Palliative care and new technologies. The use of smart sensor technologies and its impact on the Total Care principle.

BACKGROUND: Palliative care is an integral part of health care, which in term has become increasingly technologized in recent decades. Lately, innovative smart sensors combined with artificial intelligence promise better diagnosis and treatment. But to date, it is unclear: how are palliative care concepts and their underlying assumptions about humans challenged by smart sensor technologies (SST) and how can care benefit from SST? AIMS: The paper aims to identify changes and challenges in palliative care due to the use of SST. In addition, normative guiding criteria for the use of SST are developed. METHODS: The principle of Total Care used by the European Association for Palliative Care (EAPC) forms the basis for the ethical analysis. Drawing on this, its underlying conceptions of the human and its socio-ethical aspects are examined with a phenomenological focus. In the second step, the advantages, limitations, and socio-ethical challenges of using SST with respect to the Total Care principle are explored. Finally, ethical-normative requirements for the application of SST are derived. RESULTS AND CONCLUSION: First, SST are limited in their measurement capabilities. Second, SST have an impact on human agency and autonomy. This concerns both the patient and the caregiver. Third, some aspects of the Total Care principle are likely to be marginalized due to the use of SST. The paper formulates normative requirements for using SST to serve human flourishing. It unfolds three criteria according to which SST must be aligned: (1) evidence and purposefulness, (2) autonomy, and (3) Total Care.

Risk-Adjusted Prevention. Perspectives on the Governance of Entitlements to Benefits in the Case of Genetic (Breast Cancer) Risks.

This article is a revised version of our proposal for the establishment of the legal concept of risk-adjusted prevention in the German healthcare system to regulate access to risk-reduction measures for persons at high and moderate genetic cancer risk (Meier et al. Risikoadaptierte Prävention'. Governance Perspective für Leistungsansprüche bei genetischen (Brustkrebs-)Risiken, Springer, Wiesbaden, 2018). The German context specifics are summarized to enable the source text to be used for other country-specific healthcare systems. Establishing such a legal concept is relevant to all universal and free healthcare systems similar to Germany's. Disease risks can be determined with increasing precision using bioinformatics and biostatistical innovations ('big data'), due to the identification of pathogenic germ line mutations in cancer risk genes as well as non-genetic factors and their interactions. These new technologies open up opportunities to adapt therapeutic and preventive measures to the individual risk profile of complex diseases in a way that was previously unknown, enabling not only adequate treatment but in the best case, prevention. Access to risk-reduction measures for carriers of genetic risks is generally not regulated in healthcare systems that guarantee universal and equal access to healthcare benefits. In many countries, including Austria, Denmark, the UK and the US, entitlement to benefits is essentially linked to the treatment of already manifest disease. Issues around claiming benefits for prophylactic measures involve not only evaluation of clinical options (genetic diagnostics, chemoprevention, risk-reduction surgery), but the financial cost and-from a social ethics perspective-the relationship between them. Section 1 of this chapter uses the specific example of hereditary breast cancer to show why from a medical, social-legal, health-economic and socio-ethical perspective, regulated entitlement to benefits is necessary for persons at high and moderate risk of cancer. Section 2 discusses the medical needs of persons with genetic cancer risks and goes on to develop the healthy sick model which is able to integrate the problems of the different disciplines into one scheme and to establish criteria for the legal acknowledgement of persons at high and moderate (breast cancer) risks. In the German context, the social-legal categories of classical therapeutic medicine do not adequately represent preventive measures as a regular service within the healthcare system. We propose risk-adjusted prevention as a new legal concept based on the heuristic healthy sick model. This category can serve as a legal framework for social law regulation in the case of persons with genetic cancer risks. Risk-adjusted prevention can be established in principle in any healthcare system. Criteria are also developed in relation to risk collectives and allocation (Sects. 3, 4, 5).

Primer on an ethics of AI-based decision support systems in the clinic.

Making good decisions in extremely complex and difficult processes and situations has always been both a key task as well as a challenge in the clinic and has led to a large amount of clinical, legal and ethical routines, protocols and reflections in order to guarantee fair, participatory and up-to-date pathways for clinical decision-making. Nevertheless, the complexity of processes and physical phenomena, time as well as economic constraints and not least further endeavours as well as achievements in medicine and healthcare continuously raise the need to evaluate and to improve clinical decision-making. This article scrutinises if and how clinical decision-making processes are challenged by the rise of so-called artificial intelligence-driven decision support systems (AI-DSS). In a first step, this article analyses how the rise of AI-DSS will affect and transform the modes of interaction between different agents in the clinic. In a second step, we point out how these changing modes of interaction also imply shifts in the conditions of trustworthiness, epistemic challenges regarding transparency, the underlying normative concepts of agency and its embedding into concrete contexts of deployment and, finally, the consequences for (possible) ascriptions of responsibility. Third, we draw first conclusions for further steps regarding a 'meaningful human control' of clinical AI-DSS.

MaxSynBio: Avenues Towards Creating Cells from the Bottom Up.

A large German research consortium mainly within the Max Planck Society ("MaxSynBio") was formed to investigate living systems from a fundamental perspective. The research program of MaxSynBio relies solely on the bottom-up approach to synthetic biology. MaxSynBio focuses on the detailed analysis and understanding of essential processes of life through modular reconstitution in minimal synthetic systems. The ultimate goal is to construct a basic living unit entirely from non-living components. The fundamental insights gained from the activities in MaxSynBio could eventually be utilized for establishing a new generation of biotechnological processes, which would be based on synthetic cell constructs that replace the natural cells currently used in conventional biotechnology.

Ways Out of the Patenting Prohibition? Human Parthenogenetic and Induced Pluripotent Stem Cells.

According to the judgement of the European Court of Justice in 2014, human parthenogenetic stem cells are excluded from the patenting prohibition of procedures based on hESC by the European Biopatent Directive, because human parthenotes are not human embryos. This article is based on the thesis that in light of the technological advances in the field of stem cell research, the attribution of the term 'human embryo' to certain entities on a descriptive level as well as the attribution of a normative protection status to certain entities based on the criterion of totipotency, are becoming increasingly unclear. The example of human parthenotes in particular demonstrates that totipotency is not at all a necessary condition for the attribution of the term 'human embryo'. Furthermore, the example of hiPSC and somatic cells particularly shows that totipotency is also not a sufficient condition for the attribution of a normative protection status to certain entities. Therefore, it is not a suitable criterion for distinguishing between human embryos worthy of protection and human non-embryos not worthy of protection. Consequently, this conclusion has repercussions for the patenting question. The strict delineation between an ethically problematic commercial use of human embryos and the concomitant patenting prohibition of hESC-based procedures and an ethically unproblematic commercial use of human non-embryos and the therefore either unrestrictedly permitted (cf. human parthenotes) or even unregulated (cf. hiPSC) patenting of procedures based on these alleged alternatives becomes increasingly blurred.

['Healthy sick' or: How Genetic Risks Lever the Disease Concept of the Healthcare System]. / 'Healthy sick' oder: Wie genetisches Risiko den Krankheitsbegriff des GKV-Systsems aushebelt.

Currently it is not clear, whether and which specific prophylactic measures the healthcare system should provide as a standard offer for persons with genetic risks. Furthermore, there is no theoretical model for transparent regulation in this context. In the concrete case of BRCA1/2 carriers, the consequences of these defects become obvious: requests for medical measures are subjected to decision-making procedures of health insurance companies that are not wholly transparent. Against the background of medical advance in relation to complex diseases and in order to address this problem of the healthcare system, this article develops a healthy-sick model. This model gives a frame for identifying the medical demand of persons at risk of genetic diseases and for classifying the status of the persons concerned in the healthcare system.

[Entitlement to prophylactic treatment in cases of genetic predisposition for breast cancer : Interdisciplinary perspectives]. / Leistungsanspruch auf prophylaktische Behandlung bei genetischer Prädisposition für Brustkrebs : Interdisziplinäre Perspektiven.

Genetic tests can detect the predisposition to various diseases. The demand for gene diagnostics and corresponding prophylactic measures is increasing steadily. In the German healthcare system, however, legal uncertainties exist as to whether a mere risk of disease is reason enough to bear the costs for prophylactic measures. When medically effective prophylactic measures are available in certain cancer diseases, such as in hereditary breast cancer, the current practice of deciding in individual cases appears to be insufficient.The fact that persons with a high or very increased risk of breast cancer are precluded from a standard care procedure raises questions concerning ethical justification as well as medical plausibility. Moreover, it is remarkable that the statutory healthcare system treats persons at risk differently. In some cases there is a regulated way of reimbursement for preventive measures for persons at risk (factor V Leiden mutation) and in other cases there are only case-by-case decisions. Finally, in light of social regulations for persons at high and very increased risk this article considers the need of optimization regarding the risk communication in the decision-making process and the crucial question of budgetary impact for the German healthcare system.From a medical, ethical and legal perspective, a social regulation for persons at high and very increased risk of disease is inevitable and the consequences should be discussed in advance.

Determination of Death: A Discussion on Responsible Scholarship, Clinical Practices, and Public Engagement.

The concept and determination of death by neurological or cardio-circulatory criteria play a crucial role for medical practice, society, and the law. Academic debates on death determination have regained momentum, and recent cases involving the neurological determination of death ("brain death") in the United States have sparked sustained public debate. The determination of death by neurological criterion (irreversible cessation of the whole brain or of the brain stem) is medically practiced in at least 80 countries. However, academic debates persist about the conceptual and scientific validity of death determined by neurological criterion. The cardio-circulatory criterion, which permits organ donation following cardio-circulatory arrest, has also recently been challenged. Given the presence of academic debates, several questions ensue about the responsible conduct of clinicians and scholars involved in clinical practices and academic research. This article identifies tension points for responsible practices in the domains of scholarship, clinical practice, and public discourse and formulates suggestions to stimulate further dialogue on responsible practices and to identify questions in need of further research.

Prospectively investigating the impact of AI onshared decision-making in post kidney transplant care (PRIMA-AI): protocol for a longitudinal qualitative study among patients, their support persons and treating physicians at a tertiary care centre.

INTRODUCTION: As healthcare is shifting from a paternalistic to a patient-centred approach, medical decision making becomes more collaborative involving patients, their support persons (SPs) and physicians. Implementing shared decision-making (SDM) into clinical practice can be challenging and becomes even more complex with the introduction of artificial intelligence (AI) as a potential actant in the communicative network. Although there is more empirical research on patients' and physicians' perceptions of AI, little is known about the impact of AI on SDM. This study will help to fill this gap. To the best of our knowledge, this is the first systematic empirical investigation to prospectively assess the views of patients, their SPs and physicians on how AI affects SDM in physician-patient communication after kidney transplantation. Using a transdisciplinary approach, this study will explore the role and impact of an AI-decision support system (DSS) designed to assist with medical decision making in the clinical encounter. METHODS AND ANALYSIS: This is a plan to roll out a 2 year, longitudinal qualitative interview study in a German kidney transplant centre. Semi-structured interviews with patients, SPs and physicians will be conducted at baseline and in 3-, 6-, 12- and 24-month follow-up. A total of 50 patient-SP dyads and their treating physicians will be recruited at baseline. Assuming a dropout rate of 20% per year, it is anticipated that 30 patient-SP dyads will be included in the last follow-up with the aim of achieving data saturation. Interviews will be audio-recorded and transcribed verbatim. Transcripts will be analysed using framework analysis. Participants will be asked to report on their (a) communication experiences and preferences, (b) views on the influence of the AI-based DSS on the normative foundations of the use of AI in medical decision-making, focusing on agency along with trustworthiness, transparency and responsibility and (c) perceptions of the use of the AI-based DSS, as well as barriers and facilitators to its implementation into routine care. ETHICS AND DISSEMINATION: Approval has been granted by the local ethics committee of Charité-Universitätsmedizin Berlin (EA1/177/23 on 08 August 2023). This research will be conducted in accordance with the principles of the Declaration of Helsinki (1996). The study findings will be used to develop communication guidance for physicians on how to introduce and sustainably implement AI-assisted SDM. The study results will also be used to develop lay language patient information on AI-assisted SDM. A broad dissemination strategy will help communicate the results of this research to a variety of target groups, including scientific and non-scientific audiences, to allow for a more informed discourse among different actors from policy, science and society on the role and impact of AI in physician-patient communication.

Investigating the Impact of AI on Shared Decision-Making in Post-Kidney Transplant Care (PRIMA-AI): Protocol for a Randomized Controlled Trial.

BACKGROUND: Patients after kidney transplantation eventually face the risk of graft loss with the concomitant need for dialysis or retransplantation. Choosing the right kidney replacement therapy after graft loss is an important preference-sensitive decision for kidney transplant recipients. However, the rate of conversations about treatment options after kidney graft loss has been shown to be as low as 13% in previous studies. It is unknown whether the implementation of artificial intelligence (AI)-based risk prediction models can increase the number of conversations about treatment options after graft loss and how this might influence the associated shared decision-making (SDM). OBJECTIVE: This study aims to explore the impact of AI-based risk prediction for the risk of graft loss on the frequency of conversations about the treatment options after graft loss, as well as the associated SDM process. METHODS: This is a 2-year, prospective, randomized, 2-armed, parallel-group, single-center trial in a German kidney transplant center. All patients will receive the same routine post-kidney transplant care that usually includes follow-up visits every 3 months at the kidney transplant center. For patients in the intervention arm, physicians will be assisted by a validated and previously published AI-based risk prediction system that estimates the risk for graft loss in the next year, starting from 3 months after randomization until 24 months after randomization. The study population will consist of 122 kidney transplant recipients >12 months after transplantation, who are at least 18 years of age, are able to communicate in German, and have an estimated glomerular filtration rate <30 mL/min/1.73 m2. Patients with multi-organ transplantation, or who are not able to communicate in German, as well as underage patients, cannot participate. For the primary end point, the proportion of patients who have had a conversation about their treatment options after graft loss is compared at 12 months after randomization. Additionally, 2 different assessment tools for SDM, the CollaboRATE mean score and the Control Preference Scale, are compared between the 2 groups at 12 months and 24 months after randomization. Furthermore, recordings of patient-physician conversations, as well as semistructured interviews with patients, support persons, and physicians, are performed to support the quantitative results. RESULTS: The enrollment for the study is ongoing. The first results are expected to be submitted for publication in 2025. CONCLUSIONS: This is the first study to examine the influence of AI-based risk prediction on physician-patient interaction in the context of kidney transplantation. We use a mixed methods approach by combining a randomized design with a simple quantitative end point (frequency of conversations), different quantitative measurements for SDM, and several qualitative research methods (eg, records of physician-patient conversations and semistructured interviews) to examine the implementation of AI-based risk prediction in the clinic. TRIAL REGISTRATION: ClinicalTrials.gov NCT06056518; https://clinicaltrials.gov/study/NCT06056518. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/54857.

A Review and Tutorial on Machine Learning-Enabled Radar-Based Biomedical Monitoring.

Radio detection and ranging-based (radar) sensing offers unique opportunities for biomedical monitoring and can help overcome the limitations of currently established solutions. Due to its contactless and unobtrusive measurement principle, it can facilitate the longitudinal recording of human physiology and can help to bridge the gap from laboratory to real-world assessments. However, radar sensors typically yield complex and multidimensional data that are hard to interpret without domain expertise. Machine learning (ML) algorithms can be trained to extract meaningful information from radar data for medical experts, enhancing not only diagnostic capabilities but also contributing to advancements in disease prevention and treatment. However, until now, the two aspects of radar-based data acquisition and ML-based data processing have mostly been addressed individually and not as part of a holistic and end-to-end data analysis pipeline. For this reason, we present a tutorial on radar-based ML applications for biomedical monitoring that equally emphasizes both dimensions. We highlight the fundamentals of radar and ML theory, data acquisition and representation and outline categories of clinical relevance. Since the contactless and unobtrusive nature of radar-based sensing also raises novel ethical concerns regarding biomedical monitoring, we additionally present a discussion that carefully addresses the ethical aspects of this novel technology, particularly regarding data privacy, ownership, and potential biases in ML algorithms.

When performance is not enough-A multidisciplinary view on clinical decision support.

Scientific publications about the application of machine learning models in healthcare often focus on improving performance metrics. However, beyond often short-lived improvements, many additional aspects need to be taken into consideration to make sustainable progress. What does it take to implement a clinical decision support system, what makes it usable for the domain experts, and what brings it eventually into practical usage? So far, there has been little research to answer these questions. This work presents a multidisciplinary view of machine learning in medical decision support systems and covers information technology, medical, as well as ethical aspects. The target audience is computer scientists, who plan to do research in a clinical context. The paper starts from a relatively straightforward risk prediction system in the subspecialty nephrology that was evaluated on historic patient data both intrinsically and based on a reader study with medical doctors. Although the results were quite promising, the focus of this article is not on the model itself or potential performance improvements. Instead, we want to let other researchers participate in the lessons we have learned and the insights we have gained when implementing and evaluating our system in a clinical setting within a highly interdisciplinary pilot project in the cooperation of computer scientists, medical doctors, ethicists, and legal experts.

Transparent human - (non-) transparent technology? The Janus-faced call for transparency in AI-based health care technologies.

The use of Artificial Intelligence and Big Data in health care opens up new opportunities for the measurement of the human. Their application aims not only at gathering more and better data points but also at doing it less invasive. With this change in health care towards its extension to almost all areas of life and its increasing invisibility and opacity, new questions of transparency arise. While the complex human-machine interactions involved in deploying and using AI tend to become non-transparent, the use of these technologies makes the patient seemingly transparent. Papers on the ethical implementation of AI plead for transparency but neglect the factor of the "transparent patient" as intertwined with AI. Transparency in this regard appears to be Janus-faced: The precondition for receiving help - e.g., treatment advice regarding the own health - is to become transparent for the digitized health care system. That is, for instance, to donate data and become visible to the AI and its operators. The paper reflects on this entanglement of transparent patients and (non-) transparent technology. It argues that transparency regarding both AI and humans is not an ethical principle per se but an infraethical concept. Further, it is no sufficient basis for avoiding harm and human dignity violations. Rather, transparency must be enriched by intelligibility following Judith Butler's use of the term. Intelligibility is understood as an epistemological presupposition for recognition and the ensuing humane treatment. Finally, the paper highlights ways to testify intelligibility in dealing with AI in health care ex ante, ex post, and continuously.

"Nothing works without the doctor:" Physicians' perception of clinical decision-making and artificial intelligence.

Introduction: Artificial intelligence-driven decision support systems (AI-DSS) have the potential to help physicians analyze data and facilitate the search for a correct diagnosis or suitable intervention. The potential of such systems is often emphasized. However, implementation in clinical practice deserves continuous attention. This article aims to shed light on the needs and challenges arising from the use of AI-DSS from physicians' perspectives. Methods: The basis for this study is a qualitative content analysis of expert interviews with experienced nephrologists after testing an AI-DSS in a straightforward usage scenario. Results: The results provide insights on the basics of clinical decision-making, expected challenges when using AI-DSS as well as a reflection on the test run. Discussion: While we can confirm the somewhat expectable demand for better explainability and control, other insights highlight the need to uphold classical strengths of the medical profession when using AI-DSS as well as the importance of broadening the view of AI-related challenges to the clinical environment, especially during treatment. Our results stress the necessity for adjusting AI-DSS to shared decision-making. We conclude that explainability must be context-specific while fostering meaningful interaction with the systems available.

Evaluation of a clinical decision support system for detection of patients at risk after kidney transplantation.

Patient care after kidney transplantation requires integration of complex information to make informed decisions on risk constellations. Many machine learning models have been developed for detecting patient outcomes in the past years. However, performance metrics alone do not determine practical utility. We present a newly developed clinical decision support system (CDSS) for detection of patients at risk for rejection and death-censored graft failure. The CDSS is based on clinical routine data including 1,516 kidney transplant recipients and more than 100,000 data points. In a reader study we compare the performance of physicians at a nephrology department with and without the CDSS. Internal validation shows AUC-ROC scores of 0.83 for rejection, and 0.95 for graft failure. The reader study shows that predictions by physicians converge toward the CDSS. However, performance does not improve (AUC-ROC; 0.6413 vs. 0.6314 for rejection; 0.8072 vs. 0.7778 for graft failure). Finally, the study shows that the CDSS detects partially different patients at risk compared to physicians. This indicates that the combination of both, medical professionals and a CDSS might help detect more patients at risk for graft failure. However, the question of how to integrate such a system efficiently into clinical practice remains open.

Risk-Adjusted Cancer Screening and Prevention (RiskAP): Complementing Screening for Early Disease Detection by a Learning Screening Based on Risk Factors.

Background: Risk-adjusted cancer screening and prevention is a promising and continuously emerging option for improving cancer prevention. It is driven by increasing knowledge of risk factors and the ability to determine them for individual risk prediction. However, there is a knowledge gap between evidence of increased risk and evidence of the effectiveness and efficiency of clinical preventive interventions based on increased risk. This gap is, in particular, aggravated by the extensive availability of genetic risk factor diagnostics, since the question of appropriate preventive measures immediately arises when an increased risk is identified. However, collecting proof of effective preventive measures, ideally by prospective randomized preventive studies, typically requires very long periods of time, while the knowledge about an increased risk immediately creates a high demand for action. Summary: Therefore, we propose a risk-adjusted prevention concept that is based on the best current evidence making needed and appropriate preventive measures available, and which is constantly evaluated through outcome evaluation, and continuously improved based on these results. We further discuss the structural and procedural requirements as well as legal and socioeconomical aspects relevant for the implementation of this concept.

What is (synthetic) life? basic concepts of life in synthetic biology.

One of the central aims of synthetic biology (SB) is to better understand the mechanisms of life by trying to develop and synthesize new forms and perhaps modes of life. While the question of what is life has occupied mankind for centuries, there is a lack of empirical research examining the basic concepts of life scientists within SB themselves refer to and build on. In order to gain insights into these fundamental concepts, we conducted a qualitative interview study with scientists working in the field of SB. The aim was to gain a better understanding of the underlying understandings, principles, and characteristics of (synthetic) life on the one hand, and the entangled consequences for the conducted experiments and studies as well as the pursued scientific approaches. We identified four primarily underlying basic concepts of life which serve as a fundamental framework for current and further scientific research within SB and have implications for research questions, approaches and aims as well as for the evaluation of scientific results.