The impact AI will have on reimbursement for pathology services is also another significant consideration towards its future deployment. In the United States, the American Medical Association has officially recognized advances in AI and has updated the Current Procedural Terminology (CPT) codes to reflect these changes. For example, CPT codes 88365 to 88368 allow billing for computer-assisted in situ hybridization morphometric analysis while CPT code 88361 allows for an elevated charge for computer-assisted quantitative measurement of immunohistochemical markers. As a result of these special billing codes, dedicated FDA (U.S. Food and Drug Administration) approved\/ cleared instruments with vendor validated algorithms are commercially available for breast biomarkers and Ki67 from several vendors. Further, the Center for Medicare and Medicaid Services (CMS) also provides alternative mechanisms for new, but costly, medical technologies. At the time of this writing, Viz ContaCT, a large vessel occlusion detection algorithm for computed tomography angiograms, received CMS New Technology Add-on Payment designation [16]. Although this specific algorithm does not pertain to pathology directly, it does represent a significant milestone in the quest for clinically-based AI algorithms becoming reimbursable in the USA, paving a similar pathway for AI algorithms in pathology.<\/p>\n

Besides creating additional direct revenue generating charges, DP coupled with AI holds immense potential to revolutionize other areas of medicine, such as oncology and precision medicine. For example, pairing histopathology-based image analysis data with molecular\/genomics data has the potential to yield new, unforeseen relationships when subjected to unsupervised ML techniques. This pairing has already started to pay off, with a study showing how mutation predictions for non-small cell lung cancer can be made using deep learning algorithms trained on histopathology images [17]. On a more practical level, simply automating and standardizing manual and subjective image analysis tasks may have downstream patient care effects besides increasing pathologist throughput or improving case turnaround times. Instead, automation and standardization of pathologist tasks such as the assessment of tumour necrosis, detection of lymphovascular invasion, quantification of mitotic figure counts per square area of tumour, or characterizing the full extent of fibrosis could affect clinically impactful measures like tumour grade, patient treatment options and prognosis.<\/p>\n<\/div><\/section>
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