General Roadmap and Core Steps for the Development of AI Tools in Digital Pathology

Yasmine Makhlouf, Manuel Salto-Tellez, Jacqueline James, Paul O’Reilly*, Perry Maxwell

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Integrating Artificial Intelligence (AI) tools in the tissue diagnostic workflow will benefit the pathologist and, ultimately, the patient. The generation of such AI tools has two parallel yet interconnected processes, namely the definition of the pathologist’s task to be delivered in silico, and the software development requirements. In this review paper, we demystify this process, from a viewpoint that joins experienced pathologists and data scientists, by proposing a general pathway and describing the core steps to build an AI digital pathology tool. In doing so, we highlight the importance of the collaboration between AI scientists and pathologists, from the initial formulation of the hypothesis to the final, ready-to-use product.
Original languageEnglish
Article numbere1272
Number of pages8
Issue number5
Early online date20 May 2022
Publication statusEarly online date - 20 May 2022


  • artificial intelligence
  • digital pathology
  • human-AI interaction
  • diagnostic
  • machine learning
  • deep learning


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