The brightfield microscope is instrumental in the visual examination of both
biological and physical samples at sub-millimeter scales. One key clinical
application has been in cancer histopathology, where the microscopic assessment
of the tissue samples is used for the diagnosis and staging of cancer and thus
guides clinical therapy. However, the interpretation of these samples is
inherently subjective, resulting in significant diagnostic variability.
Moreover, in many regions of the world, access to pathologists is severely
limited due to lack of trained personnel. In this regard, Artificial
Intelligence (AI) based tools promise to improve the access and quality of
healthcare. However, despite significant advances in AI research, integration
of these tools into real-world cancer diagnosis workflows remains challenging
because of the costs of image digitization and difficulties in deploying AI
solutions. Here we propose a cost-effective solution to the integration of AI:
the Augmented Reality Microscope (ARM). The ARM overlays AI-based information
onto the current view of the sample through the optical pathway in real-time,
enabling seamless integration of AI into the regular microscopy workflow. We
demonstrate the utility of ARM in the detection of lymph node metastases in
breast cancer and the identification of prostate cancer with a latency that
supports real-time workflows. We anticipate that ARM will remove barriers
towards the use of AI in microscopic analysis and thus improve the accuracy and
efficiency of cancer diagnosis. This approach is applicable to other microscopy
tasks and AI algorithms in the life sciences and beyond