BACKGROUND: This report evaluates the potential of artificial intelligence (AI) in psychodermatology, emphasizing its ability to enhance diagnostic accuracy, treatment efficacy, and personalized care. Psychodermatology, which explores the connection between mental health and skin disorders, stands to benefit from AI\'s advanced data analysis and pattern recognition capabilities.
METHODS: A literature search was conducted on PubMed and Google Scholar, spanning from 2004 to 2024, following PRISMA guidelines. Studies included demonstrated AI\'s effectiveness in predicting treatment outcomes for body dysmorphic disorder, identifying biomarkers in psoriasis and anxiety disorders, and refining therapeutic strategies.
RESULTS: The review identified several studies highlighting AI\'s role in improving treatment outcomes and diagnostic accuracy in psychodermatology. AI was effective in predicting outcomes for body dysmorphic disorder and identifying biomarkers related to psoriasis and anxiety disorders. However, challenges such as limited dermatologist knowledge, integration difficulties, and ethical concerns regarding patient privacy were noted.
CONCLUSIONS: AI holds significant promise for advancing psychodermatology by improving diagnostic precision, treatment effectiveness, and personalized care. Nonetheless, realizing this potential requires large-scale clinical validation, enhanced dataset diversity, and robust ethical frameworks. Future research should focus on these areas, with interdisciplinary collaboration essential for overcoming current challenges and optimizing patient care in psychodermatology.

Heart failure (HF) remains a significant burden on global healthcare systems, necessitating innovative approaches for its management. This manuscript critically evaluates the role of remote monitoring and telemedicine in revolutionizing HF care delivery. Drawing upon a synthesis of current literature and clinical practices, it delineates the pivotal benefits, challenges, and personalized strategies associated with these technologies in HF management. The analysis highlights the potential of remote monitoring and telemedicine in facilitating timely interventions, enhancing patient engagement, and optimizing treatment adherence, thereby ameliorating clinical outcomes. However, technical intricacies, regulatory frameworks, and socioeconomic factors pose formidable hurdles to widespread adoption. The manuscript emphasizes the imperative of tailored interventions, leveraging advancements in artificial intelligence and machine learning, to address individual patient needs effectively. Looking forward, sustained innovation, interdisciplinary collaboration, and strategic investment are advocated to realize the transformative potential of remote monitoring and telemedicine in HF management, thereby advancing patient-centric care paradigms and optimizing healthcare resource allocation.

UNASSIGNED: Artificial Intelligence (AI) holds transformative potential for the healthcare industry, offering innovative solutions for diagnosis, treatment planning, and improving patient outcomes. As AI continues to be integrated into healthcare systems, it promises advancements across various domains. This review explores the diverse applications of AI in healthcare, along with the challenges and limitations that need to be addressed. The aim is to provide a comprehensive overview of AI\'s impact on healthcare and to identify areas for further development and focus.
UNASSIGNED: The review discusses the broad range of AI applications in healthcare. In medical imaging and diagnostics, AI enhances the accuracy and efficiency of diagnostic processes, aiding in early disease detection. AI-powered clinical decision support systems assist healthcare professionals in patient management and decision-making. Predictive analytics using AI enables the prediction of patient outcomes and identification of potential health risks. AI-driven robotic systems have revolutionized surgical procedures, improving precision and outcomes. Virtual assistants and chatbots enhance patient interaction and support, providing timely information and assistance. In the pharmaceutical industry, AI accelerates drug discovery and development by identifying potential drug candidates and predicting their efficacy. Additionally, AI improves administrative efficiency and operational workflows in healthcare, streamlining processes and reducing costs. AI-powered remote monitoring and telehealth solutions expand access to healthcare, particularly in underserved areas.
UNASSIGNED: Despite the significant promise of AI in healthcare, several challenges persist. Ensuring the reliability and consistency of AI-driven outcomes is crucial. Privacy and security concerns must be navigated carefully, particularly in handling sensitive patient data. Ethical considerations, including bias and fairness in AI algorithms, need to be addressed to prevent unintended consequences. Overcoming these challenges is critical for the ethical and successful integration of AI in healthcare.
UNASSIGNED: The integration of AI into healthcare is advancing rapidly, offering substantial benefits in improving patient care and operational efficiency. However, addressing the associated challenges is essential to fully realize the transformative potential of AI in healthcare. Future efforts should focus on enhancing the reliability, transparency, and ethical standards of AI technologies to ensure they contribute positively to global health outcomes.

OBJECTIVE: This work presents the development and evaluation of coordn8, a web-based application that streamlines fax processing in outpatient clinics using a \"human-in-the-loop\" machine learning framework. We demonstrate the effectiveness of the platform at reducing fax processing time and producing accurate machine learning inferences across the tasks of patient identification, document classification, spam classification, and duplicate document detection.
METHODS: We deployed coordn8 in 11 outpatient clinics and conducted a time savings analysis by observing users and measuring fax processing event logs. We used statistical methods to evaluate the machine learning components across different datasets to show generalizability. We conducted a time series analysis to show variations in model performance as new clinics were onboarded and to demonstrate our approach to mitigating model drift.
RESULTS: Our observation analysis showed a mean reduction in individual fax processing time by 147.5 s, while our event log analysis of over 7000 faxes reinforced this finding. Document classification produced an accuracy of 81.6%, patient identification produced an accuracy of 83.7%, spam classification produced an accuracy of 98.4%, and duplicate document detection produced a precision of 81.0%. Retraining document classification increased accuracy by 10.2%.
CONCLUSIONS: coordn8 significantly decreased fax-processing time and produced accurate machine learning inferences. Our human-in-the-loop framework facilitated the collection of high-quality data necessary for model training. Expanding to new clinics correlated with performance decline, which was mitigated through model retraining.
CONCLUSIONS: Our framework for automating clinical tasks with machine learning offers a template for health systems looking to implement similar technologies.

UNASSIGNED: Ultra-low-field magnetic resonance imaging (ULF-MRI) has emerged as an alternative with several portable clinical applications. This review aims to comprehensively explore its applications, potential limitations, technological advancements, and expert recommendations.
UNASSIGNED: A review of the literature was conducted across medical databases to identify relevant studies. Articles on clinical usage of ULF-MRI were included, and data regarding applications, limitations, and advancements were extracted. A total of 25 articles were included for qualitative analysis.
UNASSIGNED: The review reveals ULF-MRI efficacy in intensive care settings and intraoperatively. Technological strides are evident through innovative reconstruction techniques and integration with machine learning approaches. Additional advantages include features such as portability, cost-effectiveness, reduced power requirements, and improved patient comfort. However, alongside these strengths, certain limitations of ULF-MRI were identified, including low signal-to-noise ratio, limited resolution and length of scanning sequences, as well as variety and absence of regulatory-approved contrast-enhanced imaging. Recommendations from experts emphasize optimizing imaging quality, including addressing signal-to-noise ratio (SNR) and resolution, decreasing the length of scan time, and expanding point-of-care magnetic resonance imaging availability.
UNASSIGNED: This review summarizes the potential of ULF-MRI. The technology\'s adaptability in intensive care unit settings and its diverse clinical and surgical applications, while accounting for SNR and resolution limitations, highlight its significance, especially in resource-limited settings. Technological advancements, alongside expert recommendations, pave the way for refining and expanding ULF-MRI\'s utility. However, adequate training is crucial for widespread utilization.

UNASSIGNED: Tissue engineering is a multidisciplinary field that combines principles from cell biology, bioengineering, material sciences, medicine and surgery to create functional and viable bioproducts that can be used to repair or replace damaged or diseased tissues in the human body. The complexity of tissue engineering can affect the prospects of efficiently translating scientific discoveries in the field into scalable clinical approaches that could benefit patients. Organizational challenges may play a key role in the clinical translation of tissue engineering for the benefit of patients.
UNASSIGNED: To gain insight into the organizational aspects of tissue engineering that may create impediments to efficient clinical translation, we conducted a retrospective qualitative case study of one tissue engineering multi-site translational project on knee cartilage engineered tissue grafts. We collected qualitative data using a set of different methods: semi-structured interviews, documentary research and audio-visual content analysis.
UNASSIGNED: Our study identified various challenges associated to first-in-human trials in tissue engineering particularly related to: logistics and communication; research participant recruitment; clinician and medical student participation; study management; and regulation.
UNASSIGNED: While not directly generalizable to other types of advanced therapies or to regenerative medicine in general, our results offer valuable insights into organizational barriers that may prevent efficient clinical translation in the field of tissue engineering.

BACKGROUND: Recent enhancements in Large Language Models (LLMs) such as ChatGPT have exponentially increased user adoption. These models are accessible on mobile devices and support multimodal interactions, including conversations, code generation, and patient image uploads, broadening their utility in providing healthcare professionals with real-time support for clinical decision-making. Nevertheless, many authors have highlighted serious risks that may arise from the adoption of LLMs, principally related to safety and alignment with ethical guidelines.
OBJECTIVE: To address these challenges, we introduce a novel methodological approach designed to assess the specific feasibility of adopting LLMs within a healthcare area, with a focus on clinical nursing, evaluating their performance and thereby directing their choice. Emphasizing LLMs\' adherence to scientific advancements, this approach prioritizes safety and care personalization, according to the \"Organization for Economic Co-operation and Development\" frameworks for responsible AI. Moreover, its dynamic nature is designed to adapt to future evolutions of LLMs.
METHODS: Through integrating advanced multidisciplinary knowledge, including Nursing Informatics, and aided by a prospective literature review, seven key domains and specific evaluation items were identified as follows:A Peer Review by experts in Nursing and AI was performed, ensuring scientific rigor and breadth of insights for an essential, reproducible, and coherent methodological approach. By means of a 7-point Likert scale, thresholds are defined in order to classify LLMs as \"unusable\", \"usable with high caution\", and \"recommended\" categories. Nine state of the art LLMs were evaluated using this methodology in clinical oncology nursing decision-making, producing preliminary results. Gemini Advanced, Anthropic Claude 3 and ChatGPT 4 achieved the minimum score of the State of the Art Alignment & Safety domain for classification as \"recommended\", being also endorsed across all domains. LLAMA 3 70B and ChatGPT 3.5 were classified as \"usable with high caution.\" Others were classified as unusable in this domain.
CONCLUSIONS: The identification of a recommended LLM for a specific healthcare area, combined with its critical, prudent, and integrative use, can support healthcare professionals in decision-making processes.

BACKGROUND: Introducing new working methods is common in healthcare organisations. However, implementation of a new method is often suboptimal. This reduces the effectiveness of the innovation and has several other negative effects, for example on staff turnover. The aim of the current study was to implement the ABC method in residential departments for brain injured patients and to assess the quality of the implementation process. The ABC method is a simplified form of behavioural modification based on the concept that behaviour operates on the environment and is maintained by its consequences.
METHODS: Four residential departments for brain injured patients introduced the ABC method sequentially as healthcare innovation using a stepped-wedge design. A systematic process evaluation of the implementation was carried out using the framework of Saunders et al. Descriptive statistics were used to analyse the quantitative data; open questions were clustered.
RESULTS: The training of the ABC method was well executed and the nursing staff was enthusiastic and sufficiently involved. Important aspects for successful implementation had been addressed (like a detailed implementation plan and implementation meetings). However, facilitators and barriers that were noted were not addressed in a timely manner. This negatively influenced the extent to which the ABC method could be properly learned, implemented, and applied in the short and long term.
CONCLUSIONS: The most challenging part of the introduction of this new trained and introduced method in health care was clearly the implementation. To have a successful implementation serious attention is needed to tailor-made evidence-based implementation strategies based on facilitators and barriers that are identified during the implementation process. Bottlenecks in working with the ABC method have to be addressed as soon as possible. This likely requires \'champions\' who are trained for the job, next to an organisation\'s management that facilitates the multidisciplinary teams and provides clarity about policy and agreements regarding the training and implementation of the new method. The current process evaluation and the recommendations may serve as an example for the implementation of new methods in other healthcare organisations.

The Stanford Biodesign needs-centric framework can guide healthcare innovators to successfully adopt the \'Identify, Invent and Implement\' framework and develop new healthcare innovations products to address patients\' needs. This scoping review explored the application of the Stanford Biodesign framework for healthcare innovation training and the development of novel healthcare innovative products. Seven electronic databases were searched from their respective inception dates till April 2023: PubMed, Embase, CINAHL, PsycINFO, Web of Science, Scopus, ProQuest Dissertations, and Theses Global. This review was reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis extension for Scoping Reviews and was guided by the Arksey and O\'Malley\'s scoping review framework. Findings were analyzed using Braun and Clarke\'s thematic analysis framework. Three themes and eight subthemes were identified from the 26 included articles. The main themes are: (1) Making a mark on healthcare innovation, (2) Secrets behind success, and (3) The next steps. The Stanford Biodesign framework guided healthcare innovation teams to develop new medical products and achieve better patient health outcomes through the induction of training programs and the development of novel products. Training programs adopting the Stanford Biodesign approach were found to be successful in improving trainees\' entrepreneurship, innovation, and leadership skills and should continue to be promoted. To aid innovators in commercializing their newly developed medical products, additional support such as securing funds for early start-up companies, involving clinicians and users in product testing and validation, and establishing new guidelines and protocols for the new healthcare products would be needed.

This comprehensive review explores the transformative impact of artificial intelligence (AI) on hospital management, delving into its applications, challenges, and future trends. Integrating AI in administrative functions, clinical operations, and patient engagement holds significant promise for enhancing efficiency, optimizing resource allocation, and revolutionizing patient care. However, this evolution is accompanied by ethical, legal, and operational considerations that necessitate careful navigation. The review underscores key findings, emphasizing the implications for the future of hospital management. It calls for a proactive approach, urging stakeholders to invest in education, prioritize ethical guidelines, foster collaboration, advocate for thoughtful regulation, and embrace a culture of innovation. The healthcare industry can successfully navigate this transformative era through collective action, ensuring that AI contributes to more effective, accessible, and patient-centered healthcare delivery.