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Top 3 Benefits & Use Cases of AI in Pathology in 2024

Written by
Shehmir Javaid
Shehmir Javaid
Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple.

He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers.

He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations.

He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.
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Pathology is one of the most important fields in the healthcare sector. It studies the effects and causes of disease and injuries. Pathologists combine science and medicine to study and diagnose different diseases and injuries. For example, a pathologist will study a lump in someone’s arm to check if the patient has cancer or not.

Currently, the pathology sector faces various challenges including rising demand and a shortage of workers. Digital solutions such as AI & ML are helping to overcome these challenges by enabling improvements in many areas of the field.

This article explores key benefits and top use cases of AI/ML in the field of pathology, to help healthcare professionals and business managers make better decisions regarding future investments in the technology.

How is AI improving pathology?

Like many other areas in the healthcare sector, AI/ML is helping improve pathology in the following ways:

More accuracy

Humans have a limited level of accuracy and ability to analyze and process things. AI/ML can give pathologists the ability to go beyond those limitations. Some of the main tasks a pathologist performs are detection, classification, segmentation, and quantification of disease, and AI/ML models can help improve the accuracy of these tasks. 

For instance, an AI/ML model combined with computer vision can help examine the cells extracted from a prostate core needle biopsy with high accuracy. AI/ML models ensure that all layers and pixels of the specimen’s image are analyzed to detect even the smallest tumors or anomalies (see Figure 1).

Figure 1. Tumor detection and classification in lung cancer

An example of a small tumor cell detected by machine learning models

Source: mdpi

More productivity

Apart from a more accurate analysis of medical images, AI also helps pathologists increase productivity. With AI, the pathology sector can fulfill more demand with the existing number of staff and overcome the issue of pathologist shortage. AI can provide the following benefits:

  • Reduced human errors in specimen handling and processing
  • Better workload management for pathologists
  • Quicker patient turnaround
  • Quality assurance
  • Automatic request sending of certain tests and automated reporting

At the Hospital Campus de la Salud in Granada, Spain, AI-powered digital pathology solutions, created by Philips and Ibex Medical Analytics were implemented to increase the productivity of the pathologist team by 21%.

There is a 21% increase in diagnostic capacity when AI-powered solutions are used compared to microscopy.

The technology-enabled the team of 23 pathologists to analyze over 280,000 tissue samples each year.

What are some use cases of AI in pathology?

This section highlights some applications of AI in pathology with real-world examples:

Automated mitotic figure quantification

Mitotic figure quantification is a process of increasing accuracy in tumor detection. Studies show that AI-enabled automation in breast cancer mitosis quantification reduced overall process time by 27.8% increasing efficiency and accuracy.

Resolve the tissue floater issue

In the field of pathology, cross-contamination by tissue floaters on glass sliders is a significant issue. As seen in Figure 2, the main tissue being analyzed is A, and the other two contaminants are B and C. 

Studies show that using an AI-powered image search tool can offer pathologists a more efficient method to resolve the tissue floater problem. This system can focus on the main sample being analyzed and ignore other contaminants.

Figure 2. Tissue floaters/cross contaminants in a glass slider

An example of tissue contaminants examined by AI-powered image search tools.

Patient management

Another application of AI in pathology is patient management. In a digital laboratory, AI systems can analyze the data and medical images attached to a patient and route them to a pathologist for further consultation based on specified criteria.

An automated system can:

  • Verify the digital medical images attached to the case
  • Identify cases highlighted as stat
  • Validate the ordered tests and ensure that they match the specimen type

This can significantly increase the operational efficiency and turnaround time in a laboratory.

Watch how AI is revolutionizing the field of pathology.

You can also check out data-driven lists of:

Further reading

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Cem Dilmegani
Principal Analyst
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Shehmir Javaid
Industry Research Analyst
Shehmir Javaid in an industry & research analyst at AIMultiple. He is a frequent user of the products that he researches. For example, he is part of AIMultiple's DLP software benchmark team that has been annually testing the performance of the top 10 DLP software providers. He specializes in integrating emerging technologies into various business functions, particularly supply chain and logistics operations. He holds a BA and an MSc from Cardiff University, UK and has over 2 years of experience as a research analyst in B2B tech.

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