Surgical pathology and sustainable development: international landscape and prospects.

The healthcare sector significantly contributes to global greenhouse gas emissions, with surgical pathology (SP) playing a notable role. This review explores the ecological transformation of SP, offering a global overview of existing challenges and sustainable initiatives worldwide.While some countries, such as the UK and France, have developed national strategies to reduce the carbon footprint of healthcare, including SP, many regions remain at an early stage of implementing green practices.

[The environmental impact of digital technology and artificial intelligence, in the era of digital pathology].

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 carbon footprint of a surgical pathology laboratory in France.

Objectives: The health sector contributes to climate disruption through greenhouse gas (GHG) emissions. It accounts for 8% to 10% of France's GHG emissions. Although the medical community has been alerted to the problem, more data are needed.

Interpretable HER2 scoring by evaluating clinical guidelines through a weakly supervised, constrained deep learning approach.

The evaluation of the Human Epidermal growth factor Receptor-2 (HER2) expression is an important prognostic biomarker for breast cancer treatment selection. However, HER2 scoring has notoriously high interobserver variability due to stain variations between centers and the need to estimate visually the staining intensity in specific percentages of tumor area. In this paper, focusing on the interpretability of HER2 scoring by a pathologist, we propose a semi-automatic, two-stage deep learning approach that directly evaluates the clinical HER2 guidelines defined by the American Society of Clinical Oncology/ College of American Pathologists (ASCO/CAP).