Evidence, discovery and justification: the case of evidence-based medicine

Reconsidering evidence-based management of endometriosis

Background: Without an adequate animal model permitting experiments the pathophysiology of endometriosis remains unclear and without a non-invasive diagnosis, information is limited to symptomatic women. Lesions are macroscopically and biochemically variable. Hormonal medical therapy cannot be blinded since recognised by the patient and the evidence of extensive surgery is limited because of the combination of low numbers of interventions of variable difficulty with variable surgical skills. Experience is spread among specialists in imaging, medical therapy, infertility, pain and surgery. In addition, the limitations of traditional statistics and p-values to interpret results and the complementarity of Bayesian inference should be realised. Objectives: To review and discuss evidence in endometriosis management Materials and Methods: A PubMed search for blinded randomised controlled trials in endometriosis. Results: Good-quality evidence is limited in endometriosis. Conclusions: Clinical experience remains undervalued especially for surgery. What is new? Evidence-based medicine should integrate traditional statistical analysis and the limitations of P-values, with the complementary Bayesian inference which is predictive and sequential and more like clinical medicine. Since clinical experience is important for grading evidence, specific experience in the different disciplines of endometriosis should be used to judge trial designs and results. Finally, clinical medicine can be considered as a series of experiments controlled by the outcome. Therefore, the clinical opinion of many has more value than an opinion.

Believing in Black Boxes: Machine Learning for Healthcare Does Not Need Explainability to be Evidence-Based

OBJECTIVE

To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application.

STUDY DESIGN AND SETTING

This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC.

RESULTS

We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself. We examine the role of evidence-based medicine in evaluating inexplicable treatments and technologies, and highlight the analogy between the concept of explainability in MLHC and the related concept of mechanistic reasoning in evidence-based medicine.

CONCLUSION

Ultimately, we conclude that the value of explainability in MLHC is not intrinsic, but is instead instrumental to achieving greater imperatives such as performance and trust. We caution against the uncompromising pursuit of explainability, and advocate instead for the development of robust empirical methods to successfully evaluate increasingly inexplicable algorithmic systems.

Evidence-based medicine as science

Evidence-based medicine has claimed to be science on a number of occasions, but it is not clear that this status is deserved. Within the philosophy of science, four main theories about the nature of science are historically recognized: inductivism, falsificationism, Kuhnian paradigms, and research programmes. If evidence-based medicine is science, knowledge claims should be derived using a process that corresponds to one of these theories. This paper analyses whether this is the case. In the first section, different theories about the nature of science are introduced. In the second section, the claim that evidence-based medicine is science is reinterpreted as the claim that knowledge claims derived from randomized controlled trails and meta-analyses are science. In the third section, the knowledge claims valued within evidence-based medicine are considered from the perspective of inductivism, falsificationism, Kuhnian paradigms, and research programmes. In the final section, possible counter arguments are considered. It is argued that the knowledge claims valued by evidence-based medicine are not justified using inductivism, falsificationism, Kuhnian paradigms, or research programmes. If these are the main criteria for evaluating if something is science or not, evidence-based medicine does not meet these criteria.

Theory, experience and practice

Despite its potential hazards, the activity of questioning theoretical frameworks and proposing solutions is necessary if progress is even to be possible. Intellectual history has by no means ended, so we cannot expect to have all the answers, and from time to time the activity of critical questioning will be frustrating. But intellectual progress requires us to continue the process of asking fundamental questions. The alternative to thinking in this way is indeed unthinkable.