Computer-assisted diagnostics

Healthcare is at the edge of a profound renovation or collapse due to the rapid inflow of machine learning protocols and procedures able to optimize several processes. Clinical trials are key for the progress of science and the correct interpretation of data. Rickard et al., in this journal, report that data on misidentification rates in medical trials are scarce. In five trials involving more than 800 blood or histology specimens examined, data clarification forms (DCFs) were issued for 21% of instances, and 67% were related to sample identification. The authors suggest that a suitable number of de- recognized data points is critical. Moreover, a formalized process involving the specimen accession employed in routine care is key to mitigate recognition errors and their potential profound impact on clinical research and outcome. We fully agree with the authors and their report is highly relevant today that we face transformation in healthcare. We suggest that 3D barcoding may mitigate several issues on misidentification.