The future of immunohistochemistry: advancing precision medicine through artificial intelligence-driven spatial proteomics and multiplexed diagnostic technologies
DOI:
https://doi.org/10.71152/ajms.v16i8.4674Keywords:
Immunohistology; Technological advancement; Multiplex labelling; Cancer research; Spatial biologyAbstract
Immunohistochemistry (IHC) is experiencing a significant transformation, placing it at the leading edge of precision medicine and innovative diagnostics for the future. It is estimated that the global IHC market will hit USD 4.15 billion by 2032, fueled by technological advancements and an increasing demand for precision diagnostics, with artificial intelligence (AI) and digital pathology changing the landscape by improving diagnostic accuracy, scalability, and efficiency of workflows. The application of IHC is rapidly broadening beyond conventional single-marker analyses to include advanced multiplexed spatial proteomics techniques that allow for the detection of 40 or more protein markers simultaneously within intact tissue structures at subcellular precision, providing new insights into the tumor microenvironment and cellular interactions. The integration of AI algorithms automates the quantification of biomarkers, minimizes subjectivity in interpretations, and speeds up turnaround times in high-volume labs. Furthermore, experts in computational pathology anticipate that many diagnostic tasks based on IHC will be fully automated by 2030. Future medical applications will utilize companion diagnostics to support personalized medicine approaches, especially in oncology, where IHC assesses patient eligibility for specific targeted therapies; spatial proteomics will also enable extensive tissue mapping at the single-cell level, promoting large-scale projects that enhance our comprehension of disease mechanisms and treatment responses. This technique is broadening its application across numerous subspecialties, including neuropathology, hematopathology, and diagnosing infectious diseases, while emerging technologies such as deep visual proteomics and the combination with transcriptomics are set to transform tissue-based diagnostics by merging protein expression data with genetic information for a truly comprehensive characterization of diseases.
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