Reviewed Work: Explaining Explanation by David-Hillel Ruben .

Following a boom in investment and overinflated expectations in the 1980s, artificial intelligence entered a period of retrenchment known as the “AI winter.” With advances in the field of machine learning and the availability of large datasets for training various types of artificial neural networks, AI is in another cycle of halcyon days. Although medicine is particularly recalcitrant to change, applications of AI in health care have professionals in fields like radiology worried about the future of their careers and have (...) the public tittering about the prospect of soulless machines making life‐and‐death decisions. Medicine thus appears to be at an inflection point—a kind of Groundhog Day on which either AI will bring a springtime of improved diagnostic and predictive practices or the shadow of public and professional fear will lead to six more metaphorical weeks of winter in medical AI. (shrink)

The last two decades have witnessed a crescendo of allegations that clinical translation is rife with waste and inefficiency. Patient advocates argue that excessively demanding regulations delay access to life‐saving drugs, research funders claim that too much basic science languishes in academic laboratories, journal editors allege that biased reporting squanders public investment in biomedical research, and drug companies (and their critics) argue that far too much is expended in pharmaceutical development.But how should stakeholders evaluate the efficiency of translation and proposed (...) reforms to drug development? Effective reforms require an accurate model of the systems they aspire to improve—their components, their proper functions, and their pathologies. However, there is currently no explicit and well‐developed model of translation for evaluating such criticisms.In what follows, we offer an explicit model of clinical translation. Many discussions of clinical translation and its pathologies presume that its main output is tangible: new drugs, vaccines, devices, and diagnostics. We disagree. We argue that the principal output of clinical translation is information—in particular, information about the coordinated set of materials, practices, and constraints needed to safely unlock the therapeutic or preventive activities of drugs, biologics, and diagnostics. To develop this information calls for a process far different from a simple linear progression of clinical trials; it requires exploratory sampling of many different elements in this set. Our model points to some limitations and liabilities of influential proposals for reforming research. It also reveals some underrecognized opportunities for improving the efficiency of clinical translation. (shrink)

IN BOTH THE EUDEMIAN ETHICS AND THE NICOMACHEAN ETHICS, Aristotle says that the aim of ethical inquiry is a practical one; we want to know what virtue is so that we may become good ourselves and thereby do well and be happy. By classifying ethical inquiry as a practical endeavor, Aristotle is rejecting a view that he attributes to Socrates according to which ethics is a kind of theoretical science. In theoretical sciences, such as geometry or astronomy, the knowledge of (...) a particular subject matter is sought as an end in itself, and the possession of such knowledge is sufficient to make one a geometer or an astronomer. In rejecting this model Aristotle argues that the knowledge of virtue is sought not solely for itself but in order to inform praxis and in order that we become virtuous and good, not by knowing what the virtues are but by cultivating them in practice. (shrink)

First-in-human clinical trials represent a critical juncture in the translation of laboratory discoveries. However, because they involve the greatest degree of uncertainty at any point in the drug development process, their initiation is beset by a series of nettlesome ethical questions [1]: has clinical promise been sufficiently demonstrated in animals? Should trial access be restricted to patients with refractory disease? Should trials be viewed as therapeutic? Have researchers adequately minimized risks? The resolution of such ethical questions inevitably turns on claims (...) about future events like harms, therapeutic response, and clinical translation. Recurrent failures in clinical translation, like Eli Lilly's Alzheimer candidate semagacestat, highlight the severe limitations of current methods of prediction. In this case, patients in the active arm of the placebo-controlled trial had earlier onset of dementia and elevated rates of skin cancer [2]. Various authoritative accounts of human research ethics state that decision-making about risk and benefit should be careful, systematic, and non-arbitrary [3]–[5]. Yet, these sources provide little guidance about what kinds of evidence stakeholders should use to ensure their estimates of such events ground responsible ethical decisions. In this article, we suggest that investigators, oversight bodies, and sponsors often base their predictions on a flawed and inappropriately narrow preclinical evidence base. (shrink)