Artificial Intelligence and the Pathologist: Future Frenemies?

Abstract

T he manuscript titled ''AlphaGo, deep learning, and the future of the human microscopist'' in this month's issue of the Archives of Pathology & Laboratory Medicine 1 describes the triumph of Google's (Mountain View, California) artificial intelligence (AI) program, AlphaGo, which beat the 18-time world champion of Go, an ancient Chinese board game far more complex than chess.The authors have hypothesized that the development of intuition and creativity combined with the raw computing of AI heralds an age where well-designed and well-executed AI algorithms can solve complex medical problems, including the interpretation of diagnostic images, thereby replacing the microscopist. Of note, in a prior work, the microscope was predicted to have a 75% chance of remaining in use for another 144 years. 2To support their hypothesis, the authors presented recent studies that compared the performance of nontraditional interpreters to those of experienced pathologists, in making accurate diagnoses (note: 1 author disclosed a significant financial interest in an AI company). One study examined the potential of using pigeons (yes, pigeons) for medical image studies, 3 wherein the pigeons engaged in a matching game of completely benign and unambiguously malignant breast histology images. Pigeons correctly classified images as benign or malignant 85% of the time. A separate image algorithm study was erroneously reported to differentiate between small cell and non-small cell lung carcinoma with the accuracy of expert pulmonary pathologists, but instead, multiple computational algorithms were used to subtype known non-small cell lung carcinomas and gliomas in separate experiments. 4 The accuracy rate of each algorithm approached 70% to 85%. We believe that this level of diagnostic accuracy in settings that lack complexity is an extremely poor replica of a human pathologist's diagnostic capabilities.So, will the data-digesting and 24 3 7 learning AI be capable of looking at an image and able to render a pathologic diagnosis? Before attempting to answer this, we caution against the difficulties of predicting the future. Much of our existence still rests on innovations that have remained unchanged because of their inherent simplicity, applicability, and trueness to purpose (eg, the wheel), proving the point that something new (and different) is not always something better. On the other hand, several established and incumbent technologies were quickly (albeit incompletely) eclipsed, often within a decade, by a challenger that was faster, more convenient, cheaper, or better for the need (eg, postal mail being replaced by electronic mail). In the latter context, we note that information technology and AI are clearly better at repetitive detailed tasks that require accuracy and speed than are humans, who often find such tasks mind-numbing, and consequently are error-prone.

Artificial Intelligence–Based Breast Cancer Nodal Metastasis Detection: Insights Into the Black Box for Pathologists

“…Despite some debate, people agree that computerassisted diagnosis using technologies such as deep learning could be used to augment pathologists' workflows. [24][25][26] One slide-level use case is to automatically flag negative or challenging slides for IHC staining before pathologist review as a way to streamline the pathologist's review process. 11 When used in this manner, all of the negative cases could be verified by up-front IHC, at a cost excess of 3 slides in both datasets studied (staining 83 slides to verify the 80 negative slides in Camelyon16 and 55 slides to verify 52 negatives in DS2).…”

Digital Era and Artificial Intelligence in Pathology: Basic Information

“…şampiyonu karşısında galip gelmesi ve 2016'da Google AlphaGo'nun 160.000 oyun permütasyonluk veri seti ile eğitilerek Go oyunu şampiyonu Lee Sedol'ü 4-1 alt etmesi, yapay zekâ ile ilgili heyecanı olağanüstü artırdı. 8,9 Günümüzde sıkça duyduğumuz "yapay zekâ", insanlar tarafından makinelere kazandırılan, deneyimleri hatırlama, bunlardan öğrenme, düşünme, oluşturma, yargılama ve karar verme becerisi olarak tanımlanmaktadır. [4][5][6]10 Bir makinenin, insan tarafından belli bir göreve yönelik olarak yüklenen çok sayıda veriyi çeşitli yöntemler ile "öğrenmesi" ve insanın istediği bu görevi gerçekleştirebilmesi amaçlanır.…”

“…5 Patolojide yapay zekâ uygulamalarının temelinde görsel paternlerin CNN yöntemi aracılığı ile tanınması [yani görsel görüntü analizi (visual image analysis)] ve dijital görüntülerden anlamlı bilgilerin görüntü işleme teknikleri ile çıkartılması yer alır. 5,9 Bunun gerçekleştirilmesi için önce sağlam bir dijital patoloji alt yapısının kurulmuş olması ve çok miktarda kaliteli verinin uzmanlar tarafından işaretlenmesi gerekir. 2,4,6,11 Dijital patoloji uygulaması, konvansiyonel teknikle hazırlanmış lamların WSI teknolojisine dayalı özel bir tarayıcı ile taranması ve dijital ortama aktarılması ile başlar.…”

Artificial Intelligence – Consumers and Industry Impact

“…Artificial intelligent technologies seem promising in healthcare because they have the potential to improve quality of life and health consequences for a substantial number of patients in future years [8]. However, this is only true if these individuals obtain patient, doctor, and nurse trust, as well as, the removal of commercial, regulatory, and policy barriers.…”