[The environmental impact of digital technology and artificial intelligence, in the era of digital pathology]. / Impacts environnementaux du numérique et de l'intelligence artificielle, à l'heure de la pathologie digitale.

While digitization and artificial intelligence represent the future of our specialty, future is also constrained by global warming and overstepping of planetary limits, threatening human health and the functioning of the healthcare system. The report by the Délégation ministérielle du numérique en santé and the French government's ecological planning of the healthcare system confirm the need to control the environmental impact of digital technology. Indeed, despite the promises of dematerialization, digital technology is a very material industry, generating greenhouse gas emissions, problematic consumption of water and mineral resources, and social impacts. The digital sector is impacting at every stage: (i) manufacture of equipment; (ii) use; and (iii) end-of-life of equipment, which, when recycled, can only be recycled to a very limited extent. This is a fast-growing sector, and the digitization of our specialty is part of its acceleration and its impact. Understanding the consequences of digitalization and artificial intelligence, and phenomena such as the rebound effect, is an essential prerequisite for the implementation of a sober, responsible, and sustainable digital pathology. The aim of this update is to help pathologists better understand the environmental impact of digital technology. As healthcare professionals, we have a responsibility to combine technological advances with an awareness of their impact, within a systemic vision of human health.

[Neural network: A future in pathology?] / Intelligence artificielle : quel avenir en anatomie pathologique ?

Artificial Intelligence, in particular deep neural networks are the most used machine learning technics in the biomedical field. Artificial neural networks are inspired by the biological neurons; they are interconnected and follow mathematical models. Two phases are required: a learning and a using phase. The two main applications are classification and regression Computer tools such as GPU computational accelerators or some development tools such as MATLAB libraries are used. Their application field is vast and allows the management of big data in genomics and molecular biology as well as the automated analysis of histological slides. The Whole Slide Image scanner can acquire and store slides in the form of digital images. This scanning associated with deep learning algorithms allows automatic recognition of lesions through the automatic recognition of regions of interest previously validated by the pathologist. These computer aided diagnosis techniques are tested in particular in mammary pathology and dermatopathology. They will allow an efficient and a more comprehensive vision, and will provide diagnosis assistance in pathology by correlating several biomedical data such as clinical, radiological and molecular biology data.

[Impact of digital pathology implementation in Reunion Island]. / Intérêt de la pathologie numérique à La Réunion.

In recent decades, the major scientific advances in oncology have complexified anatomic pathology practice. Collaboration with local and national pathologists is essential for ensuring a high-quality diagnosis. Anatomic pathology is undergoing a digital revolution that implements whole slide imaging in routine pathologic diagnosis. Digital pathology improves diagnostic efficiency, allows remote peer review and consultations (telepathology), and enables the use of artificial intelligence. The implementation of digital pathology is of particular interest in isolated territories, facilitating access to expertise and therefore to specialized diagnosis. This review discusses the impact of digital pathology implementation in French overseas territories, particularly in Reunion Island.