Re: Hofmann et al. Overdiagnosis, one concept, three perspectives, and a model.

It is with great interest we have read the article "Overdiagnosis: one concept, three perspectives, and a model" by Hofmann and colleagues. We share the authors' ambition of understanding what overdiagnosis is and what it isn't. In our research, we define overdiagnosis on the basis of two interrelated phenomena: overdetection and overdefinition. Overdetection is the labelling of a person with a disease or abnormal condition, that would not have caused the person harm, e.g., symptoms or death, if left undiscovered. Overdefinition is the creation of new diagnoses by overmedicalising ordinary life experiences or expanding existing diagnoses by lowering thresholds or widening diagnostic criteria, without evidence of improved outcomes. These phenomena have different causes and thereby often different drivers. However, they have one important consequence in common: people are turned into patients unnecessarily, i.e., overdiagnosed. On a personal level, overdiagnosis cause various types of harms, including physical, psychological, social and financial harm. On a societal level, overdiagnosis may also cause harm to public health, cause resource waste, and cultural changes with overmedicalisation of normal life events. By definition, none of the aforementioned phenomena lead to any clinical benefit. Therefore, we disagree with Hofmann and colleagues' definition of overdiagnosis as diagnoses that "…on balance, do more harm than good.". We argue that introducing balance and benefits to the definition of overdiagnosis complicates the concept unnecessarily and cause problems operationalising overdiagnosis.

Speed, accuracy, and efficiency: The promises and practices of digitization in pathology.

Digitization is often presented in policy discourse as a panacea to a multitude of contemporary problems, not least in healthcare. How can policy promises relating to digitization be assessed and potentially countered in particular local contexts? Based on a study in Denmark, we suggest scrutinizing the politics of digitization by comparing policy promises about the future with practitioners' experience in the present. While Denmark is one of the most digitalized countries in the world, digitization of pathology has only recently been given full policy attention. As pathology departments are faced with an increased demand for pathology analysis and a shortage of pathologists, Danish policymakers have put forward digitization as a way to address these challenges. Who is it that wants to digitize pathology, why, and how does digitization unfold in routine work practices? Using online search and document analysis, we identify actors and analyze the policy promises describing expectations associated with digitization. We then use interviews and observations to juxtapose these expectations with observations of everyday pathology practices as experienced by pathologists. We show that policymakers expect digitization to improve speed, patient safety, and diagnostic accuracy, as well as efficiency. In everyday practice, however, digitization does not deliver on these expectations. Fulfillment of policy expectations instead hinges on the types of artificial intelligence (AI) applications that are still to be developed and implemented. Some pathologists remark that AI might work in the easy cases, but this would leave them with only the difficult cases, which they consider too burdensome. Our particular mode of juxtaposing policy and practice throws new light on the political work done by policy promises and helps to explain why the discipline of pathology does not seem to easily lend itself to the digital embrace.

Lost in digitization - A systematic review about the diagnostic test accuracy of digital pathology solutions.

Introduction: Digital pathology solutions are increasingly implemented for primary diagnostics in departments of pathology around the world. This has sparked a growing engagement on validation studies to evaluate the diagnostic performance of whole slide imaging (WSI) regarding safety, reliability, and accuracy. The aim of this review was to evaluate the performance of digital pathology for diagnostic purposes compared to light microscopy (LM) in human pathology, based on validation studies designed to assess such technologies. Methods: In this systematic review based on PRISMA guidelines, we analyzed validation studies of WSI compared with LM. We included studies of diagnostic performance of WSI regarding diagnostic test accuracy (DTA) indicators, degree of overdiagnosis, diagnostic concordance, and observer variability as a secondary outcome. Overdiagnosis is (for example) detecting a pathological condition that will either not progress or progress very slowly. Thus, the patient will never get symptoms from this condition and the pathological condition will never be the cause of death. From a search comprising four databases: PubMed, EMBASE, Cochrane Library, and Web of Science, encompassing the period 2010-2021, we selected and screened 12 peer-reviewed articles that fulfilled our selection criteria. Risk of bias was conducted through QUADAS-2 tool, and data analysis and synthesis were performed in a qualitative format. Results: We found that diagnostic performance of WSI was not inferior to LM for DTA indicators, concordance, and observer variability. The degree of overdiagnosis was not explicitly reported in any of the studies, while the term itself was used in one study and could be implicitly calculated in another. Conclusion: WSI had an overall high diagnostic accuracy based on traditional accuracy measurements; however, the degree of overdiagnosis is unknown.