Artificial intelligence and digital pathology: clinical promise and deployment considerations

Abstract

Artificial intelligence (AI) presents an opportunity in anatomic pathology to provide quantitative objective support to a traditionally subjective discipline, thereby enhancing clinical workflows and enriching diagnostic capabilities. AI requires access to digitized pathology materials, which, at present, are most commonly generated from the glass slide using whole-slide imaging. Models are developed collaboratively or sourced externally, and best practices suggest validation with internal datasets most closely resembling the data expected in practice. Although an array of AI models that provide operational support for pathology practices or improve diagnostic quality and capabilities has been described, most of them can be categorized into one or more discrete types. However, their function in the pathology workflow can vary, as a single algorithm may be appropriate for screening and triage, diagnostic assistance, virtual second opinion, or other uses depending on how it is implemented and validated. Despite the clinical promise of AI, the barriers to adoption have been numerous, to which inclusion of new stakeholders and expansion of reimbursement opportunities may be among the most impactful solutions.

Keywords: computational pathology; digital pathology; image analysis; machine learning; whole-slide imaging.

Fig. 1

Clinical impact of digital pathology, computational pathology, and AI on the traditional AP workflow: (a) traditional AP workflow and (b) AI-enhanced AP workflow. Dark blue denotes manual tasks, and light blue denotes AI-enhanced/automated tasks. Note that the AI-enhanced workflow assumes the application of many different AI tools used in tandem, with both the individual tools and the multi-staged clinical workflows all validated for clinical use by the pathology practice.

Fig. 2

Examples of three classes of AI employment in AP: (a) quantitative immunohistochemistry, in which cell nuclei are detected, and biomarker staining intensity is quantified; (b) heat map overlaid on a low power digital image of H&E stained tissue to direct the pathologist’s gaze to the presence of a histologic feature of interest; and (c) slide-level characterization of a prostate biopsy based on integration of regional classifications.