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Deep learning is a state-of-the-art pattern recognition technique that has been found extremely powerful for the analysis of digitized histopathological slides. The number of studies presenting highly promising results for solving diagnostic tasks in histopathology has grown exponentially over the last few years. Examples are subtyping of lung and skin tumors, breast and prostate cancer grading, and detection of metastases. Unfortunately, few studies so far include an external validation using large, independent cohorts, let alone study the true clinical usefulness in prospective studies. As a result, the balance between promise and hype in public opinion may be skewed.
In this talk, I will present some of the current possibilities of AI for histopathology, and discuss potential future developments. I will also address the challenges that have to be overcome before we can deliver true value to pathologists, patients, and the healthcare system. These are in many cases of a non-technical nature. Issues related to the availability of large heterogeneous data sets, possibilities to obviate expensive manual labeling of data, workflow integration, ethical, legal and regulatory issues, explainability, and reimbursement models all lie on the way forward for full adoption of computational pathology.
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