Digital assistants have become an indispensable tool in modern cardiology. The associated technological progress offers a significant potential to increase the efficiency of medical processes, enable more precise diagnoses in a shorter time, and thus improve patient care. However, the integration of digital assistants into clinical cardiology also raises new challenges and questions, particularly regarding the handling of legal issues. This review article aims to raise awareness of individual legal issues resulting from the use of digital technologies in cardiology. The focus is on how to deal with various legal challenges that cardiologists face, including issues related to treatment freedom, professional confidentiality and data protection. The integration of digital assistants in cardiology leads to a noticeable improvement in efficiency and quality of patient care, but at the same time, it involves a variety of legal challenges that need to be carefully addressed.
UNASSIGNED: Digitale Helfer sind inzwischen ein unverzichtbares Werkzeug in der modernen Kardiologie. Der damit einhergehende technologische Fortschritt bietet hierbei ein ausgesprochenes Potenzial, die Effizienz medizinischer Prozesse zu steigern, in kürzester Zeit präzisere Diagnosen zu ermöglichen und damit die Patientenversorgung zu verbessern. Doch mit der zunehmenden Integration digitaler Helfer im sonst arbeitsintensiven kardiologischen Berufsalltag ergeben sich auch neue Herausforderungen und Fragestellungen, insbesondere im Hinblick auf den Umgang mit dem Thema Recht. Der vorliegende Übersichtsartikel zielt darauf ab, ein Bewusstsein für einzelne juristische Themen zu schaffen, die aus der Verwendung digitaler Technologien in der Kardiologie resultieren. Dabei wird es im Kern um den wohlüberlegten Umgang mit den haftungsrechtlichen Themen der Therapiefreiheit, des kardiologischen Facharztstandards, der ärztlichen Schweigepflicht wie auch des Datenschutzes gehen. Die Integration digitaler Helfer in der Kardiologie führt insgesamt zu einer spürbaren Verbesserung der Effizienz und Qualität der Patientenversorgung, beinhaltet jedoch gleichzeitig eine Vielzahl rechtlicher Herausforderungen, die sorgfältig adressiert werden müssen.

The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to set out some of the advantages and disadvantages of the use of AI technologies within the field of anaesthesia. Benefits of the application of AI in anaesthesia include an improvement in perioperative risk stratification, personalisation of anaesthetic plans, improvement in efficiency and ultimately reduce healthcare costs. However, reliance on technology may reduce clinical acumen but furthermore there are issues surrounding data quality, privacy as well as legal and ethical concerns, which require further evaluation. Whilst AI within anaesthetic practice holds immense promise, there are substantial challenges which require careful consideration and ongoing evaluation. A collaborative approach will be required from healthcare staff, developers and regulators to promote the safe, responsible, and effective application of AI in anaesthesia practice.

BACKGROUND: Social media platforms are increasingly used to recruit patients for clinical studies. Yet, patients\' attitudes regarding social media recruitment are underexplored.
OBJECTIVE: This mixed methods study aims to assess predictors of the acceptance of social media recruitment among patients with hepatitis B, a patient population that is considered particularly vulnerable in this context.
METHODS: Using a mixed methods approach, the hypotheses for our survey were developed based on a qualitative interview study with 6 patients with hepatitis B and 30 multidisciplinary experts. Thematic analysis was applied to qualitative interview analysis. For the cross-sectional survey, we additionally recruited 195 patients with hepatitis B from 3 clinical centers in Germany. Adult patients capable of judgment with a hepatitis B diagnosis who understood German and visited 1 of the 3 study centers during the data collection period were eligible to participate. Data analysis was conducted using SPSS (version 28; IBM Corp), including descriptive statistics and regression analysis.
RESULTS: On the basis of the qualitative interview analysis, we hypothesized that 6 factors were associated with acceptance of social media recruitment: using social media in the context of hepatitis B (hypothesis 1), digital literacy (hypothesis 2), interest in clinical studies (hypothesis 3), trust in nonmedical (hypothesis 4a) and medical (hypothesis 4b) information sources, perceiving the hepatitis B diagnosis as a secret (hypothesis 5a), attitudes toward data privacy in the social media context (hypothesis 5b), and perceived stigma (hypothesis 6). Regression analysis revealed that the higher the social media use for hepatitis B (hypothesis 1), the higher the interest in clinical studies (hypothesis 3), the more trust in nonmedical information sources (hypothesis 4a), and the less secrecy around a hepatitis B diagnosis (hypothesis 5a), the higher the acceptance of social media as a recruitment tool for clinical hepatitis B studies.
CONCLUSIONS: This mixed methods study provides the first quantitative insights into social media acceptance for clinical study recruitment among patients with hepatitis B. The study was limited to patients with hepatitis B in Germany but sets out to be a reference point for future studies assessing the attitudes toward and acceptance of social media recruitment for clinical studies. Such empirical inquiries can facilitate the work of researchers designing clinical studies as well as ethics review boards in balancing the risks and benefits of social media recruitment in a context-specific manner.

The SARS-CoV-2 pandemic highlighted the importance of fast, collaborative research in biomedicine. Within the ORCHESTRA consortium, we rapidly deployed a pseudonymization service with minimal training and maintenance efforts under time-critical conditions to support a complex, multi-site research project. Over two years, the service was deployed in 13 sites across 11 countries to register more than 10,000 study participants and 15,000 biosamples. In this work, we present lessons learned as part of this process. Most importantly, we learned that common challenges can be overcome by creatively utilizing widely available tools and that having a dedicated partner to manage software rollout and pre-configure software packages for each site fosters the effective implementation.

Clinical imaging uses a variety of medical imaging techniques to diagnose and monitor diseases, injuries and other health conditions. These include X‑ray images, computed tomography (CT), magnetic resonance imaging (MRI) and ultrasound. These procedures are used to make accurate diagnoses and plan the best possible treatment for patients. Forensic imaging, in contrast, is used in both living and deceased persons in the context of criminal investigations. Postmortem forensic imaging techniques, such as postmortem CT (PMCT) and postmortem CT angiography (PMCTA), include some of the same procedures used in clinical imaging. An important difference between clinical and forensic imaging is the purpose and context in which the imaging studies are used. In addition, radiological procedures, such as angiography, need to be adapted and modified in the post-mortem setting. From a legal perspective clinical and forensic imaging must strictly adhere to privacy and procedural guidelines. Forensic images often need to be admissible as evidence in court, which places specific requirements on the quality, authenticity and documentation of images. In the case of living individuals, there must be a valid indication and consent from the patient. Consent must also fundamentally be obtained for post-mortem examinations, e.g. from the public prosecutor\'s office.
UNASSIGNED: In der klinischen Bildgebung werden verschiedene medizinische Bildgebungsverfahren eingesetzt, um Krankheiten, Verletzungen und andere Gesundheitszustände zu diagnostizieren und zu überwachen. Dazu gehören unter anderem Röntgenaufnahmen, Computertomographie (CT), Magnetresonanztomographie (MRT) und Ultraschall. Diese Verfahren werden genutzt, um genaue Diagnosen zu stellen und die bestmögliche Behandlung für die Patienten zu planen. Die forensische Bildgebung wird im Gegensatz dazu für lebende als auch verstorbene Personen im Rahmen strafrechtlicher Ermittlungen genutzt. Postmortale forensische Bildgebungstechniken, wie postmortale CT (PMCT) und postmortale CT-Angiographien (PMCTA) umfassen teils gleiche Verfahren wie in der klinischen Bildgebung. Ein wichtiger Unterschied zwischen klinischer und forensischer Bildgebung liegt in der Zielsetzung und dem Kontext, in dem die Bilder verwendet werden. Zudem müssen die radiologischen Verfahren, beispielsweise die Angiographie, im postmortalen Setting angepasst und modifiziert werden. In rechtlicher Hinsicht müssen bei der klinischen und der forensischen Bildgebung strenge Datenschutz- und Verfahrensrichtlinien eingehalten werden. Forensische Bilder müssen oft vor Gericht als Beweismittel zugelassen werden können, was spezifische Anforderungen an die Qualität, Authentizität und Dokumentation der Bilder stellt. Bei Lebenden müssen insbesondere eine rechtfertigende Indikation sowie die Einwilligung des Patienten vorliegen. Auch für die postmortale Untersuchung muss grundsätzlich eine Einwilligung, beispielsweise durch die Staatsanwaltschaft, vorliegen.

Real-world evidence (RWE) studies are conducted on patient\'s data primarily collected for monitoring of health status of patients. The use of real-world data to generate evidence in academic research or for regulatory submission raises a variety of ethical issues such as privacy, confidentiality, data protection, data de-identification, data sharing, scientific design of study, and informed consent requirements. The investigators-researchers and sponsors should adhere to current standards of ethics whilst planning and conduct of RWE studies. The ethics committees should consider ethical issues specific to RWE studies before approval.

BACKGROUND: The demand for chat messaging apps for communication between physicians, therapists and patients is increasing. The expectations for this form of communication and uncertainties regarding introduction and use are heterogeneous.
OBJECTIVE: The implementation of chat messengers in the care of patients with Parkinson\'s disease should be facilitated by recommendations regarding introduction and usage.
METHODS: Semi-structured interviews with neurologists and physiotherapists were conducted to capture the expectations and needs regarding the use of chat messengers. From the data analysis, recommendations were derived.
RESULTS: The expectations for technical functionality exceeded the chat messenger functions. This concerns, e.g., the connection of the chat messenger to the electronic patient file. There is a great deal of uncertainty, particularly when it comes to the applicable General Data Protection Regulations (GDPR). The recommendations relating to the use of chat messengers, data protection aspects, the design of such tools and methodological considerations can help to implement the tool as an additional communication channel.
CONCLUSIONS: Practical recommendations regarding functionality, the use of chat messengers in everyday life and in relation to data protection are derived from the results. By improving knowledge, physicians and therapists can contribute to the successful establishment of chat messengers as an additional communication tool.
UNASSIGNED: HINTERGRUND: Die Nachfrage nach Chat-Messengern für den Austausch zwischen Ärzten, Therapeuten und ihren Patienten steigt. Die Erwartungen an die Chat-Messenger und Unsicherheiten hinsichtlich Einführung und Nutzung sind heterogen.
UNASSIGNED: Die Implementierung eines Chat-Messengers in die Parkinson-Versorgung soll durch die Formulierung einführungs- und anwendungsrelevanter Empfehlungen vereinfacht werden.
METHODS: Semistrukturierte Interviews mit Neurologen und Physiotherapeuten wurden durchgeführt, um die Erwartungen an die Nutzung von Chat-Messengern zu erfassen, welche die Basis für die abgeleiteten Empfehlungen bildeten.
UNASSIGNED: Die Erwartungen an die technische Funktionalität übersteigen die Funktionen von Chat-Messenger. Das betrifft z. B. die Anbindung des Chat-Messengers an die elektronische Patientenakte. Insbesondere auch bei den anzuwendenden Regelungen der Datenschutz-Grundverordnung (DSGVO) herrscht große Unsicherheit. Die Empfehlungen, die sich auf die Nutzung der Chat-Messenger, auf die Aspekte des Datenschutzes, die Gestaltung dieser Technologien und methodische Überlegungen beziehen, sollen bei der Implementierung des Tools als zusätzlichen Kommunikationskanal helfen.
CONCLUSIONS: Aus den Ergebnissen leiten sich Empfehlungen zur Funktionalität, dem Umgang mit Chat-Messengern im Alltag und in Bezug zum Datenschutz ab. Durch Verbesserung der Kenntnisse bei den Gesundheitsberuflern und Beachtung dieser Handlungsanweisungen können Ärzte und Therapeuten zu einer erfolgreichen Etablierung des Chat-Messengers als zusätzliches Kommunikationstool beitragen.

This viewpoint article first explores the ethical challenges associated with the future application of large language models (LLMs) in the context of medical education. These challenges include not only ethical concerns related to the development of LLMs, such as artificial intelligence (AI) hallucinations, information bias, privacy and data risks, and deficiencies in terms of transparency and interpretability but also issues concerning the application of LLMs, including deficiencies in emotional intelligence, educational inequities, problems with academic integrity, and questions of responsibility and copyright ownership. This paper then analyzes existing AI-related legal and ethical frameworks and highlights their limitations with regard to the application of LLMs in the context of medical education. To ensure that LLMs are integrated in a responsible and safe manner, the authors recommend the development of a unified ethical framework that is specifically tailored for LLMs in this field. This framework should be based on 8 fundamental principles: quality control and supervision mechanisms; privacy and data protection; transparency and interpretability; fairness and equal treatment; academic integrity and moral norms; accountability and traceability; protection and respect for intellectual property; and the promotion of educational research and innovation. The authors further discuss specific measures that can be taken to implement these principles, thereby laying a solid foundation for the development of a comprehensive and actionable ethical framework. Such a unified ethical framework based on these 8 fundamental principles can provide clear guidance and support for the application of LLMs in the context of medical education. This approach can help establish a balance between technological advancement and ethical safeguards, thereby ensuring that medical education can progress without compromising the principles of fairness, justice, or patient safety and establishing a more equitable, safer, and more efficient environment for medical education.

Artificial intelligence (AI) is a tool that is only as good as its user. In the case of humanoid robots, an AI system can be seen as a social counterpart. Decision intelligence (DI) is a term that stems from engineering. DI as a science is used to process data with findings from the social sciences and decision theories. The aim is to improve decision-making processes. However, AI should be categorized as a tool and not as a communication partner. AI analyzes information from studies, guidelines, and textbooks from the outset-taking individual patient information into account. Physicians with a high level of clinical expertise can ask more specific questions about the latter. ChatGPT is trained with millions of texts from the internet, social media, online forums, journal articles, and books; it covers almost all areas of life.
UNASSIGNED: Die künstliche Intelligenz (KI) ist ein Werkzeug, das so gut oder schlecht ist wie deren Nutzer. Beim humanoiden Roboter kann ein KI-System als soziales Gegenüber erscheinen. „Decision intelligence“ (DI, Entscheidungsintelligenz), ist ein Begriff aus den Ingenieurdisziplinen und dient der Verarbeitung von Daten mit Erkenntnissen aus den Sozialwissenschaften und Entscheidungstheorien. Das Ziel ist die Verbesserung von Entscheidungsprozessen. KI ist als Werkzeug einzustufen und nicht als Kommunikationspartner. KI will Informationen aus Studien, Leitlinien und Lehrbüchern von Anfang an analysieren – unter Berücksichtigung individueller Patientendaten, diese kann der Arzt mit hoher klinischer Kompetenz gezielter erfragen. ChatGPT wird mit Millionen von Texten aus dem Internet, sozialen Medien, Online-Foren, Zeitschriftenartikeln und Büchern trainiert, es erfasst fast alle Lebensbereiche.

UNASSIGNED: The open-source software offered by the Observational Health Data Science and Informatics (OHDSI) collective, including the OMOP-CDM, serves as a major backbone for many real-world evidence networks and distributed health data analytics platforms. While container technology has significantly simplified deployments from a technical perspective, regulatory compliance can remain a major hurdle for the setup and operation of such platforms. In this paper, we present OHDSI-Compliance, a comprehensive set of document templates designed to streamline the data protection and information security-related documentation and coordination efforts required to establish OHDSI installations.
UNASSIGNED: To decide on a set of relevant document templates, we first analyzed the legal requirements and associated guidelines with a focus on the General Data Protection Regulation (GDPR). Moreover, we analyzed the software architecture of a typical OHDSI stack and related its components to the different general types of concepts and documentation identified. Then, we created those documents for a prototypical OHDSI installation, based on the so-called Broadsea package, following relevant guidelines from Germany. Finally, we generalized the documents by introducing placeholders and options at places where individual institution-specific content will be needed.
UNASSIGNED: We present four documents: (1) a record of processing activities, (2) an information security concept, (3) an authorization concept, as well as (4) an operational concept covering the technical details of maintaining the stack. The documents are publicly available under a permissive license.
UNASSIGNED: To the best of our knowledge, there are no other publicly available sets of documents designed to simplify the compliance process for OHDSI deployments. While our documents provide a comprehensive starting point, local specifics need to be added, and, due to the heterogeneity of legal requirements in different countries, further adoptions might be necessary.