Trust constitutes a fundamental strategy to deal with risks and uncertainty in complex societies. In line with the vast literature stressing the importance of trust in doctor–patient relationships, trust is therefore regularly suggested as a way of dealing with the risks of medical artificial intelligence (AI). Yet, this approach has come under charge from different angles. At least two lines of thought can be distinguished: (1) that trusting AI is conceptually confused, that is, that we cannot trust AI; and (2) that it is also dangerous, that is, that we should not trust AI—particularly if the stakes are as high as they routinely are in medicine. In this paper, we aim to defend a notion of trust in the context of medical AI against both charges. To do so, we highlight the technically mediated intentions manifest in AI systems, rendering trust a conceptually plausible stance for dealing with them. Based on literature from human–robot interactions, psychology and sociology, we then propose a novel model to analyse notions of trust, distinguishing between three aspects: reliability, competence, and intentions. We discuss each aspect and make suggestions regarding how medical AI may become worthy of our trust.

Recent advances in machine learning (ML) promise far-reaching improvements across medical care, not least within psychiatry. While to date no psychiatric application of ML constitutes standard clinical practice, it seems crucial to get ahead of these developments and address their ethical challenges early on. Following a short general introduction concerning ML in psychiatry, we do so by focusing on schizophrenia as a paradigmatic case. Based on recent research employing ML to further the diagnosis, treatment, and prediction of schizophrenia, we discuss three hypothetical case studies of ML applications with view to their ethical dimensions. Throughout this discussion, we follow the principlist framework by Tom Beauchamp and James Childress to analyse potential problems in detail. In particular, we structure our analysis around their principles of beneficence, non-maleficence, respect for autonomy, and justice. We conclude with a call for cautious optimism concerning the implementation of ML in psychiatry if close attention is paid to the particular intricacies of psychiatric disorders and its success evaluated based on tangible clinical benefit for patients.

Machine Learning (ML) is on the rise in medicine, promising improved diagnostic, therapeutic and prognostic clinical tools. While these technological innovations are bound to transform health care, they also bring new ethical concerns to the forefront. One particularly elusive challenge regards discriminatory algorithmic judgements based on biases inherent in the training data. A common line of reasoning distinguishes between justified differential treatments that mirror true disparities between socially salient groups, and unjustified biases which do not, leading to misdiagnosis and erroneous treatment. In the curation of training data this strategy runs into severe problems though, since distinguishing between the two can be next to impossible. We thus plead for a pragmatist dealing with algorithmic bias in healthcare environments. By recurring to a recent reformulation of William James’s pragmatist understanding of truth, we recommend that, instead of aiming at a supposedly objective truth, outcome-based therapeutic usefulness should serve as the guiding principle for assessing ML applications in medicine.

Using artificial intelligence to improve patient care is a cutting-edge methodology, but its implementation in clinical routine has been limited due to significant concerns about understanding its behavior. One major barrier is the explainability dilemma and how much explanation is required to use artificial intelligence safely in healthcare. A key issue is the lack of consensus on the definition of explainability by experts, regulators, and healthcare professionals, resulting in a wide variety of terminology and expectations. This paper aims to fill the gap by defining minimal explainability standards to serve the views and needs of essential stakeholders in healthcare. In that sense, we propose to define minimal explainability criteria that can support doctors’ understanding, meet patients’ needs, and fulfill legal requirements. Therefore, explainability need not to be exhaustive but sufficient for doctors and patients to comprehend the artificial intelligence models’ clinical implications and be integrated safely into clinical practice. Thus, minimally acceptable standards for explainability are context-dependent and should respond to the specific need and potential risks of each clinical scenario for a responsible and ethical implementation of artificial intelligence.