Recent years have witnessed intensive efforts to specify which requirements ethical artificial intelligence (AI) must meet. General guidelines for ethical AI consider a varying number of principles important. A frequent novel element in these guidelines, that we have bundled together under the term explicability, aims to reduce the black‐box character of machine learning algorithms. The centrality of this element invites reflection on the conceptual relation between explicability and the four bioethical principles. This is important because the application of general ethical frameworks to clinical decision‐making entails conceptual questions: Is explicability a free‐standing principle? Is it already covered by the well‐established four bioethical principles? Or is it an independent value that needs to be recognized as such in medical practice? We discuss these questions in a conceptual‐ethical analysis, which builds upon the findings of an empirical document analysis. On the example of the medical specialty of radiology, we analyze the position of radiological associations on the ethical use of medical AI. We address three questions: Are there references to explicability or a similar concept? What are the reasons for such inclusion? Which ethical concepts are referred to?

Purpose: The method of diagnosing diabetic retinopathy (DR) through artificial intelligence (AI)-based systems has been commercially available since 2018. This introduces new ethical challenges with regard to obtaining informed consent from patients. The purpose of this work is to develop a checklist of items to be disclosed when diagnosing DR with AI systems in a primary care setting.Methods: Two systematic literature searches were conducted in PubMed and Web of Science databases: a narrow search focusing on DR and a broad search on general issues of AI-based diagnosis. An ethics content analysis was conducted inductively to extract two features of included publications: (1) novel information content for AI-aided diagnosis and (2) the ethical justification for its disclosure.Results: The narrow search yielded n = 537 records of which n = 4 met the inclusion criteria. The information process was scarcely addressed for primary care setting. The broad search yielded n = 60 records of which n = 11 were included. In total, eight novel elements were identified to be included in the information process for ethical reasons, all of which stem from the technical specifics of medical AI.Conclusions: Implications for the general practitioner are two-fold: First, doctors need to be better informed about the ethical implications of novel technologies and must understand them to properly inform patients. Second, patient's overconfidence or fears can be countered by communicating the risks, limitations, and potential benefits of diagnostic AI systems. If patients accept and are aware of the limitations of AI-aided diagnosis, they increase their chances of being diagnosed and treated in time.

Biomarker-based predictive tests for subjectively asymptomatic Alzheimer’s disease (AD) are utilized in research today. Novel applications of artificial intelligence (AI) promise to predict the onset of AD several years in advance without determining biomarker thresholds. Until now, little attention has been paid to the new ethical challenges that AI brings to the early diagnosis in asymptomatic individuals, beyond contributing to research purposes, when we still lack adequate treatment. The aim of this paper is to explore the ethical arguments put forward for AI aided AD prediction in subjectively asymptomatic individuals and their ethical implications. The ethical assessment is based on a systematic literature search. Thematic analysis was conducted inductively of 18 included publications. The ethical framework includes the principles of autonomy, beneficence, non-maleficence, and justice. Reasons for offering predictive tests to asymptomatic individuals are the right to know, a positive balance of the risk-benefit assessment, and the opportunity for future planning. Reasons against are the lack of disease modifying treatment, the accuracy and explicability of AI aided prediction, the right not to know, and threats to social rights. We conclude that there are serious ethical concerns in offering early diagnosis to asymptomatic individuals and the issues raised by the application of AI add to the already known issues. Nevertheless, pre-symptomatic testing should only be offered on request to avoid inflicted harm. We recommend developing training for physicians in communicating AI aided prediction.

Definition of the problem: The umbrella term “explicability” refers to the reduction of opacity of artificial intelligence (AI) systems. These efforts are challenging for medical AI applications because higher accuracy often comes at the cost of increased opacity. This entails ethical tensions because physicians and patients desire to trace how results are produced without compromising the performance of AI systems. The centrality of explicability within the informed consent process for medical AI systems compels an ethical reflection on the trade-offs. Which levels of explicability are needed to obtain informed consent when utilizing medical AI? Arguments: We proceed in five steps: First, we map the terms commonly associated with explicability as described in the ethics and computer science literature, i.e., disclosure, intelligibility, interpretability, and explainability. Second, we conduct a conceptual analysis of the ethical requirements for explicability when it comes to informed consent. Third, we distinguish hurdles for explicability in terms of epistemic and explanatory opacity. Fourth, this then allows to conclude the level of explicability physicians must reach and what patients can expect. In a final step, we show how the identified levels of explicability can technically be met from the perspective of computer science. Throughout our work, we take diagnostic AI systems in radiology as an example. Conclusion: We determined four levels of explicability that need to be distinguished for ethically defensible informed consent processes and showed how developers of medical AI can technically meet these requirements.

Introduction: Head-mounted displays (HMDs) that superimpose holograms onto patients are of particular surgical interest as they are believed to dramatically change surgical procedures by including safety warning and allowing real-time offsite consultations. Although there are promising benefits of mixed and augmented reality (MR/AR) technologies in surgery, they also raise new ethical concerns. The aim of this systematic review is to determine the full spectrum of ethical issues that is raised for surgeons in the intraoperative application of MR/AR technology.Methods: Five bibliographic databases were searched for publications on the use of MR/AR, HMDs and other devices, their intraoperative application in surgery, and ethical issues. We applied qualitative content analysis to the n = 50 articles included. Firstly, we coded the material with deductive categories derived from ethical frameworks for surgical innovations, complications and research. Secondly, clinical aspects with ethical relevance were inductively coded as ethical issues within the main categories. Thirdly, we pooled the ethical issues into themes and sub-themes. We report our findings according to the reporting guideline RESERVE.Results: We found n = 143 ethical issues across ten main themes, namely patient-physician relationship, informed consent, professionalism, research and innovation, legal and regulatory issues, functioning equipment and optimal operating conditions, allocation of resources, minimizing harm, good communication skills and the ability to exercise sound judgement. The five most prevalent ethical issues are “Need for continuous research and innovation”, “Ensuring improvement of the learning curve”, “MR/AR enables new maneuvers for surgeons”, “Ensuring improvement of comfort, ergonomics, and usability of devices,” and “Not withholding MR/AR if it performs better”.Conclusions: Recognizing the evidence-based limitations of the intraoperative MR/AR application is of paramount importance to avoid ethical issues, but clinical trials in surgery pose particular ethical risks for patients. Regarding the digital surgeon, long-term impact on human workforce, potentially harmful “negative training,” i.e., acquiring inappropriate behaviors, and the fear of surveillance need further attention. MR/AR technologies offer not only challenges but significant advantages, promoting a more equitable distribution of surgical expertise and optimizing healthcare. Aligned with the core principle of social justice, these technologies enable surgeons to collaborate globally, improving training conditions and addressing enduring global healthcare inequalities.

Informed consent in surgical settings requires not only the accurate communication of medical information but also the establishment of trust through empathic engagement. The use of large language models (LLMs) offers a novel opportunity to enhance the informed consent process by combining advanced information retrieval capabilities with simulated emotional responsiveness. However, the ethical implications of simulated empathy raise concerns about patient autonomy, trust and transparency. This paper examines the challenges of surgical informed consent, the potential benefits and limitations of digital tools such as LLMs and the ethical implications of simulated empathy. We distinguish between active empathy, which carries the risk of creating a misleading illusion of emotional connection and passive empathy, which focuses on recognising and signalling patient distress cues, such as fear or uncertainty, rather than attempting to simulate genuine empathy. We argue that LLMs should be limited to the latter, recognising and signalling patient distress cues and alerting healthcare providers to patient anxiety. This approach preserves the authenticity of human empathy while leveraging the analytical strengths of LLMs to assist surgeons in addressing patient concerns. This paper highlights how LLMs can ethically enhance the informed consent process without undermining the relational integrity essential to patient-centred care. By maintaining transparency and respecting the irreplaceable role of human empathy, LLMs can serve as valuable tools to support, rather than replace, the relational trust essential to informed consent.