Hypothetical Abductive Reasoning in Dermatology and Dermatopathology

Abductive reasoning, or <i>abduction</i>, is a key process in scientific discovery and medical diagnosis. In everyday dermatology and dermatopathology, however, it functions as the practical engine behind differential diagnosis, clinicopathologic correlation, and disciplined pattern recognition. In this paper, we retain the epistemological foundation of abduction but translate it into usable steps for clinicians and dermatopathologists. We distinguish abduction from deduction and induction; separate <i>creative abduction</i> (which generates new concepts) from <i>selective abduction</i> (daily diagnostic choice); and show how both operate within a simple Select-and-Test (ST) Model: select a hypothesis, deduce what else should be true, test against data, and then update. We then reinterpret Ackerman's algorithmic method of pattern analysis as an operationalization of the ST-Model. Through a couple of concise case vignettes, we illustrate visual and manipulative abduction, nonmonotonic updates, and the role of artifacts (dermoscopy, DIF, stains) as so-called epistemic mediators. Finally, we map contemporary AI tools to selective abduction and propose practical guardrails for fairness, transparency, and accountability. The result is a pragmatic framework that preserves philosophical depth while addressing the daily needs of dermatologists and dermatopathologists in the clinic and at the microscope.