Introduction:

The process of scanning glass slides using a whole slide image scanner and then evaluating the digitised pictures with an image viewer, usually on a computer display or mobile device, is known as digital pathology. An image viewer functions in the same way as a regular light microscope, allowing pathologists to move slides around in the same way. The viewing capabilities of equipment have not altered significantly, but digital pathology has resulted in significant improvements in pathology lab efficiency, workflow, and income.

Digital Pathology initially developed from adding the camera to the microscope’s lens through the invention of the first digital scanner to what we have today: technology. It is rapidly becoming necessary in the anatomic laboratory. The original scanners were bulky devices with big footprints, long processing times (up to 6 minutes), high storage costs, and restricted applications. Today’s scanners can scan slides in as little as 30 seconds, have several magnification settings, and handle up to 1,000 slides at once. Storage on the cloud is now inexpensive, secure, and easily accessible. Artificial intelligence (AI)-enabled computational applications to provide a plethora of methods to evaluate and present data.

Telepathology has progressed in a similar way as the cell phone. Early versions were unreasonably large, odd, and restricted in terms of scope, expense, and need. The mobile phone has now become as common as the watch, and it is being utilized in several ways which was hard to predict in the early 1980s. Telepathology has grown in popularity as whole-slide image scanners have become more common, just like mobile phone usage has increased as more powerful devices have been available.

Future of Digital Pathology

As AI-enabled telepathology advances, it will most likely follow most technological curves, with prices decreasing and the number of applications increasing. The shortage of pathologists, along with an increase in biopsy volume, necessitates the development of more efficient labs. Furthermore, integration with other AI applications, such as those in radiology and MRI, will allow digital pathology tools to be used as part of integrated diagnostics and predictive decision-making.

Upon case signout, some labs have already set up their digital pathology system to auto-load chosen pictures from their radiology PACS (Picture Archiving Communication System), allowing treating physicians to examine and browse the same slide on which the patient’s diagnosis is based. Radiologists may see radiographs, MRI and CT scans, as well as the accompanying histology slides, all in one screen.

With so many laboratories achieving fantastic results by using digital pathology, the challenges of expanding server storage and reconfiguring infrastructure appear to be less intimidating than previously. More laboratories will dust off their scanners and re-engage in the discussion of moving to digital pathology. Labs will see more significant advances in workflow efficiency as digital and computational pathology use develops, ushering in a new era of cooperation. What will be the final result? Patients will benefit from faster, more accurate diagnoses as well as a larger amount of information available to their treating providers.

To supplement the pathologist’s job, AI programs can “read” an entire slide picture and use specialized algorithms that can perform a variety of valuable clinical activities. It is generally established that pathology pictures include important prognostic information. Software tools can now measure characteristics of tissue that are frequently unseen to humans, even when viewed under a microscope, to predict the likelihood of a diagnosis, tumor aggressiveness, and, ultimately, patient outcomes. Under the umbrella of computational pathology, these AI applications are redefining why laboratories are increasingly adopting digital pathology. The same technologies may be used to decrease malpractice by providing a second perspective, recognizing patterns that the human eye misses, and alerting pathologists to any inconsistencies.

Other use cases, like sorting and workload balancing, can be handled in the background by computer programs, making the laboratory process more efficient and maximizing pathologists’ time. An AI program, for example, can automatically classify tissue samples by disease status and then route them to specialized pathologists for examination. A more senior pathologist may evaluate the less complicated cases first, storing the more difficult samples for later in the day.

A pathologist, on the other hand, may delegate simple cases to less experienced colleagues while taking on more challenging cases right away. Finally, there are now ways to increase revenue by attracting customers who wish to read their own cases via digital pathology.

Conclusion

Overall, pathologists are unlikely to be replaced by artificial intelligence in the near future. In recent years, software and hardware improvements have accelerated, and the first applications in pathology are being evaluated, although they only cover a small portion of this very complicated discipline of pathology.

In addition to reducing human error, telepathology may produce exact diagnostic results in a short amount of time. These techniques allow it to speed up typical operations without sacrificing the quality of each task’s completion.

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