The question what scientific progress means for a particular domain such as medicine seems importantly different from the question what scientific progress is in general. While the latter question received ample treatment in the philosophical literature, the former question is hardly discussed. I argue that it is nonetheless important to think about this question in view of the methodological choices we make. I raise specific questions that should be tackled regarding scientific progress in the medical sciences and demonstrate their importance (...) by means of an analysis of what evidence-based medicine (EBM) has, and has not, to offer in terms of progress. I show how critically thinking about EBM from the point of view of progress can help us in putting EBM and its favoured methodologies in the right perspective. My conclusion will be that blindly favouring certain methods because of their immediately tangible short-term benefits implies that we parry the important question of how best to advance progress in the long run. This leads us to losing sight of our general goals in doing research in the medical sciences. (shrink)

Researchers are studying how artificial intelligence (AI) can be used to better detect, prognosticate and subgroup diseases. The idea that AI might advance medicine’s understanding of biological categories of psychiatric disorders, as well as provide better treatments, is appealing given the historical challenges with prediction, diagnosis and treatment in psychiatry. Given the power of AI to analyse vast amounts of information, some clinicians may feel obligated to align their clinical judgements with the outputs of the AI system. However, a potential (...) epistemic privileging of AI in clinical judgements may lead to unintended consequences that could negatively affect patient treatment, well-being and rights. The implications are also relevant to precision medicine, digital twin technologies and predictive analytics generally. We propose that a commitment to epistemic humility can help promote judicious clinical decision-making at the interface of big data and AI in psychiatry. (shrink)

Psychotherapy research is known for its pursuit of evidence-based treatment. Psychotherapeutic efficacy is assessed by calculation of aggregated differences between pre treatment- and post treatment symptom levels. As this ‘gold standard methodology’ is regarded as ‘procedurally objective’, the efficacy number that results from the procedure is taken as a valid indicator of treatment efficacy. However, I argue that the assumption of procedural objectivity is not justified, as the methodology is build upon a problematic numerical basis. I use an empirical case (...) study to show how measurement problems practically occur in the first step of data collection, i.e. in individual symptom measurement. These problems have been discussed and acknowledged for decades, but still measurement is regarded as the best epistemic means to gain evidence on psychotherapeutic efficacy. Therefore, I show how initial measurement problems are overlooked in the remainder of the methodological procedure, which harms the ‘evidence-base’ of psychotherapeutic EBT. Via this applied analysis, I exhibit concerns that are increasingly raised in the literature in an empirical way, to emphasize the need for a non-idealized consideration of the ‘gold standard methodology’ as a means towards its clinical end. (shrink)