Digital Pathology is a revolutionary innovation that is transforming the way labs conduct their testing. However, it has its fair share of challenges and early adopters. Learn about these challenges and what the future holds for this rapidly evolving technology. You’ll also learn about its impact on quality assurance and patient care.
Digital pathology in lab testing presents both advantages and challenges. It has the potential to improve diagnostic services for patients, while reducing operational costs. Many factors need to be considered to successfully implement this technology. Among these are time, cost, and clinical benefits. Moreover, digital pathology may provide opportunities for consultations outside the scope of primary diagnosis.
The advantages of digital pathology include the availability of dense data, which will help clinicians make better decisions and optimize patient care. It is also important to consider that multimodal data fusion will help doctors make personalized medicine recommendations. To this end, our group has studied the correlation between protein expression and histological image measurements. As a result, we have developed a combined classifier that is able to predict CaP recurrence.
The pathology industry has always needed digital pathology but the process has been hampered by overregulation, tight reimbursement, and lack of evidence for cost savings. The industry also has a shortage of personnel and an increasing caseload. Although the digital pathology market is growing at an impressive rate, adoption rates in the US are still low. In addition, the industry was put on hold by the COVID-19 virus, which shut down hospitals nationwide. As a result, vendors were concerned about disruption of the market and logistical challenges involved in deployment of scanners.
In fact, the accelerated adoption of digital pathology is due in part to the need for remote diagnostics during a pandemic. Ongoing improvements in AI-based diagnostic algorithms will further fuel adoption. This new technology will facilitate the rapid transition from analog to digital workflow for all diagnostic stages, from specimen imaging to analysis and archiving. As healthcare provision shifts toward value-based care, the emphasis on operational efficiency is likely to grow. The growing use of analytics and machine learning in healthcare will also accelerate the transition to digital pathology.
Impact on quality assurance
Digital pathology has many potential benefits for quality assurance. However, adoption has been slow, and there have been many failed business cases. There are many challenges to digital pathology implementation, including a lack of available specialists to conduct the work. Here are some things to keep in mind when considering its adoption.
To begin, digital pathology requires a secure server to store images. It also allows pathologists to collaborate remotely with colleagues, viewing digital images on computer monitors. Although many health care organizations are exploring digital pathology, there are many issues that need to be resolved before widespread adoption can begin. The current adoption rate is low, and projections of a full switch to digital workflows are conservative. As a result, scalable digital pathology deployment is needed to ensure the effectiveness of the technology.
Impact on image interpretation
Digital pathology can help address several current challenges in pathology laboratories. First, it eliminates the need for shipping individual slides to distant experts. In the past, this required weeks to respond to requests, which limited collaboration. Now, multiple pathologists can consult on the same image and make a diagnosis in a matter of minutes.
Second, digital pathology images allow for the use of artificial intelligence and machine learning to enhance image analysis. This technology makes it easier to manipulate images than physical tissue, and its computational power has made it possible to automate tasks such as interpreting images. Third, digital pathology can be used in research, as academic medical centers are building massive digital biobanks to train algorithms and feed clinical studies.