Clinical decision-making is being supported by machine learning models. So-called decision-support systems (DSS) are intended to improve the decision-making of human operators (e.g., physicians), but they also introduce issues of epistemic uncertainty and over-reliance, and thereby open up responsibility gaps. To overcome these shortcomings, explainable AI attempts to provide insights into how the system made a decision. Explanations are, however, provided post-hoc and require contextual interpretation.

One of the big shifts in healthcare caused by so-called disruptive innovations in healthcare powered by AI and big data, is a shift from diagnostic and curative healthcare to predictive and preventive healthcare. While preventive healthcare is almost exclusively cloaked in positive attributes, we need to maintain semantic clarity about what it can and cannot deliver, so that patients are not misguided about its benefits and limitations and can make well-informed decisions regarding their healthcare.

Innovations that have been regarded as disruptive in the medical realm, such as mHealth applications, or machine learning, are perceived as part of a positive shift towards a more preventive, participatory and affordable healthcare model. More recently, several contributions have started exploring the ecological impacts of disruptive innovations in healthcare. New principles have been developed concerning sustainable development and use of technology in health care.