Revolutionizing Pathology with Artificial Intelligence: Innovations in Immunohistochemistry.

Area of Science:

• Medical Informatics
• Computational Pathology
• Digital Pathology

Background:

• Artificial intelligence (AI) is increasingly integrated into medicine, with pathology being a key area for precision medicine advancements.
• Accurate diagnoses in pathology are crucial and can be significantly improved through AI-driven algorithmic development.
• Immunohistochemistry (IHC) is a vital diagnostic tool in pathology, offering opportunities for AI augmentation.

Purpose of the Study:

• To explore the applications and integration of AI software and platforms in immunohistochemical (IHC) analysis.
• To highlight how AI, including deep learning (DL) and machine learning (ML), can enhance the assessment of IHC markers.
• To discuss the role of AI in improving diagnostic accuracy and guiding therapeutic strategies in pathology.

Main Methods:

• Review of current AI software and platforms utilized in IHC analysis.
• Exploration of deep learning (DL) and machine learning (ML) algorithms for IHC marker assessment.
• Analysis of AI applications across various pathological conditions.

Main Results:

• AI algorithms are being developed and implemented to analyze IHC markers, aiding in diagnostic accuracy.
• AI facilitates targeted therapeutic approaches and prognostic stratification through enhanced IHC analysis.
• AI applications show promise in diverse pathologies including breast, prostate, lung, melanocytic, and hematologic conditions.

Conclusions:

• AI significantly enhances immunohistochemistry analysis in pathology, leading to more accurate diagnoses.
• Further development of innovative AI diagnostic algorithms is necessary to support physicians.
• AI integration in pathology promises advancements in precision medicine and patient care.