Data Standardization in the Medical Field Through FHIR and FAIR Implementation: A Systematic Review.

The authors investigate in this paper the current situation of the FHIR resources adoption in order to FAIRify data in the medical research field. By aligning with the FAIR data principles, data becomes easier to share and reuse. This review aims to analyze how integrating the FHIR resources improved the findability, accessibility, interoperability, and reusability of datasets. By searching for the state-of-art situation in this field, we want to emphasize the significant role that FAIR data occupies in the medical research community, by also providing directions for further development and improved interoperability.

A Digital Tool Supporting Pathology Practice and Identifying Leucocytes.

The aim of this work is to deliver an all-in-one package that contains both the part where the pathologist can manipulate the data as well as predefined models, altogether with the digital pathology interface with a comprehensive component that provides traceability between the identified leucocytes and the underlying possible outcomes of the potential disease. The aim is to directly provide the number of leucocytes and the mass of the cell, only from the image, with minimal intervention from the pathologist, necessary to have a PoC (Proof of Concept) or a prototype. The model was trained on a dataset of around 20,000 models, and the achieved accuracy was approximately 85%. Approximately 82% of the identified areas of interest, as determined by the models, were true positive predictions. The models correctly identified approximately 89% of the actual positive instances - areas of interest - identified by the pathologist. Approximately 6% of the total actual negative instances were incorrectly classified as positive by the models. The tool provides visual scripting, reducing the learning curve for pathology analysis techniques and offers an intuitive interface for healthcare professionals.

Explaining Deep Learning Models Applied in Histopathology: Current Developments and the Path to Sustainability.

The digital pathology landscape is in continuous expansion. The digitalization of slides using WSIs (Whole Slide Images) fueled the capacity of automatic support for diagnostics. The paper presents an overview of the current state of the art methods used in histopathological practice for explaining CNN classification useful for histopathological experts. Following the study we observed that histopathological deep learning models are still underused and that the pathologists do not trust them. Also we need to point out that in order to get a sustainable use of deep learning we need to get the experts to trust the models. In order to do that, they need to understand how the results are generated and how this information correlates with their prior knowledge and for obtaining this they can use the methods highlighted in this study.