Integrating artificial intelligence in bedside care for covid-19 and future pandemics

Michael Yu and colleagues examine the challenges in developing AI tools for use at point of care The covid-19 pandemic created unprecedented challenges for both clinicians and healthcare institutions. Adapting to a rapidly emerging disease while facing staff and material shortages prompted difficult decisions on how best to allocate resources. Artificial intelligence (AI) rapidly moved to the forefront of the effort to adapt our healthcare systems to coping with covid-19. Hundreds of new models were developed, promising best solutions for all aspects of patient care from diagnostics to therapeutics and logistics. Yet only a small minority of these models were deployed, and none became widely adopted.12 We argue that the covid-19 pandemic exposed flaws in the technological, institutional, and ethical foundations upon which AI must build to considerably improve bedside care. If AI is to be part of a rapid response to future health crises, the challenges that it faced during the covid-19 pandemic must be carefully analysed and overcome. AI is a branch of computer science that uses data and algorithms to extract meaning in a way that is characteristic of intelligent beings—that is, turning data into effective decision making processes. Research applications of AI in medicine have already emerged far and wide—for example, in drug discovery and modelling of complex biological systems. By contrast, efforts to integrate AI into everyday clinical care have had minimal success, despite the comparatively simple nature of the problems: optimising patient trajectories, maximising use of existing facilities, or determining when and how to reallocate resources. We surmise that this translational gap, which was magnified by the covid-19 pandemic, is due to the nature of the underlying data, the infrastructure through which they emerge, and the human context in which they occur. By understanding the influence of these factors on the chances …