to the use of AI in the disciplines of physiotherapy, occupational therapy, speech pathology, podiatry and dietetics.

No ethics approval will be sought, as only secondary research outputs will be used. Findings will be disseminated through peer-reviewed publication and presentations at workshops and conferences.

Open Science Framework Protocol Registration https://osf.io/r7t4s.

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

Background: Chatbots based on artificial intelligence (AI) and machine learning are rapidly growing in popularity. Patients may use these technologies to ask questions regarding surgical interventions, preoperative assessments, and postoperative outcomes. The aim of this study was to determine whether ChatGPT could appropriately answer some of the most frequently asked questions posed by patients about lung cancer surgery. Methods: Sixteen frequently asked questions about lung cancer surgery were asked to the chatbot in one conversation, without follow-up questions or repetition of the same questions. Each answer was evaluated for appropriateness and accuracy using an evidence-based approach by a panel of specialists with relevant clinical experience. The responses were assessed using a four-point Likert scale (i.e., "strongly agree, satisfactory", "agree, requires minimal clarification", "disagree, requires moderate clarification", and "strongly disagree, requires substantial clarification"). Results: All answers provided by the chatbot were judged to be satisfactory, evidence-based, and generally unbiased overall, seldomly requiring minimal clarification. Moreover, information was delivered in a language deemed easy-to-read and comprehensible to most patients. Conclusions: ChatGPT could effectively provide evidence-based answers to the most commonly asked questions about lung cancer surgery. The chatbot presented information in a language considered understandable by most patients. Therefore, this resource may be a valuable adjunctive tool for preoperative patient education.

The development of machine learning (ML) tools in many different medical settings is largely increasing. However, the use of the resulting algorithms in daily medical practice is still an unsolved challenge. We propose an epistemological approach (i.e., based on logical principles) to the application of computational tools in clinical practice. We rely on the classification of scientific inference into deductive, inductive, and abductive comparing the characteristics of ML tools with those derived from evidence-based medicine [EBM] and experience-based medicine, as paradigms of well-known methods for generation of knowledge. While we illustrate our arguments using liver transplantation as an example, this approach can be applied to other aspects of the specialty. Regarding EBM, it generates general knowledge that clinicians apply deductively, but the certainty of its conclusions is not guaranteed. In contrast, automatic algorithms primarily rely on inductive reasoning. Their design enables the integration of vast datasets and mitigates the emotional biases inherent in human induction. However, its poor capacity for abductive inference (a logical mechanism inherent to human clinical experience) constrains its performance in clinical settings characterized by uncertainty, where data are heterogeneous, results are highly influenced by context, or where prognostic factors can change rapidly.

Integrating artificial intelligence (AI) into clinical trials for inflammatory bowel disease (IBD) has potential to be transformative to the field. This article explores how AI-driven technologies, including machine learning (ML), natural language processing, and predictive analytics, have the potential to enhance important aspects of IBD trials-from patient recruitment and trial design to data analysis and personalized treatment strategies. As AI advances, it has potential to improve long-standing challenges in trial efficiency, accuracy, and personalization with the goal of accelerating the discovery of novel therapies and improve outcomes for people living with IBD. AI can streamline multiple trial phases, from target identification and patient recruitment to data analysis and monitoring. By integrating multi-omics data, electronic health records, and imaging repositories, AI can uncover molecular targets and personalize trial strategies, ultimately expediting drug development. However, the adoption of AI in IBD clinical trials encounters significant challenges. These include technical barriers in data integration, ethical concerns regarding patient privacy, and regulatory issues related to AI validation standards. Additionally, AI models risk producing biased outcomes if training datasets lack diversity, potentially impacting underrepresented populations in clinical trials. Addressing these limitations requires standardized data formats, interdisciplinary collaboration, and robust ethical frameworks to ensure inclusivity and accuracy. Continued partnerships among clinicians, researchers, data scientists, and regulators will be essential to establish transparent, patient-centered AI frameworks. By overcoming these obstacles, AI has the potential to enhance the efficiency, equity, and efficacy of IBD clinical trials, ultimately benefiting patient care.

Remote Patient Monitoring (RPM) has driven significant advancements in Canadian healthcare, especially during the transformative period from 2018 to 2023. This perspective article explores the state of play and examines the current landscape of RPM platforms adopted across Canada, detailing their functionalities and measurable impacts on healthcare outcomes, particularly in chronic disease management and hospital readmission reduction. We explore the regulatory, technical, and operational challenges that RPM faces, including critical issues around data privacy, security, and interoperability, factors essential for sustainable integration. Additionally, this article provides a balanced analysis of RPM's potential for continued growth within Canadian healthcare, highlighting its strengths and limitations in the post-2023 context and offering strategic recommendations to guide its future development. Keywords: Remote Patient Monitoring, Digital Health, Virtual Care, Canadian Healthcare, Healthcare Technology, AI, Perspectives.

This article analyses the integration of artificial intelligence (AI) in health pastoral care, emphasizing the synergy between technology and spirituality. This paper discusses possible AI applications, highlighting the importance of ethical implementation that respects human interactions. Ethical issues like privacy and empathy are examined, as well as the potential of AI in facilitating collaboration between healthcare professionals and pastoral workers. Finally, it calls for a debate on the responsible use of AI in care contexts.

This article aims to evaluate the use and effects of an artificial intelligence system supporting a critical diagnostic task during radiology resident training, addressing a research gap in this field.

We involved eight residents evaluating 150 CXRs in three scenarios: no AI, on-demand AI, and integrated-AI. The considered task was the assessment of a multi-regional severity score of lung compromise in patients affected by COVID-19. The chosen artificial intelligence tool, fully integrated in the RIS/PACS, demonstrated superior performance in scoring compared to the average radiologist. Using quantitative metrics and questionnaires, we measured the 'upskilling' effects of using AI support and residents' resilience to 'deskilling,' i.e., their ability to overcome AI errors.

Residents required AI in 70% of cases when left free to choose. AI support significantly reduced severity score errors and increased inter-rater agreement by 22%. Residents were resilient to AI errors above an acceptability threshold. Questionnaires indicated high tool usefulness, reliability, and explainability, with a preference for collaborative AI scenarios.

With this work, we gathered quantitative and qualitative evidence of the beneficial use of a high-performance AI tool that is well integrated into the diagnostic workflow as a training aid for radiology residents.

Balancing educational benefits and deskilling risks is essential to exploit AI systems as effective learning tools in radiology residency programs. Our work highlights metrics for evaluating these aspects.

Insights into AI tools' effects in radiology resident training are lacking. Metrics were defined to observe residents using an AI tool in different settings. This approach is advisable for evaluating AI tools in radiology training.

The aim of this study was to investigate the perceptions of health profession students regarding ChatGPT use and the potential impact of integrating ChatGPT in healthcare and education.

Artificial Intelligence is increasingly utilized in medical education and clinical profession training. However, since its introduction, ChatGPT remains relatively unexplored in terms of health profession students' acceptance of its use in education and practice.

This study employed a mixed-methods approach, using a web-based survey.

The study involved a convenience sample recruited through various methods, including Faculty of Medicine announcements, social media, and snowball sampling, during the second semester (March to June 2023). Data were collected using a structured questionnaire with closed-ended questions and three open-ended questions. The final sample comprised 217 undergraduate health profession students, including 73 (33.6%) nursing students, 65 (30.0%) medical students, and 79 (36.4%) occupational therapy, physiotherapy, and speech therapy students.

Among the surveyed students, 86.2% were familiar with ChatGPT, with generally positive perceptions as reflected by a mean score of 4.04 (SD = 0.62) on a scale of 1 to 5. Positive feedback was particularly noted with respect to ChatGPT's role in information retrieval and summarization. The qualitative data revealed three main themes: experiences with ChatGPT, its impact on the quality of healthcare, and its integration into the curriculum. The findings highlight benefits such as serving as a convenient tool for accessing information, reducing human errors, and fostering innovative learning approaches. However, they also underscore areas of concern, including ethical considerations, challenges in fostering critical thinking, and issues related to verification. The absence of significant differences between the different fields of study indicates consistent perceptions across nursing, medicine, and other health profession students.

Our findings underscore the necessity for continuous refinement to enhance ChatGPT's accuracy, reliability, and alignment with the diverse educational needs of health professions. These insights not only deepen our understanding of student perceptions of ChatGPT in healthcare education but also have significant implications for the future integration of AI in health profession practice. The study emphasizes the importance of a careful balance between leveraging the benefits of AI tools and addressing ethical and pedagogical concerns.

The escalating global scarcity of skilled health care professionals is a critical concern, further exacerbated by rising stress levels and clinician burnout rates. Artificial intelligence (AI) has surfaced as a potential resource to alleviate these challenges. Nevertheless, it is not taken for granted that AI will inevitably augment human performance, as ill-designed systems may inadvertently impose new burdens on health care workers, and implementation may be challenging. An in-depth understanding of how AI can effectively enhance rather than impair work conditions is therefore needed.

This research investigates the efficacy of AI in alleviating stress and enriching work conditions, using intensive care units (ICUs) as a case study. Through a sociotechnical system lens, we delineate how AI systems, tasks, and responsibilities of ICU nurses and physicians can be co-designed to foster motivating, resilient, and health-promoting work.

We use the sociotechnical system framework COMPASS (Complementary Analysis of Sociotechnical Systems) to assess 5 job characteristics: autonomy, skill diversity, flexibility, problem-solving opportunities, and task variety. The qualitative analysis is underpinned by extensive workplace observation in 6 ICUs (approximately 559 nurses and physicians), structured interviews with work unit leaders (n=12), and a comparative analysis of data science experts' and clinicians' evaluation of the optimal levels of human-AI teaming.

The results indicate that AI holds the potential to positively impact work conditions for ICU nurses and physicians in four key areas. First, autonomy is vital for stress reduction, motivation, and performance improvement. AI systems that ensure transparency, predictability, and human control can reinforce or amplify autonomy. Second, AI can encourage skill diversity and competence development, thus empowering clinicians to broaden their skills, increase the polyvalence of tasks across professional boundaries, and improve interprofessional cooperation. However, careful consideration is required to avoid the deskilling of experienced professionals. Third, AI automation can expand flexibility by relieving clinicians from administrative duties, thereby concentrating their efforts on patient care. Remote monitoring and improved scheduling can help integrate work with other life domains. Fourth, while AI may reduce problem-solving opportunities in certain areas, it can open new pathways, particularly for nurses. Finally, task identity and variety are essential job characteristics for intrinsic motivation and worker engagement but could be compromised depending on how AI tools are designed and implemented.

This study demonstrates AI's capacity to mitigate stress and improve work conditions for ICU nurses and physicians, thereby contributing to resolving health care staffing shortages. AI solutions that are thoughtfully designed in line with the principles for good work design can enhance intrinsic motivation, learning, and worker well-being, thus providing strategic value for hospital management, policy makers, and health care professionals alike.

Patients with nasopharyngeal carcinoma (NPC) are prone to malnutrition, which leads to deterioration of health. This study is to clarify the effect of Artificial intelligence (AI)-assisted home enteral nutrition (HEN) management mode on the health status of patients with stage III to stage IV NPC after 3 years of treatment, and to provide a new strategy for improving the quality of life of patients.

Patients with stage III ~ IV NPC were determined whether to accept AI-assisted HEN management according to voluntary principle. After 3 years of management, the survival rate, distant metastasis rate and local recurrence rate were counted, and the basic body quality, laboratory detection, eating difficulty score, mental health score and other examinations were performed on the surviving patients to evaluate the overall health status.

The three-year survival rate of patients with NPC who received AI-assisted HEN management after treatment was improved. Various tests showed that AI-assisted HEN improved the nutritional intake of patients, had a low positive rate of Epstein-Barr virus, reduced adverse reactions such as psychological stress and physical pain, and could improve the quality of life of patients.

AI-assisted HEN has a positive auxiliary effect on clinical treatment, which is helpful to promote the recovery of patients with NPC.

NCT06603909.

The Ki-67 index is a histopathological marker that has been reported to be a crucial factor in the biological behavior and prognosis of meningiomas. Several studies have developed artificial intelligence (AI) models to predict the Ki-67 based on radiomics. In this study, we aimed to perform a systematic review and meta-analysis of AI models that predicted the Ki-67 index in meningioma.

Literature records were retrieved on April 27, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from included studies were extracted. The quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

Our study included 6 studies. The mean Ki-67 ranged from 2.7 ± 2.97 to 4.8 ± 40.3. Of 6 studies, 5 utilized a machine learning method. The most used AI method was the least absolute shrinkage and selection operator. The area under the curve and accuracy ranged from 0.83 to 0.99 and 0.81 to 0.95, respectively. AI models demonstrated a pooled sensitivity of 87.5% (95% confidence interval [CI]: 75.2%, 94.2%), a specificity of 86.9% (95% CI: 75.8%, 93.4%), and a diagnostic odds ratio of 40.02 (95% CI: 13.5, 156.4). The summary receiver operating characteristic curve indicated an area under the curve of 0.931 for the prediction of Ki-67 index status in intracranial meningiomas.

AI models have demonstrated promising performance for predicting the Ki-67 index in meningiomas and can optimize the treatment strategy.

Artificial intelligence (AI) represents an exciting and evolving technology that is increasingly being utilized across pain medicine. Large language models (LLMs) are one type of AI that has become particularly popular. Currently, there is a paucity of literature analyzing the impact that AI may have on trainee education. As such, we sought to assess the benefits and pitfalls that AI may have on pain medicine trainee education. Given the rapidly increasing popularity of LLMs, we particularly assessed how these LLMs may promote and hinder trainee education through a pilot quality improvement project.

A comprehensive search of the existing literature regarding AI within medicine was performed to identify its potential benefits and pitfalls within pain medicine. The pilot project was approved by UPMC Quality Improvement Review Committee (#4547). Three of the most commonly utilized LLMs at the initiation of this pilot study - ChatGPT Plus, Google Bard, and Bing AI - were asked a series of multiple choice questions to evaluate their ability to assist in learner education within pain medicine.

Potential benefits of AI within pain medicine trainee education include ease of use, imaging interpretation, procedural/surgical skills training, learner assessment, personalized learning experiences, ability to summarize vast amounts of knowledge, and preparation for the future of pain medicine. Potential pitfalls include discrepancies between AI devices and associated cost-differences, correlating radiographic findings to clinical significance, interpersonal/communication skills, educational disparities, bias/plagiarism/cheating concerns, lack of incorporation of private domain literature, and absence of training specifically for pain medicine education. Regarding the quality improvement project, ChatGPT Plus answered the highest percentage of all questions correctly (16/17). Lowest correctness scores by LLMs were in answering first-order questions, with Google Bard and Bing AI answering 4/9 and 3/9 first-order questions correctly, respectively. Qualitative evaluation of these LLM-provided explanations in answering second- and third-order questions revealed some reasoning inconsistencies (e.g., providing flawed information in selecting the correct answer).

AI represents a continually evolving and promising modality to assist trainees pursuing a career in pain medicine. Still, limitations currently exist that may hinder their independent use in this setting. Future research exploring how AI may overcome these challenges is thus required. Until then, AI should be utilized as supplementary tool within pain medicine trainee education and with caution.

The application of artificial intelligence (AI) to healthcare in Africa has the potential to transform productivity, diagnosis, disease surveillance, and resource allocation by improving accuracy and efficiency. However, to fully realize its benefits, it is necessary to consider issues concerning data privacy, equity, infrastructure integration, and ethical policy development. The use of these tools may improve the detection of diseases, the distribution of resources, and the continuity of care. The use of AI allows for the development of policies that are tailored to address health disparities based on evidence. While AI may increase accessibility and affordability through telehealth, remote monitoring, and cost reductions, significant barriers remain. Ethical guidelines are needed to ensure AI decisions align with medical standards and patient autonomy. Strict privacy and security controls are crucial to protecting sensitive health data. This article evaluates the current and potential roles of AI in the African health sector. It identifies opportunities to address challenges through tailored interventions and an AI framework to simulate policy impacts.

The potential of artificial intelligence (AI) to significantly transform numerous aspects of contemporary civilization is substantial. Advancements in research show an increasing interest in creating AI solutions in the healthcare sector. This interest is driven by the broad spectrum and extensive nature of easily accessible patient data-including medical imaging, digitized data collection, and electronic health records - and by the ability to analyze and interpret complex data, facilitating more accurate and timely diagnoses. This review's goal is to provide a comprehensive overview of the advancements achieved by AI in healthcare, to elucidate the present state of AI in enhancing the healthcare system and improving the quality and efficiency of healthcare decision making, and to discuss selected medical applications of AI. Furthermore, the barriers and constraints that may impede the use of AI in healthcare are outlined, and the potential future directions of AI-augmented healthcare systems are discussed.

Artificial Intelligence (AI) is being used in every aspect of life today. It has found great application in the healthcare sector, with the use of this technology by medical schools all over the globe. AI has found multiple applications in medical fields such as diagnostics, medicine, surgery, oncology, radiology, ophthalmology, medical education, and numerous other medical fields. It has assisted in diagnosing conditions in a much quicker and more efficient manner, and the use of AI chatbots has greatly enhanced the learning process. Despite the benefits that AI applications provide, such as saving precious time for healthcare givers, there are also concerns regarding AI, mainly, ethical, and the fact that they might render the human race unemployed. However, despite these concerns, a lot of innovations are being made using AI applications, which show a very bright prospect for this technology. Although humans use AI in every part of their daily lives, they are also opposed to its use because they believe it could eventually replace them in the future. In this review of literature, a detailed analysis of the use of AI in the healthcare industry and medical education will be done, along with its shortcomings as well as its future prospects.

Artificial Intelligence (AI) holds immense potential for revolutionizing medical education and healthcare. Despite its proven benefits, the full integration of AI faces hurdles, with ethical concerns standing out as a key obstacle. Thus, educators should be equipped to address the ethical issues that arise and ensure the seamless integration and sustainability of AI-based interventions. This article presents twelve essential tips for addressing the major ethical concerns in the use of AI in medical education. These include emphasizing transparency, addressing bias, validating content, prioritizing data protection, obtaining informed consent, fostering collaboration, training educators, empowering students, regularly monitoring, establishing accountability, adhering to standard guidelines, and forming an ethics committee to address the issues that arise in the implementation of AI. By adhering to these tips, medical educators and other stakeholders can foster a responsible and ethical integration of AI in medical education, ensuring its long-term success and positive impact.

Artificial intelligence (AI) technology is being used in various fields and its use is increasingly expanding in dentistry. The key aspects of AI include machine learning (ML), deep learning (DL), and neural networks (NNs). The aim of this review is to present an overview of AI, its various aspects, and its application in biomedicine, dentistry, and dental biomaterials focusing on restorative dentistry and prosthodontics. AI-based systems can be a complementary tool in diagnosis and treatment planning, result prediction, and patient-centered care. AI software can be used to detect restorations, prosthetic crowns, periodontal bone loss, and root canal segmentation from the periapical radiographs. The integration of AI, digital imaging, and 3D printing can provide more precise, durable, and patient-oriented outcomes. AI can be also used for the automatic segmentation of panoramic radiographs showing normal anatomy of the oral and maxillofacial area. Recent advancement in AI in medical and dental sciences includes multimodal deep learning fusion, speech data detection, and neuromorphic computing. Hence, AI has helped dentists in diagnosis, planning, and aid in providing high-quality dental treatments in less time.

Background and Objectives: The use of artificial intelligence (AI) and, in particular, machine learning (ML) techniques is growing rapidly in the healthcare field. Their application in pharmacokinetics is of potential interest due to the need to relate enormous amounts of data and to the more efficient development of new predictive dose models. The development of pharmacokinetic models based on these techniques simplifies the process, reduces time, and allows more factors to be considered than with classical methods, and is therefore of special interest in the pharmacokinetic monitoring of antibiotics. This review aims to describe the studies that use AI, mainly oriented to ML techniques, for dose prediction and analyze their results in comparison with the results obtained by classical methods. Furthermore, in the review, the techniques employed and the metrics to evaluate the precision are described to improve the compression of the results. Methods: A systematic search was carried out in the EMBASE, OVID, and PubMed databases and the results obtained were analyzed in detail. Results: Of the 13 articles selected, 10 were published in the last three years. Vancomycin was monitored in seven and none of the studies were performed on new antibiotics. The most used techniques were XGBoost and neural networks. Comparisons were conducted in most cases against population pharmacokinetic models. Conclusions: AI techniques offer promising results. However, the diversity in terms of the statistical metrics used and the low power of some of the articles make the overall assessment difficult. For now, AI-based ML techniques should be used in addition to classical population pharmacokinetic models in clinical practice.

Artificial Intelligence (AI) applications in healthcare are growing exponentially. The field of neurosurgery is particularly suited to implement AI solutions given its technology-driven nature. It is of paramount importance to understand the basics of AI to make informed decision on how to shape current and future applications.

What is the level of confidence, knowledge and the attitude of the global neurosurgical community towards AI basic concepts and applications?

A 24-item survey was designed and distributed. The survey results reported on level of knowledge, confidence and interest in AI, perspectives and attitude towards the application of AI technologies in neurosurgery. The potential influence of demographics and work-related environment features on AI knowledge was investigated.

We received a total of 250 responses from 61 countries. The correct definition of 'Machine Learning', 'Deep Learning' and main Big Data features were identified by respectively 42%, 23% and 23% of the respondents. The survey unveiled a strong interest and a positive attitude towards the introduction of AI in the neurosurgical practice. The main concerns included trustworthiness and liability, the main barriers to implementation were considered lack of funding, infrastructure, knowledge and multidisciplinary collaboration.

There is a low familiarity with basic AI concepts in the neurosurgical community. Nevertheless, there is a strong interest and a positive attitude towards AI implementation. The systematization of training and the production of educational resources will be key in guaranteeing a successful implementation of AI in the evolving history of neurosurgery.

Lymph node metastasis (LNM) is often regarded as an indicator of poor prognosis in various cancers. Despite over three centuries of exploration since its discovery, the molecular mechanisms underlying LNM remain inconclusive. This review summarizes the molecular mechanisms of LNM, using the "PUMP+" principle for clarification. Pathological examination remains the gold standard for LNM diagnosis, yet there is a need to explore early diagnostic strategies that can effectively improve patient outcomes. With the advent of immunotherapy, discussions on the fate of lymph nodes (LN) have emerged, emphasizing the importance of preserving LN integrity prior to immunotherapy. This, in turn, poses higher demands for diagnostic accuracy and precision treatment of LNM. This review comprehensively discusses the molecular mechanisms, diagnostic methods, and treatment strategies for cancer lymph node metastasis, along with current bottlenecks and future directions in this field.

This study aims to create a vision of the future hospital to help healthcare leaders understand how changes in society and the healthcare system, compounded by climate change, could affect future hospital estate.

The study is part of a larger project based on participatory backcasting aimed at providing integrated strategies for transitioning to a zero-carbon future and adapting to existing climate change through improved asset management.

The data presented in this paper were collected during a full-day workshop to construct the vision of the future hospital in 2050. A multidisciplinary team of 19 participants participated in the discussions. A six-phase thematic analysis was applied to the data to develop the narrative vision and graphic recording.

The healthcare system is undergoing transformative changes due to evolving healthcare delivery, patient expectations, emerging technologies, climate change, and sustainability. However, current hospital strategies often fail to consider the interrelationship between the hospital estate and its socio-environmental context. Policymakers, healthcare system leaders, and hospital leaders need a clear vision of the hospital of the future to implement transformational strategies.

Healthcare transformations require shifting from traditional centralised hospitals to a more flexible, distributed model. Healthcare leaders need to proactively assess how hospitals respond to current and future hazards and consider the impacts within the context of integrated and dispersed healthcare delivery. To address this, a systematic approach to modelling hazards and evaluating design or upgrading options is essential to mitigate the transfer of climate-related risks within healthcare systems.

Artificial intelligence (AI) applications in radiotherapy (RT) are expected to save time and improve quality, but implementation remains limited. Therefore, we used implementation science to develop a format for designing an implementation strategy for AI. This study aimed to (1) apply this format to develop an AI implementation strategy for our center; (2) identify insights gained to enhance AI implementation using this format; and (3) assess the feasibility and acceptability of this format to design a center-specific implementation strategy for departments aiming to implement AI.

We created an AI-implementation strategy for our own center using implementation science methods. This included a stakeholder analysis, literature review, and interviews to identify facilitators and barriers, and designed strategies to overcome the barriers. These methods were subsequently used in a workshop with teams from seven Dutch RT centers to develop their own AI-implementation plans. The applicability, appropriateness, and feasibility were evaluated by the workshop participants, and relevant insights for AI implementation were summarized.

The stakeholder analysis identified internal (physicians, physicists, RT technicians, information technology, and education) and external (patients and representatives) stakeholders. Barriers and facilitators included concerns about opacity, privacy, data quality, legal aspects, knowledge, trust, stakeholder involvement, ethics, and multidisciplinary collaboration, all integrated into our implementation strategy. The workshop evaluation showed high acceptability (18 participants [90%]), appropriateness (17 participants [85%]), and feasibility (15 participants [75%]) of the implementation strategy. Sixteen participants fully agreed with the format.

Our study highlights the need for a collaborative approach to implement AI in RT. We designed a strategy to overcome organizational challenges, improve AI integration, and enhance patient care. Workshop feedback indicates the proposed methods are useful for multiple RT centers. Insights gained by applying the methods highlight the importance of multidisciplinary collaboration in the development and implementation of AI.

The integration of artificial intelligence (AI) into health care has the potential to transform the industry, but it also raises ethical, regulatory, and safety concerns. This review paper provides an in-depth examination of the benefits and risks associated with AI in health care, with a focus on issues like biases, transparency, data privacy, and safety.

This study aims to evaluate the advantages and drawbacks of incorporating AI in health care. This assessment centers on the potential biases in AI algorithms, transparency challenges, data privacy issues, and safety risks in health care settings.

Studies included in this review were selected based on their relevance to AI applications in health care, focusing on ethical, regulatory, and safety considerations. Inclusion criteria encompassed peer-reviewed articles, reviews, and relevant research papers published in English. Exclusion criteria included non-peer-reviewed articles, editorials, and studies not directly related to AI in health care. A comprehensive literature search was conducted across 8 databases: OVID MEDLINE, OVID Embase, OVID PsycINFO, EBSCO CINAHL Plus with Full Text, ProQuest Sociological Abstracts, ProQuest Philosopher's Index, ProQuest Advanced Technologies & Aerospace, and Wiley Cochrane Library. The search was last updated on June 23, 2023. Results were synthesized using qualitative methods to identify key themes and findings related to the benefits and risks of AI in health care.

The literature search yielded 8796 articles. After removing duplicates and applying the inclusion and exclusion criteria, 44 studies were included in the qualitative synthesis. This review highlights the significant promise that AI holds in health care, such as enhancing health care delivery by providing more accurate diagnoses, personalized treatment plans, and efficient resource allocation. However, persistent concerns remain, including biases ingrained in AI algorithms, a lack of transparency in decision-making, potential compromises of patient data privacy, and safety risks associated with AI implementation in clinical settings.

In conclusion, while AI presents the opportunity for a health care revolution, it is imperative to address the ethical, regulatory, and safety challenges linked to its integration. Proactive measures are required to ensure that AI technologies are developed and deployed responsibly, striking a balance between innovation and the safeguarding of patient well-being.

Recent studies have identified significant gaps in equity, diversity, and inclusion (EDI) considerations within the lifecycle of artificial intelligence (AI), spanning from data collection and problem definition to implementation stages. Despite the recognized need for integrating EDI principles, there is currently no existing guideline or framework to support this integration in the AI lifecycle.

This study aimed to address this gap by identifying EDI principles and indicators to be integrated into the AI lifecycle. The goal was to develop a comprehensive guiding framework to guide the development and implementation of future AI systems.

This study was conducted in 3 phases. In phase 1, a comprehensive systematic scoping review explored how EDI principles have been integrated into AI in health and oral health care settings. In phase 2, a multidisciplinary team was established, and two 2-day, in-person international workshops with over 60 representatives from diverse backgrounds, expertise, and communities were conducted. The workshops included plenary presentations, round table discussions, and focused group discussions. In phase 3, based on the workshops' insights, the EDAI framework was developed and refined through iterative feedback from participants. The results of the initial systematic scoping review have been published separately, and this paper focuses on subsequent phases of the project, which is related to framework development.

In this study, we developed the EDAI framework, a comprehensive guideline that integrates EDI principles and indicators throughout the entire AI lifecycle. This framework addresses existing gaps at various stages, from data collection to implementation, and focuses on individual, organizational, and systemic levels. Additionally, we identified both the facilitators and barriers to integrating EDI within the AI lifecycle in health and oral health care.

The developed EDAI framework provides a comprehensive, actionable guideline for integrating EDI principles into AI development and deployment. By facilitating the systematic incorporation of these principles, the framework supports the creation and implementation of AI systems that are not only technologically advanced but also sensitive to EDI principles.

Postoperative cerebrospinal fluid (CSF) leakage is the leading adverse event in transsphenoidal surgery. Intraoperative CSF (ioCSF) leakage is one of the most important predictive factors for postoperative CSF leakage. This systematic review and meta-analysis aimed to evaluate the effectiveness of artificial intelligence (AI) models in predicting ioCSF.

Literature records were retrieved on June 13, 2024, using the relevant key terms without filters in PubMed, Embase, Scopus, and Web of Science. Records were screened according to the eligibility criteria, and the data from the included studies were extracted. The quality assessment was performed using the Quality Assessment of Diagnostic Accuracy Studies-2 tool. The meta-analysis, sensitivity analysis, and meta-regression were conducted using R software.

Our results demonstrate that the AI models achieved a pooled sensitivity of 93.4% (95% confidence interval [CI]: 74.8%-98.6%) and specificity of 91.7% (95% CI: 75%-97.6%). The subgroup analysis revealed that the pooled sensitivities in machine learning and deep learning were 86.2% (95% CI: 83%-88.8%) and 99% (95% CI: 93%-99%), respectively (P < 0.01). The subgroup analysis demonstrated a pooled specificity of 92.1% (95% CI: 63.1%-98.7%) for machine learning and 90.6% (95% CI: 78.2%-96.3%) for deep learning models (P = 0.87). The diagnostic odds ratio meta-analysis revealed an odds ratio 114.6 (95% CI: 17.6-750.9). The summary receiver operating characteristic curve demonstrated that the overall area under the curve of the studies was 0.955, which is a considerable performance.

AI models have demonstrated promising performance for predicting the ioCSF leakage in pituitary surgery and can optimize the treatment strategy.