This result leads to the formalization of the concept of clinical-grade decision support systems, proposing, in contrast with the existing literature, a new measure for clinical applicability (which is one of the several reasons to implement digital pathology in routine practice). The results obtained are, in terms of sensitivity and specificity, very encouraging with a sensitivity of 100% (i.e., no false-negative case) for the prostate dataset. This is a step forward in the development of AI tool, thus overpassing the major limitation in obtaining consistent results due to the labor-intensive annotation process. propose a framework for training classification models on a very large scale without the need for pixel-level annotations. The results are based on almost a total of 45,000 slides, which represents one of the largest datasets evaluated up to now. In their article, the authors collected three datasets of slides: (1) a prostate core biopsy dataset (2) a skin dataset and (3) a breast metastasis to lymph node dataset in order to develop a deep learning model to obtain a well-accurate prediction (on the level of benign vs. Moreover, the authors give us a more stringent and a more practical definition of clinical-grade performance of AI tools in routine.
shows us that computers, CNN, and AI are things that can be implemented in the pathology laboratories.
But, when and how to use these new words in our day-to-day practice remains unclear. Nowadays, words such as computational pathology, deep learning, convolutional neural network (CNN), and AI are as common words as “differential diagnosis” and “immunohistochemistry” in our pathologist's dictionary.
By citing the oneiric movie Blade Runner, “I've seen things that you people wouldn't believe,” computers are now rendering a histological diagnosis, predicting genomic mutations by just analyzing whole-slide images stained with hematoxylin and eosin. It has been suggested that with the advent of the artificial intelligence (AI), we are approaching the third revolution in pathology. Many things have changed in the last few years in the field of surgical pathology. Clinical-grade Computational Pathology: Alea Iacta Est.