Artificial Intelligence (AI) often outperforms human doctors in terms of decisional speed. For some diseases, the expected benefit of a fast but less accurate decision exceeds the benefit of a slow but more accurate one. In such cases, we argue, it is often justified to rely on a medical AI to maximise decision speed – even if the AI is less accurate than human doctors.

We would like to thank the authors of the commentaries for their critical appraisal of our feature article, Who is afraid of black box algorithms?1 Their comments, suggestions and concerns are various, and we are glad that our article contributes to the academic debate about the ethical and epistemic conditions for medical Explanatory AI. We would like to bring to attention a few issues that are common worries across reviewers. Most prominently are the merits of computational reliabilism —in particular, when (...) promoted as an alternative to transparency—and CR as necessary but not sufficient for delivering trust. We finalise our response by addressing concerns about the place and role of artificial intelligence in medical decision-making and the physician’s responsibilities. We understand the concerns and reservations that some of the reviewers express regarding the epistemic merits of CR. We believe that, in part, this is due to a practice too deeply rooted in transparency. But on …. (shrink)

Artificial intelligence (AI) has attracted an increasing amount of attention, both positive and negative. Its potential applications in healthcare are indeed manifold and revolutionary, and within the realm of medical imaging and radiology (which will be the focus of this paper), significant increases in accuracy and speed, as well as significant savings in cost, stand to be gained through the adoption of this technology. Because of its novelty, a norm of keeping humans “in the loop” wherever AI mechanisms are deployed (...) has become synonymous with good ethical practice in some circles. It has been argued that keeping humans “in the loop” is important for reasons of safety, accountability, and the maintenance of institutional trust. However, as the application of machine learning for the detection of lumbar spinal stenosis (LSS) in this paper’s case study reveals, there are some scenarios where an insistence on keeping humans in the loop (or in other words, the resistance to automation) seems unwarranted and could possibly lead us to miss out on very real and important opportunities in healthcare—particularly in low-resource settings. It is important to acknowledge these opportunity costs of resisting automation in such contexts, where better options may be unavailable. Using an AI model based on convolutional neural networks developed by a team of researchers at NUH/NUS medical school in Singapore for automated detection and classification of the lumbar spinal canal, lateral recess, and neural foraminal narrowing in an MRI scan of the spine to diagnose LSS, we will aim to demonstrate that where certain criteria hold (e.g., the AI is as accurate or better than human experts, risks are low in the event of an error, the gain in wellbeing is significant, and the task being automated is not essentially or importantly human), it is both morally permissible and even desirable to kick the humans out of the loop. (shrink)