Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry

Abstract

Artificial intelligence (AI) is a reality of our times, and it has been successfully implemented in all fields, including medicine. As a relatively new domain, all efforts are directed towards creating algorithms applicable in most medical specialties. Pathology, as one of the most important areas of interest for precision medicine, has received significant attention in the development and implementation of AI algorithms. This focus is especially important for achieving accurate diagnoses. Moreover, immunohistochemistry (IHC) serves as a complementary diagnostic tool in pathology. It can be further augmented through the application of deep learning (DL) and machine learning (ML) algorithms for assessing and analyzing immunohistochemical markers. Such advancements can aid in delineating targeted therapeutic approaches and prognostic stratification. This article explores the applications and integration of various AI software programs and platforms used in immunohistochemical analysis. It concludes by highlighting the application of these technologies to pathologies such as breast, prostate, lung, melanocytic proliferations, and hematologic conditions. Additionally, it underscores the necessity for further innovative diagnostic algorithms to assist physicians in the diagnostic process.

Keywords: artificial intelligence; computer-aided diagnosis; computer-assisted image analysis; digital pathology; immunohistochemistry; pathology.

Figure 1

Illustration of the tissues with reported AI pathologic applications (AI—Artificial Intelligence; CUPs—Cancers of Unknown Primary Origin; ER—Estrogen Receptor; PR—Progesterone Receptor; Her2/neu—Human Epidermal Growth Factor Receptor 2; Ki67—proliferation index; TILs—Tumor-Infiltrating Lymphocytes; PD-L1—Programmed Death-Ligand 1; TNFAIP8—Tumor Necrosis Factor Alpha-Induced Protein 8).

Figure 2

Steps adopted to reduce incorrect cell identification and improve diagnosis.