How Can Artificial Intelligence Improve Clinical Pathology Testing?

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Table of Contents

Introduction
Artificial Intelligence-Based Image Analysis
Conclusion
References

Introduction

One of the largest problems in the field of pathology is human fallibility. However competent and experienced a medical specialist is, their judgment is inevitably clouded by subjective and unconscious biases. Bera et al. (2019) argue that even physicians with the same training and medical background can arrive at different diagnostic conclusions about the same patient. Recent technological advancements open the possibility of solving this problem by shifting the responsibility from the human mind to the computational power of machines. Artificial intelligence is a developing field that receives significant academic and scientific attention. AI-based image analysis and machine learning have the potential to improve diagnostics by removing the influence of human bias.

Artificial Intelligence-Based Image Analysis

Image analysis is a processing technique that extracts information from visual data. Doctors use the same method when looking for symptoms of a particular medical condition. For instance, when in order for a bone fracture to be diagnosed, an X-Ray image has to be consulted. However, before the physician can look at the image, it has to be uploaded and printed. Delegating this task to AI should make the entire process faster and more efficient since data will be created and interpreted by the same program (Colling et al., 2019). As a result, the procedure of scheduling patients’ clinical tests will be streamlined, with more time available for the administration of treatment.

The technology behind image analysis takes the form of uploading pre-defined parameters into the diagnostic software. The process itself revolves around the use of statistical methods. The program will analyze test images and calculate the probability of a specific diagnosis based on the pre-set settings. However, in order for the program to be able to analyze data, the source has to be digital. Therefore, replacing traditional microscopy with digital slides that can be processed by computer equipment is essential. However, the real benefit is the ability to generate 3D images without performing invasive medical procedures, such as biopsies (Bera et al., 2019). Subsequently, the implementation of AI-based equipment allows clinicians to analyze patients without sampling tissue.

Another technology made possible by AI research is machine learning. The software uses mathematical models and statistical tools to analyze input. These algorithms provide the basis for the learning of new data by machine. The most evident practical application is the AI’s potential to prognosis the most effective treatment. George et al. (2022) reference a study, in which a machine learning model performed exceptionally well at predicting a proper treatment course. The subsequent implication is that AI-based software can prescribe highly individualistic treatment to every patient. Moreover, as the program is not affected by emotions and uncertainty, it can administer treatment more accurately than human physicians can because it will judge the suitability of a particular treatment based on the pre-defined risk assessment.

Conclusion

Altogether, there are three distinct benefits that AI technology can bring to the diagnostic field. First, image analysis can accelerate the process of evaluation of lab tests, thus allowing more patients to receive corresponding treatment more quickly. Second, the implementation of technology that can be processed by AI might supplant invasive diagnostic procedures with precise 3D modeling. Finally, machine learning algorithms may enable AI to accurately calculate the most appropriate treatment while considering the individual specifics of each patient. Combined together, these innovations will improve diagnostic efficiency while minimizing the influence of human biases.

References

Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V., & Madabhushi, A. (2019). Artificial intelligence in digital pathology—New tools for diagnosis and precision oncology.Nature Reviews Clinical Oncology, 16(11), 703-715.

Colling, R., Pitman, H., Oien, K., Rajpoot, N., Macklin, P., CM-Path AI in Histopathology Working Group, Snead, D., Sackville, T., & Verril, C. (2019). Artificial intelligence in digital pathology: a roadmap to routine use in clinical practice.The Journal of Pathology, 249(2), 143-150.

George, R. S., Htoo, A., Cheng, M., Masterson, T. M., Huang, K., Adra, N., Kaimakliotis, H. Z., Akgul, M., & Cheng, L. (2022). Artificial intelligence in prostate cancer: Definitions, current research, and future directions.Urologic Oncology: Seminars and Original Investigations, 40(6), 262-270.

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How Can Artificial Intelligence Improve Clinical Pathology Testing?

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