Application of AI in Pathological Diagnosis

Artificial intelligence (AI) is revolutionizing the way diseases are diagnosed with its unique advantages, significantly improving the accuracy and efficiency of diagnosis. Here, we cite a review of literature that synthesizes the applications and advances of AI in the field of diagnostic pathology.

This article discusses multiple potential applications of AI tools in pathology, including but not limited to:

Image analysis: AI can identify specific features of cells and tissues, such as nuclear shape, tissue texture and structure, and tumor microstructure.

Disease diagnosis: AI predicts disease through image pattern recognition, e.g., using image features to distinguish between different types of cancer.

Prognostic assessment: AI can predict a patient's prognosis based on histological features of tumors, such as lung and breast cancer.

Treatment response prediction: AI helps predict the patient's response to specific treatments, such as immunotherapy.

AI in Diagnostic Pathology.Fig. 1. AI/ML tools are used to analyze tumor images. (Shafi S.; et al. 2023)

Machine learning algorithms may or may not use pathologists' and oncologists' inherent domain knowledge. Through the use of domain-independent features, we are able to achieve better image characterization of a wide range of diseases and tissue types. It has been used for diagnosis, prognosis, grading, and prediction of treatment response for a variety of cancers, including breast cancer, prostate cancer, and brain tumors.

AI in Clinical Pathology Practice

AI performs multiple tasks in the diagnostic pathology workflow and has significantly changed the way cancer is diagnosed and classified. AI tools can help standardize scoring criteria for a wide range of tumors.

A variety of AI tools have been used to provide information that is difficult for pathologists to recognize, such as accurate and objective assessment of immunohistochemical biomarkers, quantification of cell counts, and assessment of the spatial arrangement of cells.

The AI tool Content-Based Image Retrieval (CBIR) enables pathologists to search for images similar to the image under discussion from large histopathology database repositories. This has significant implications for guiding pathologists in the diagnosis of rare and complex cases occasionally encountered in clinical practice.

AI in Diagnosis

AI algorithms can be incorporated into digital pathology workflows as stand-alone reporting algorithms, assisted diagnostic tools, and automated quantifiers of specific features.

AI in Prediction and Prognosis

AI can be used to predict prognosis and treatment response based on histologic features. AI tools predict clinical outcomes, probability of recurrence or metastasis, and treatment response by integrating multiple morphological features.

Although the use of AI in pathology faces a number of challenges, including quality and accessibility of data, interpretability and transparency of algorithms, and regulatory and ethical issues. However, as technology advances, we can foresee AI playing an increasingly important role in pathology, providing patients with faster, more accurate diagnoses and more personalized treatment options.

Protheragen-ING AI-Pharma provides customized AI-assisted diagnostic tool services dedicated to meeting various diagnostic needs in oncology, cardiology, neurology, and many other medical fields. If you are interested in our services or have a question, please feel free to contact us for more details.

Original Article:

Shafi S.; et al. (2023). Artificial intelligence in diagnostic pathology. Diagnostic pathology. 2023, 18(1): 109.

Services Related in the Article:

Diagnostic Tool Development

Inquiry

logo

We harness the power of artificial intelligence to transform the landscape of drug discovery. Our mission is to accelerate the development of life-saving medicines by leveraging cutting-edge AI technologies.

CONTACT US
  • Tel:
  • E-mail:
  • Address:
Top