Ecologically sustainable benchmarking of AI models for histopathology.

Deep learning (DL) holds great promise to improve medical diagnostics, including pathology. Current DL research mainly focuses on performance. DL implementation potentially leads to environmental consequences but approaches for assessment of both performance and carbon footprint are missing.

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.

Annotation practices in computational pathology: a European Society of Digital and Integrative Pathology (ESDIP) survey study.

Integrating digital pathology (DP) and artificial intelligence (AI) algorithms can potentially improve diagnostic practice and precision medicine. Developing reliable, generalizable, and comparable AI algorithms depends on access to meticulously annotated data. However, achieving this requires robust collaboration among pathologists, computer scientists and other researchers to ensure data quality and consistency.