Unleashing the potential of AI for pathology: challenges and recommendations

Abstract

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer‐reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre‐clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Computational pathology in cancer diagnosis, prognosis, and prediction – present day and prospects

“…The road to clinical adoption of computational pathology is paved with a number of challenges, summarised in Table 1. Whilst a more detailed review can be found in the corresponding computational pathology article of this annual review issue by Professor Rajpoot's group [68], we highlight some key areas of concern focused on the pathologists’ perspective.…”

Recent Advances in Pathology: the 2023 Annual Review Issue of The Journal of Pathology

“…They conclude by suggesting developments to improve these techniques for acceptance in the clinical and research settings. The second review in this section is contributed by Nasir Rajpoot and colleagues from the University of Warwick, Histofy Ltd, and The Alan Turing Institute in the UK [5]. This review provides a detailed summary of the latest developments in AI techniques applied for computational pathology, and gives informative insights for identifying specific challenges and problems that require resolution to allow clinical implementation of models, including the need for large, well-curated datasets and uniform standards and regulatory policies.…”