OBJECTIVE: Large language models (LLMs) are gaining popularity due to their ability to communicate in a human-like manner. Their potential for science, including urology, is increasingly recognized. However, unresolved concerns regarding transparency, accountability, and the accuracy of LLM results still exist.
OBJECTIVE: This review examines the ethical, technical, and practical challenges as well as the potential applications of LLMs in urology and science.
METHODS: A selective literature review was conducted to analyze current findings and developments in the field of LLMs. The review considered studies on technical aspects, ethical considerations, and practical applications in research and practice.
RESULTS: LLMs, such as GPT from OpenAI and Gemini from Google, show great potential for processing and analyzing text data. Applications in urology include creating patient information and supporting administrative tasks. However, for purely clinical and scientific questions, the methods do not yet seem mature. Currently, concerns about ethical issues and the accuracy of results persist.
CONCLUSIONS: LLMs have the potential to support research and practice through efficient data processing and information provision. Despite their advantages, ethical concerns and technical challenges must be addressed to ensure responsible and trustworthy use. Increased implementation could reduce the workload of urologists and improve communication with patients.
UNASSIGNED: HINTERGRUND: Large Language Models (LLM) gewinnen aufgrund ihrer Fähigkeit zur menschenähnlichen Kommunikation an Popularität. Ihr Potenzial für die Wissenschaft, einschließlich der Urologie, wird zunehmend erkannt. Allerdings bestehen derzeit noch ungelöste Fragen hinsichtlich Transparenz, Verantwortlichkeiten und der Korrektheit der Ergebnisse von LLM.
UNASSIGNED: Diese Übersichtsarbeit untersucht die ethischen, technischen und praktischen Herausforderungen sowie Anwendungsmöglichkeiten von LLM in der Urologie und Wissenschaft.
METHODS: Es wurde eine selektive Literaturrecherche durchgeführt, um aktuelle Erkenntnisse und Entwicklungen im Bereich der LLM zu analysieren. Berücksichtig wurden Studien zu technischen Aspekten, ethischen Überlegungen und praktischen Anwendungen in der Forschung und Praxis.
UNASSIGNED: Die LLM, wie GPT (Generative Pretrained Transformer) von OpenAI und Gemini von Google, zeigen großes Potenzial für die Verarbeitung und Analyse von Textdaten. Anwendungen in der Urologie umfassen die Erstellung von Patienteninformationen und die Unterstützung bei administrativen Aufgaben. Für rein klinische und wissenschaftliche Fragestellungen scheinen die Verfahren noch nicht ausgereift zu sein. Gegenwärtig bestehen weiterhin Bedenken hinsichtlich ethischer Fragen und der Genauigkeit der Ergebnisse.
UNASSIGNED: Die LLM haben das Potenzial, Forschung und Praxis durch effiziente Datenverarbeitung und Informationsbereitstellung zu unterstützen. Trotz ihrer Vorteile müssen ethische Bedenken und technische Herausforderungen adressiert werden, um eine verantwortungsvolle und vertrauenswürdige Nutzung zu gewährleisten. Eine verstärkte Implementierung könnte die Arbeitsbelastung von Urologen reduzieren und die Kommunikation mit Patienten verbessern.

Henrietta Lacks\' deidentified tissue became HeLa cells (the paradigmatic learning health platform). In this article, we discuss separating research on Ms Lacks\' tissue from obligations to promote respect, beneficence, and justice for her as a patient. This case illuminates ethical challenges for the secondary use of biospecimens, which persist in contemporary learning health systems. Deidentification and broad consent seek to maximize the benefits of learning from care by minimizing burdens on patients, but these strategies are insufficient for privacy, transparency, and engagement. The resulting supply chain for human cellular and tissue-based products may therefore recapitulate the harms experienced by the Lacks family. We introduce the potential for blockchain technology to build unprecedented transparency, engagement, and accountability into learning health system architecture without requiring deidentification. The ability of nonfungible tokens to maintain the provenance of inherently unique digital assets may optimize utility, value, and respect for patients who contribute tissue and other clinical data for research. We consider the potential benefits and survey major technical, ethical, socioeconomic, and legal challenges for the successful implementation of the proposed solutions. The potential for nonfungible tokens to promote efficiency, effectiveness, and justice in learning health systems demands further exploration.

It is necessary to harmonize and standardize data variables used in case report forms (CRFs) of clinical studies to facilitate the merging and sharing of the collected patient data across several clinical studies. This is particularly true for clinical studies that focus on infectious diseases. Public health may be highly dependent on the findings of such studies. Hence, there is an elevated urgency to generate meaningful, reliable insights, ideally based on a high sample number and quality data. The implementation of core data elements and the incorporation of interoperability standards can facilitate the creation of harmonized clinical data sets.
This study\'s objective was to compare, harmonize, and standardize variables focused on diagnostic tests used as part of CRFs in 6 international clinical studies of infectious diseases in order to, ultimately, then make available the panstudy common data elements (CDEs) for ongoing and future studies to foster interoperability and comparability of collected data across trials.
We reviewed and compared the metadata that comprised the CRFs used for data collection in and across all 6 infectious disease studies under consideration in order to identify CDEs. We examined the availability of international semantic standard codes within the Systemized Nomenclature of Medicine - Clinical Terms, the National Cancer Institute Thesaurus, and the Logical Observation Identifiers Names and Codes system for the unambiguous representation of diagnostic testing information that makes up the CDEs. We then proposed 2 data models that incorporate semantic and syntactic standards for the identified CDEs.
Of 216 variables that were considered in the scope of the analysis, we identified 11 CDEs to describe diagnostic tests (in particular, serology and sequencing) for infectious diseases: viral lineage/clade; test date, type, performer, and manufacturer; target gene; quantitative and qualitative results; and specimen identifier, type, and collection date.
The identification of CDEs for infectious diseases is the first step in facilitating the exchange and possible merging of a subset of data across clinical studies (and with that, large research projects) for possible shared analysis to increase the power of findings. The path to harmonization and standardization of clinical study data in the interest of interoperability can be paved in 2 ways. First, a map to standard terminologies ensures that each data element\'s (variable\'s) definition is unambiguous and that it has a single, unique interpretation across studies. Second, the exchange of these data is assisted by \"wrapping\" them in a standard exchange format, such as Fast Health care Interoperability Resources or the Clinical Data Interchange Standards Consortium\'s Clinical Data Acquisition Standards Harmonization Model.

BACKGROUND: Heart failure (HF) is a leading cause of morbidity and mortality globally, with a high disease burden. The prevalence of HF in Ghana is increasing rapidly, but epidemiological profiles, treatment patterns, and survival data are scarce. The national capacity to diagnose and manage HF appropriately is also limited. To address the growing epidemic of HF, it is crucial to recognize the epidemiological characteristics and medium-term outcomes of HF in Ghana and improve the capability to identify and manage HF promptly and effectively at all levels of care.
OBJECTIVE: This study aims to determine the epidemiological characteristics and medium-term HF outcomes in Ghana.
METHODS: We conducted a prospective, multicenter, multilevel cross-sectional observational study of patients with HF from January to December 2023. Approximately 5000 patients presenting with HF to 9 hospitals, including teaching, regional, and municipal hospitals, will be recruited and evaluated according to a standardized protocol, including the use of an echocardiogram and an N-terminal pro-brain natriuretic peptide (NT-proBNP) test. Guideline-directed medical treatment of HF will be initiated for 6 months, and the medium-term outcomes of interventions, including rehospitalization and mortality, will be assessed. Patient data will be collated into a HF registry for continuous assessment and monitoring.
RESULTS: This intervention will generate the necessary information on the etiology of HF, clinical presentations, the diagnostic yield of various tools, and management outcomes. In addition, it will build the necessary capacity and support for HF management in Ghana. As of July 30, 2023, the training and onboarding of all 9 centers had been completed. Preliminary analyses will be conducted by the end of the second quarter of 2024, and results are expected to be publicly available by the middle of 2024.
CONCLUSIONS: This study will provide the necessary data on HF, which will inform decisions on the prevention and management of HF and form the basis for future research.
BACKGROUND: ISRCTN Registry (United Kingdom) ISRCTN18216214; https:www.isrctn.com/ISRCTN18216214.
UNASSIGNED: DERR1-10.2196/52616.

BACKGROUND: This exploratory study compares self-reported COVID-19 vaccine side effects and breakthrough infections in people who described themselves as having diabetes with those who did not identify as having diabetes.
OBJECTIVE: The study uses person-reported data to evaluate differences in the perception of COVID-19 vaccine side effects between adults with diabetes and those who did not report having diabetes.
METHODS: This is a retrospective cohort study conducted using data provided online by adults aged 18 years and older residing in the United States. The participants who voluntarily self-enrolled between March 19, 2021, and July 16, 2022, in the IQVIA COVID-19 Active Research Experience project reported clinical and demographic information, COVID-19 vaccination, whether they had experienced any side effects, test-confirmed infections, and consented to linkage with prescription claims. No distinction was made for this study to differentiate prediabetes or type 1 and type 2 diabetes nor to verify reports of positive COVID-19 tests. Person-reported medication use was validated using pharmacy claims and a subset of the linked data was used for a sensitivity analysis of medication effects. Multivariate logistic regression was used to estimate the adjusted odds ratios of vaccine side effects or breakthrough infections by diabetic status, adjusting for age, gender, education, race, ethnicity (Hispanic or Latino), BMI, smoker, receipt of an influenza vaccine, vaccine manufacturer, and all medical conditions. Evaluations of diabetes medication-specific vaccine side effects are illustrated graphically to support the examination of the magnitude of side effect differences for various medications and combinations of medications used to manage diabetes.
RESULTS: People with diabetes (n=724) reported experiencing fewer side effects within 2 weeks of vaccination for COVID-19 than those without diabetes (n=6417; mean 2.7, SD 2.0 vs mean 3.1, SD 2.0). The adjusted risk of having a specific side effect or any side effect was lower among those with diabetes, with significant reductions in fatigue and headache but no differences in breakthrough infections over participants\' maximum follow-up time. Diabetes medication use did not consistently affect the risk of specific side effects, either using self-reported medication use or using only diabetes medications that were confirmed by pharmacy health insurance claims for people who also reported having diabetes.
CONCLUSIONS: People with diabetes reported fewer vaccine side effects than participants not reporting having diabetes, with a similar risk of breakthrough infection.
BACKGROUND: ClinicalTrials.gov NCT04368065; https://clinicaltrials.gov/study/NCT04368065.

BACKGROUND: Digital health interventions offer a promising approach for monitoring during postoperative recovery. However, the effectiveness of these interventions remains poorly understood, particularly in children. The objective of this study was to assess the efficacy of digital health interventions for postoperative recovery in children.
METHODS: A systematic review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, with the use of automation tools for searching and screening. We searched five electronic databases for randomised controlled trials or non-randomised studies of interventions that utilised digital health interventions to monitor postoperative recovery in children. The study quality was assessed using Cochrane Collaboration\'s Risk of Bias tools. The systematic review protocol was prospectively registered with PROSPERO (CRD42022351492).
RESULTS: The review included 16 studies involving 2728 participants from six countries. Tonsillectomy was the most common surgery and smartphone apps (WeChat) were the most commonly used digital health interventions. Digital health interventions resulted in significant improvements in parental knowledge about the child\'s condition and satisfaction regarding perioperative instructions (standard mean difference=2.16, 95% confidence interval 1.45-2.87; z=5.98, P<0.001; I2=88%). However, there was no significant effect on children\'s pain intensity (standard mean difference=0.09, 95% confidence interval -0.95 to 1.12; z=0.16, P=0.87; I2=98%).
CONCLUSIONS: Digital health interventions hold promise for improving parental postoperative knowledge and satisfaction. However, more research is needed for child-centric interventions with validated outcome measures. Future work should focus development and testing of user-friendly digital apps and wearables to ease the healthcare burden and improve outcomes for children.
UNASSIGNED: PROSPERO (CRD42022351492).

UNASSIGNED: Public trust in national electronic health record systems is essential for the successful implementation within a healthcare system. Research investigating public trust in electronic health records is limited, leading to a lack of conceptual clarity. In response, the objective of this study is to gain a clearer understanding on the conceptualizations of public trust in electronic health records, which can support the implementation of national electronic health record systems.
UNASSIGNED: Guided by the PRISMA-ScR checklist, a scoping review of 27 qualitative studies on public trust in electronic health records found between January 2022 and June 2022 was conducted using an inclusive search method. In an iterative process, conceptual themes were derived describing the promoters and outcomes of public trust in electronic health records.
UNASSIGNED: Five major conceptual themes with 15 sub-themes were present across the literature. Comprehension, autonomy, and data protection promote public trust in electronic health record; while personal and system benefits are the outcomes once public trust in electronic health records exists. Additional findings highlight the pivotal role of healthcare actors for the public trust building process.
UNASSIGNED: The results underscore comprehension, autonomy, and data protection as important themes that help ascertain and solidify public trust in electronic health records. As well, health system actors have the capacity to promote or hinder national electronic health record implementation, depending on their actions and how the public perceives those actions. The findings can assist researchers, policymakers, and other health system actors in attaining a better understanding of the intricacies of public trust in electronic health records.

UNASSIGNED: Social media data may augment understanding of the disease and treatment experiences and quality of life of youth with chronic medical conditions. Little is known about the willingness to share social media data for health research among youth with chronic medical conditions and the differences in health status between sharing and nonsharing youth with chronic medical conditions.
UNASSIGNED: We aimed to evaluate the associations between patient-reported measures of disease symptoms and functioning and the willingness to share social media data.
UNASSIGNED: Between February 2018 and August 2019, during routine clinic visits, survey data about social media use and the willingness to share social media data (dependent variable) were collected from adolescents in a national rheumatic disease registry. Survey data were analyzed with patient-reported measures of disease symptoms and functioning and a clinical measure of disease activity, which were collected through a parent study. We used descriptive statistics and multivariate logistic regression to compare patient-reported outcomes between youth with chronic medical conditions who opted to share social media data and those who did not opt to share such data.
UNASSIGNED: Among 112 youths, (age: mean 16.1, SD 1.6 y; female: n=72, 64.3%), 83 (74.1%) agreed to share social media data. Female participants were more likely to share (P=.04). In all, 49 (43.8%) and 28 (25%) participants viewed and posted about rheumatic disease, respectively. Compared to nonsharers, sharers reported lower mobility (T-score: mean 49.0, SD 9.4 vs mean 53.9, SD 8.9; P=.02) and more pain interference (T-score: mean 45.7, SD 8.8 vs mean 40.4, SD 8.0; P=.005), fatigue (T-score: mean 49.1, SD 11.0 vs mean 39.7, SD 9.7; P<.001), depression (T-score: mean 48.1, SD 8.9 vs mean 42.2, SD 8.4; P=.003), and anxiety (T-score: mean 45.2, SD 9.3 vs mean 38.5, SD 7.0; P<.001). In regression analyses adjusted for age, sex, study site, and Physician Global Assessment score, each 1-unit increase in symptoms was associated with greater odds of willingness to share social media data, for measures of pain interference (Adjusted Odds Ratio [AOR] 1.07, 95% CI 1.001-1.14), fatigue (AOR 1.08, 95% CI 1.03-1.13), depression (AOR 1.07, 95% CI 1.01-1.13), and anxiety (AOR 1.10, 95% CI 1.03-1.18).
UNASSIGNED: High percentages of youth with rheumatic diseases used and were willing to share their social media data for research. Sharers reported worse symptoms and functioning compared to those of nonsharers. Social media may offer a potent information source and engagement pathway for youth with rheumatic diseases, but differences between sharing and nonsharing youth merit consideration when designing studies and evaluating social media-derived findings.

Patient-Based Real-Time Quality Control involves monitoring an assay using patient samples rather than external material. If the patient population does not change, then a shift in the long-term assay population results represents the introduction of a change in the assay. The advantages of this approach are that the sample(s) are commutable, it is inexpensive, the rules are simple to interpret and there is virtually continuous monitoring of the assay. The disadvantages are that the laboratory needs to understand their patient population and how they may change during the day, week or year and the initial change of mindset required to adopt the system. The concept is not new, having been used since the 1960s and widely adopted on hematology analyzers in the mid-1970s. It was not widely used in clinical chemistry as there were other stable quality control materials available. However, the limitations of conventional quality control approaches have become more evident. There is a greater understanding of how to collect and use patient data in real time and a range of powerful algorithms which can identify changes in assays. There are more assays on more samples being run. There is also a greater interest in providing a theoretical basis for the validation and integration of these techniques into routine practice.

Artificial intelligence (AI) opens up new opportunities to improve medical care in internal medicine; however, legal uncertainties in the application of AI impede its integration into the daily practice of internal medicine. To clarify the situation this paper gives an overview of the legal aspects related to AI and shows which frameworks must be adhered to in order to exploit the benefits of AI without neglecting the rights and protection of patients. The paper first addresses data protection issues which arise when sensitive health data are processed by AI. This is followed by a discussion of the key regulatory requirements for the use of AI in internal medicine. As the establishment of AI in practice also depends on sufficient funding, legal issues of reimbursement are additionally examined. Finally, the specific features that need to be considered when using AI to avoid medical liability consequences are highlighted.
UNASSIGNED: Künstliche Intelligenz (KI) eröffnet in der Inneren Medizin neue Möglichkeiten zur Verbesserung der medizinischen Versorgung. Rechtsunsicherheiten bei der Anwendung von KI stellen ein Hindernis für deren Integration in den Alltag der Inneren Medizin dar. Um Klarheit zu schaffen, bietet dieser Aufsatz einen Überblick über die rechtlichen Aspekte im Zusammenhang mit KI und zeigt, welche Rahmenbedingungen einzuhalten sind, um die Vorteile von KI auszuschöpfen, ohne dabei die Rechte und den Schutz der Patienten zu vernachlässigen. Es werden zunächst datenschutzrechtliche Fragen beantwortet, die sich bei der Verarbeitung sensibler Gesundheitsdaten durch KI ergeben. Anschließend werden die wichtigsten regulatorischen Anforderungen für die Nutzung von KI in der Inneren Medizin erörtert. Da die Etablierung von KI in der Praxis auch von einer ausreichenden Finanzierung abhängt, werden zudem erstattungsrechtliche Fragen thematisiert. Schließlich wird beleuchtet, welche Besonderheiten beim Einsatz von KI zu beachten sind, um arzthaftungsrechtliche Konsequenzen zu vermeiden.