Though burnout due to COVID-19 pandemic plays a role, the future is bright for pathology assistants
Anatomic pathology laboratories are expanding the role of Pathologist Assistants (PathAs) beyond the traditional duties. What does that mean for the future of this critical position? In an article she penned for the College of American Pathologists (CAP), certified pathologists’ assistant Heather Gaburo, MHS, PA(ASCP)cm, explains how PathA responsibilities are evolving to meet the needs of today’s surgical pathology suite and anatomic pathology service.
The PNPL in Woodbridge, Connecticut, funded the study and worked with various pathology laboratories to gather the information presented.
In her paper published in the Archives of Pathology and Laboratory Medicine, certified pathologists’ assistant Heather Gaburo (above), wrote “PathAs can fill a wide variety of nontraditional roles in hospital-based and private practice laboratory settings. In the current state of pathology, PathAs are underused in these roles.” (Photo copyright: American Association of Pathologists’ Assistants.)
Traditional Duties of PathAs
The job of the PathA was developed in the 1970s to fill a gap in the pathology workforce. Traditional duties for PathAs include, but are not limited to, tasks such as:
Macroscopic examination (grossing process) and dissection of surgical specimens,
Assisting with intraoperative frozen sections and autopsies.
However, this role is expanding. According Gaburo, the 2021 AAPA membership survey showed that PathAs duties have grown to include tasks such as:
Why have the duties of PathAs broadened so much? According to Gaburo, the COVID-19 pandemic had much to do with it.
COVID-19 Pandemic Leads to New Duties/Burnout for PathAs
“The pandemic increased public awareness of the clinical laboratory by highlighting essential clinical workers with frequent spotlights on COVID-19 testing and staffing shortages, as well as understaffing in the anatomic space,” Gaburo said in an exclusive interview with Dark Daily.
“COVID-19 caused delays in cancer screening and non-emergency surgery, which led to a backlog of cases and delayed cancer presentations. Some studies have shown an increase in late-stage cancer presentations, which can be more time-consuming to diagnose in pathology. Both factors are contributing to higher traditional workloads for PathAs,” she added.
The pandemic, according to Gaburo, also led to increased duties for PathAs. “The pandemic also provided PathAs with opportunities to assist in developing new protocols such as: handling surgical specimens from COVID-19 patients, enhanced safety procedures in the laboratory, and autopsies on SARS-CoV-2 patients.”
But, with this expansion of duties also comes with the threat of burnout. “I believe the pandemic contributed to the burnout of PathAs in several ways. Many labs faced staffing challenges as employees contracted COVID-19, straining the existing workforce,” she noted.
“Some personnel struggled to balance their jobs as essential workers with providing virtual schooling for their children. Workloads increased when surgical cases resumed to catch up with the patient backlog. The incoming specimens were more complex due to delays in screening and advanced disease at presentation,” Gaburo added.
Job retention is an issue also explored by Gaburo in her Archives of Pathology and Laboratory Medicine paper. “Almost half of the laboratory professionals (including PathAs) surveyed by the ASCP addressed being underappreciated, especially compared with nursing and other allied health professionals.” She goes on to cite the risks of worker burnout, including adverse errors that could lead to liability of healthcare organizations.
Gaburo notes that burnout was an issue for PathAs before the COVID-19 pandemic “possibly due to a lack of job diversity and opportunities for growth,” she said. But the COVID-19 pandemic provided a unique opportunity for many PathAs, as well.
“The pandemic, while it brought challenges, also provided opportunities for PathAs to step into new, temporary roles early on when surgeries were limited, and clinics were closed. This job diversification may have helped develop resiliency and decrease burnout.”
PathA Shortage and Educational Opportunities
The COVID-19 pandemic required the entire healthcare industry to be flexible and expand in a short time. This, according to Gaburo, contributed to the growth of PathAs’ duties and could have helped with job retention as well.
When asked whether there was a shortage of PathAs in clinical laboratories and anatomic pathology groups, Gaburo said, “Though there are many open jobs for PathAs, our profession is fortunate in that we are not experiencing the same type of shortage as other laboratory professions. Instead of struggling to fill vacant positions, it seems many of the PathA openings are newly created positions. In fact, the new graduate employment rate of most, if not all, PathA programs is 100%.”
However, pandemic-related stresses and burnout have led to a shortage of anatomic pathologists, Gaburo notes. But in this she also sees new opportunities for PathAs.
“This is an area where the utilization of pathologists’ assistants has value for pathologists. PathAs, with support and mentorship, can provide assistance in many areas at a lower cost than pathologists, freeing up the pathologists to devote more time to patient care activities.”
As Gaburo concludes in her paper, “PathAs are qualified allied health professionals capable of handling a wide range of nontraditional roles in the pathology laboratory.” She goes on to note how practices can choose to mentor and support their PathAs by offering them mentorship and diverse educational opportunities.
“Over the last 15 years, the number of training programs for PathAs has more than doubled, from seven to 15. Class sizes have also increased to meet the growing demand for admission, which has become more and more competitive.
“The curricula include basic laboratory management classes, and some programs are considering incorporating ‘Business of Pathology’ courses as well. Many programs have expanded their clinical rotation sites, leading to opportunities for experienced PathAs to move into nontraditional teaching roles by becoming preceptors. However, there is still a need for more high-level administrative training opportunities,” Gaburo wrote.
Job satisfaction and retention increases quality for everyone involved. As clinical laboratories and anatomic pathology groups continue to support COVID-19 testing on top of traditional laboratory requirements, pathologist assistants have proven—and will continue to prove—what a valuable asset they are to clinical pathology practices.
These findings hint at the role of pre-existing conditions in raising the risk of an individual having a severe case of COVID-19 once infected
At the University of Michigan, a team of pathologists have been researching the factors that might cause some patients infected by SARS-CoV-2 to suffer persistent respiratory problems, often described as “long COVID.” They have identified factors that place some individuals at higher risk for these problems.
Little is known about how the SARS-CoV-2 coronavirus affects the body long-term. Millions of people who have survived COVID-19 infections are living with chronic symptoms, including persistent respiratory problems such as shortness of breath. However, until now, it was not clear what may be causing these symptoms in some people but not others, even after the coronavirus has completely cleared their bodies.
Now, anatomic pathologists at Michigan Medicine, formerly the University of Michigan Health, believe they may have discovered what is causing ongoing respiratory problems in some patients who have recovered from the COVID-19 infection—pre-existing conditions.
The researchers examined lung biopsies from COVID-19 patients who continued to experience lingering symptoms. They discovered in some individuals lung damage that was present prior to contracting the virus.
“Some of the early publications and popular press around long COVID has implied or assumed that once you had COVID, everything that happens next is COVID-related,” said anatomic pathologist and senior author of the study Jeffrey Myers, MD (above), Vice Chair for Clinical Affairs and Quality at Michigan Medicine, in a news release. “Of course, that might or might not be true,” he added. (Photo copyright: University of Michigan.)
The research team analyzed lung biopsies from 18 COVID-19 survivors who were still experiencing respiratory symptoms or had abnormal computed tomography (CT) scans after the virus was no longer present in their bodies. The researchers found ground glass opacities on the radiological scans of 14 of those patients.
According to the news release, this finding indicates there were “areas of the lungs that appear as a cloudy gray color as opposed to the dark color of normal air-filled lungs, on a chest X-ray or CT scan.”
The biopsies exhibited evidence of pre-existing lung scarring and proof of diffuse alveolar damage, which is typically seen in patients with acute respiratory illnesses. Only five of the patients examined in the study were known to have lung disease prior to their COVID-19 diagnoses.
The researchers found that the most common condition present in these 18 patients was usual interstitial pneumonia (UIP). This condition, also known clinically as idiopathic pulmonary fibrosis (IPF), is a common form of pulmonary fibrosis that is characterized by progressive scarring and stiffening of both lungs.
“We were seeing a lot of UIP, which isn’t the pattern we tend to associate with acute lung injury,” said Kristine Konopka, MD, Clinical Associate Professor at Michigan Medicine and lead author of the study, in the news release. “So, we think these are patients who had lung disease prior to COVID and maybe they just weren’t being followed by primary care physicians. They then had COVID, are still sick, and their UIP is finally being picked up.”
Could Patients Have Lung Disease and Not Know it?
“The notion,” Myers noted in the news release, “that a person could have chronic lung damage and not know it was unheard of until relatively recently.” He also explained that UIP/IPF is a progressive disease that gets worse with time and that an infection like COVID-19 can accelerate the illness to a more serious condition known as an acute exacerbation of IPF, which can lead to death.
“SARS-CoV-2 comes along and does to the lung, from a pathology perspective, exactly what happens with an acute exacerbation,” Myers said.
The researchers also stated that it’s impossible to determine for certain whether the SARS-CoV-2 virus caused the UIP/IPF without the existence of full clinical histories of the patients prior to their COVID-19 diagnoses. They hope their research will motivate clinicians to be cautious before automatically attributing respiratory symptoms to long COVID in survivors of the virus. It is possible that the lung damage was present prior to the coronavirus.
“You shouldn’t make assumptions but [instead] ask the right questions, the first of which would be ‘I wonder if this is really COVID?’ What you do after that depends on the answer to that question,” he added.
This research is an example of how pathologists can add insight and value into the deeper understanding of the processes involved in specific diseases. Dark Daily invites any of our readers who are aware of other pathologist-authored studies or published papers about COVID-19 to alert us to the availability of those works.
MIT’s deep learning artificial intelligence algorithm demonstrates how similar new technologies and smartphones can be combined to give dermatologists and dermatopathologists valuable new ways to diagnose skin cancer from digital images
According to an MIT press release, “The paper describes the development of an SPL [Suspicious Pigmented Lesion] analysis system using DCNNs [Deep Convolutional Neural Networks] to more quickly and efficiently identify skin lesions that require more investigation, screenings that can be done during routine primary care visits, or even by the patients themselves. The system utilized DCNNs to optimize the identification and classification of SPLs in wide-field images.”
The MIT scientists believe their AI analysis system could aid dermatologists, dermatopathologists, and clinical laboratories detect melanoma, a deadly form of skin cancer, in its early stages using smartphones at the point-of-care.
“Our research suggests that systems leveraging computer vision and deep neural networks, quantifying such common signs, can achieve comparable accuracy to expert dermatologists,” said Luis Soenksen, PhD (above), Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in an MIT press release. “We hope our research revitalizes the desire to deliver more efficient dermatological screenings in primary care settings to drive adequate referrals.” The MIT study demonstrates that dermatologists, dermatopathologists, and clinical laboratories can benefit from using common technologies like smartphones in the diagnosis of disease. (Photo copyright: Wyss Institute Harvard University.)
Improving Melanoma Treatment and Patient Outcomes
Melanoma develops when pigment-producing cells called melanocytes start to grow out of control. The cancer has traditionally been diagnosed through visual inspection of SPLs by physicians in medical settings. Early-stage identification of SPLs can drastically improve the prognosis for patients and significantly reduce treatment costs. It is common to biopsy many lesions to ensure that every case of melanoma can be diagnosed as early as possible, thus contributing to better patient outcomes.
“Early detection of SPLs can save lives. However, the current capacity of medical systems to provide comprehensive skin screenings at scale are still lacking,” said Luis Soenksen, PhD, Venture Builder in Artificial Intelligence and Healthcare at MIT and first author of the study in the MIT press release.
The researchers trained their AI system by using 20,388 wide-field images from 133 patients at the Gregorio Marañón General University Hospital in Madrid, as well as publicly available images. The collected photographs were taken with a variety of ordinary smartphone cameras that are easily obtainable by consumers.
They taught the deep learning algorithm to examine various features of skin lesions such as size, circularity, and intensity. Dermatologists working with the researchers also visually classified the lesions for comparison.
When the algorithm is “shown” a wide-field image like that above taken with a smartphone, it uses deep convolutional neural networks to analyze individual pigmented lesions and screen for early-stage melanoma. The algorithm then marks suspicious images as either yellow (meaning further inspection should be considered) or red (indicating that further inspection and/or referral to a dermatologist is required). Using this tool, dermatopathologists may be able to diagnose skin cancer and excise it in-office long before it becomes deadly. (Photo copyright: MIT.)
“Our system achieved more than 90.3% sensitivity (95% confidence interval, 90 to 90.6) and 89.9% specificity (89.6 to 90.2%) in distinguishing SPLs from nonsuspicious lesions, skin, and complex backgrounds, avoiding the need for cumbersome individual lesion imaging,” the MIT researchers noted in their Science Translational Medicine paper.
In addition, the algorithm agreed with the consensus of experienced dermatologists 88% of the time and concurred with the opinions of individual dermatologists 86% of the time, Medgadget reported.
Modern Imaging Technologies Will Advance Diagnosis of Disease
According to the American Cancer Society, about 106,110 new cases of melanoma will be diagnosed in the United States in 2021. Approximately 7,180 people are expected to die of the disease this year. Melanoma is less common than other types of skin cancer but more dangerous as it’s more likely to spread to other parts of the body if not detected and treated early.
More research is needed to substantiate the effectiveness and accuracy of this new tool before it could be used in clinical settings. However, the early research looks promising and smartphone camera technology is constantly improving. Higher resolutions would further advance development of this type of diagnostic tool.
In addition, MIT’s algorithm enables in situ examination and possible diagnosis of cancer. Therefore, a smartphone so equipped could enable a dermatologist to diagnose and excise cancerous tissue in a single visit, without the need for biopsies to be sent to a dermatopathologist.
Currently, dermatologists refer a lot of skin biopsies to dermapathologists and anatomic pathology laboratories. An accurate diagnostic tool that uses modern smartphones to characterize suspicious skin lesions could become quite popular with dermatologists and affect the flow of referrals to medical laboratories.
WASE-COVID Study also found that use of artificial intelligence technology minimized variability among echocardiogram scan results
Many physicians—including anatomic pathologists—are watching the development of artificial intelligence (AI)-powered diagnostic tools that are intended to analyze images and analyze the data with accuracy comparable to trained doctors. Now comes news of a recent study that demonstrated the ability of an AI tool to analyze echocardiograph images and deliver analyses equal to or better than trained physicians.
Conducted by researchers from the World Alliance Societies of Echocardiography and presented at the latest annual sessions of the American College of Cardiology (ACC), the WASE-COVID Study involved assessing the ability of the AI platform to analyze digital echocardiograph images with the goal of predicting mortality in patients with severe cases of COVID-19.
To complete their research, the WASE-COVID Study scientists examined 870 patients with acute COVID-19 infection from 13 medical centers in nine countries throughout Asia, Europe, United States, and Latin America.
Human versus Artificial Intelligence Analysis
Echocardiograms were analyzed with automated, machine learning-derived algorithms to calculate various data points and identify echocardiographic parameters that would be prognostic of clinical outcomes in hospitalized patients. The results were then compared to human analysis.
All patients in the study had previously tested positive for COVID-19 infection using a polymerase chain reaction (PCR) or rapid antigen test (RAT) and received a clinically-indicated echocardiogram upon admission. For those patients ultimately discharged from the hospital, a follow-up echocardiogram was performed after three months.
“What we learned was that the manual tracings were not able to predict mortality,” Federico Asch, MD, FACC, FASE, Director of the Echocardiography Core Lab at MedStar Health Research Institute in Washington, DC, told US Cardiology Review in a video interview describing the WASE-COVID Study findings.
Asch is also Associate Professor of Medicine (Cardiology) at Georgetown University. He added, “But on the same echoes, if the analysis was done by machine—Ultromics EchoGo Core, a software that is commercially available—when we used the measurements obtained through this platform, we were able to predict in-hospital and out-of-hospital mortality both with ejection fraction and left ventricular longitudinal strain.”
“When compared to the manual reads, the AI algorithms had a much higher predictive value for mortality,” Federico Asch, MD (above), told US Cardiology Review. “Indeed, they were predictive where the manual ones were not.” These findings may have implications in the development and adoption of artificial intelligence driven clinical laboratory diagnostics and for predicting risk of COVID-19 deaths in hospitalized heart patients. Click here to review the entire video interview. (Photo copyright: US Cardiology Review.)
Nearly half of the 870 hospitalized patients were admitted to intensive care units, 27% were placed on ventilators, 188 patients died in the hospital, and 50 additional patients died within three to six months after being released from the hospital.
10 of 13 medical centers performed limited cardiac exams as their primary COVID in-patient practice and three out of the 13 centers performed comprehensive exams.
In-hospital mortality rates ranged from 11% in Asia, 19% in Europe, 26% in the US, to 27% in Latin America.
Left ventricular longitudinal strain (LVLS), right ventricle free wall strain (RVFWS), as well as a patient’s age, lactic dehydrogenase levels and history of lung disease, were independently associated with mortality. Left ventricle ejection fraction (LVEF) was not.
Fully automated quantification of LVEF and LVLS using AI minimized variability.
AI-based left ventricular analyses, but not manual, were significant predictors of in-hospital and follow-up mortality.
The WASE-COVID Study also revealed the varying international use of cardiac ultrasound (echocardiography) on COVID-19 patients.
“By using machines, we reduce variability. By reducing variability, we have a better capacity to compare our results with other outcomes, whether that outcome in this case is mortality or it could be changes over time,” Asch stated in the US Cardiology Review video. “What this really means is that we may be able to show associations and comparisons by using AI that we cannot do with manual [readings] because manual has more variation and is less reliable.”
He said the next steps will be to see if the findings hold true when AI is used in other populations of cardiac patients.
COVID-19 Pandemic Increased Need for Swift Analyses
An earlier WASE Study in 2016 set out to answer whether normal left ventricular heart chamber quantifications vary across countries, geographical regions, and cultures. However, the data produced by that study took years to review. Asch said the COVID-19 pandemic created a need for such analysis to be done more quickly.
“When the pandemic began, we knew that the clinical urgency to learn as much as possible about the cardiovascular connection to COVID-19 was incredibly high, and that we had to find a better way of securely and consistently reviewing all of this information in a timely manner,” he said in the Ultromics new release.
Coronary artery disease (CAD) is the most common form of heart disease and affects more than 16.5 million people over the age of 20. By 2035, the economic burden of CAD will reach an estimated $749 billion in the US alone, according to the Ultromics website.
“COVID-19 has placed an even greater pressure on cardiac care and looks likely to have lasting implications in terms of its impact on the heart,” said Ross Upton, PhD, Founder and CEO of Oxford, UK-based Ultromics, in a news release announcing the US Food and Drug Administration’s 510(k) clearance for the EchoGo Pro, which supports clinicians’ diagnosing of CAD. “The healthcare industry needs to quickly pivot towards AI-powered automation to reduce the time to diagnosis and improve patient care.”
Use of AI to analyze digital pathology images is expected to be a fast-growing element in the anatomic pathology profession, particularly in the diagnosis of cancer. As Dark Daily outlined in this free white Paper, “Anatomic Pathology at the Tipping Point? The Economic Case for Adopting Digital Technology and AI Applications Now,” anatomic pathology laboratories can expect adoption of AI and digital technology to gain in popularity among pathologists in coming years.
As the worldwide demand for histopathology services increases faster than the increase in the number of anatomic pathologist and histopathologists, a DP platform that suggests courses of treatments may be a boon to cancer diagnostics
Europe may become Ground Zero for the widespread adoption of whole-slide imaging (WSI), digital pathology (DP) workflow, and the use of image-analysis algorithms to make primary diagnoses of cancer. Several forward-looking histopathology laboratories in different European countries are moving swiftly to adopt these innovative technologies.
Clinical laboratories and anatomic pathology groups worldwide have watched digital pathology tools evolve into powerful diagnostic aids. And though not yet employed for primary diagnoses, thanks to artificial intelligence (AI) and machine learning many DP platforms are moving closer to daily clinical use and new collaborations with pathologists who utilize the technology to confirm cancer and other chronic diseases.
Now, Swiss company Unilabs, one of the largest laboratory, imaging, and pathology diagnostic developers in Europe, and Israel-based Ibex Medical Analytics, developer of AI-based digital pathology and cancer diagnostics, have teamed together to deploy “Ibex’s multi-tissue AI-powered Galen platform” across 16 European nations, according to a Unilabs press release.
Though not cleared by the federal Food and Drug Administration (FDA) for clinical use in the US, the FDA recently granted Breakthrough Device Designation to Ibex’s Galen platform. This designation is part of the FDA’s Breakthrough Device Program which was created to help expedite the development, assessment, and review of certain medical devices and products that promise to provide for more effective treatment or diagnosis of life-threatening or irreversibly debilitating diseases or conditions.
Benefits of AI-Digital Pathology to Pathologists, Clinical Labs, and Patients
According to Ibex’s website, the Galen DP platform uses AI algorithms to analyze images from breast and prostate tissue biopsies and provide insights that help pathologists and physicians determine the best treatment options for cancer patients.
This will, Ibex says, give pathologists “More time to dedicate to complex cases and research,” and will make reading biopsies “Less tedious, tiring, and stressful.”
Patients, according to Ibex, benefit from “Increased diagnostic accuracy” and “More objective results.”
And pathology laboratories benefit from “Increased efficiency, decreased turnaround time, and improved quality of service,” Ibex claims.
According to the press release, AI-generated insights can include “case prioritization worklists, cancer heatmaps, tumor grading and measurements, streamlined reporting tools and more.”
This more collaborative approach between pathologists and AI is a somewhat different use of digital pathology, which primarily has been used to confirm pathologists’ diagnoses, rather than helping to identify cancer and suggest courses of treatment to pathologists.
“This cutting-edge AI technology will help our teams quickly prioritize urgent cases, speed up diagnosis, and improve quality by adding an extra set of digital eyes,” said Christian Rebhan, MD, PhD (above), Chief Medical and Operations Officer at Unilabs, in the press release. “When it comes to cancer, the earlier you catch it, the better the prognosis—so getting us critical results faster will help save lives.” (Photo copyright: Unilabs.)
AI-based First and Second Reads
The utilization of the Galen platform will first be rolled out nationally in Sweden and then deployed in sixteen other countries. The AI-based DP platform is CE marked in the European Union for breast and prostate cancer detection in multiple workflows.
“The partnership with Ibex underlines Unilabs’ pioneering role in Digital Pathology and represents yet another step in our ambition to become the most digitally-enabled provider of diagnostic services in Europe,” Rebhan stated.
The Ibex website explains that the Galen platform is divided into two parts—First Read and Second Read:
The First Read “is an AI-based diagnostics application that aims to help pathologists significantly reduce turnaround time and improve diagnostic accuracy. The application uses a highly accurate AI algorithm to analyze slides prior to the pathologist and provides decision support tools that enable focusing on cancerous slides and areas of interest, streamline reporting, improve lab efficiency, and increase diagnostic confidence.”
The Second Read “is an AI-based diagnostics and quality control application that helps pathologists enhance diagnostic accuracy with no impact on routine workflow. The application analyzes slides in parallel with the pathologist and alerts in case of discrepancies with high clinical significance (e.g., a missed cancer), thereby providing a safety net that reduces error rates and enables a more efficient workflow.”
“Ibex is transforming cancer diagnosis with innovative AI solutions across the diagnostic pathway,” said Joseph Mossel, Chief Executive Officer and co-founder of Ibex, in the press release. “We are excited to partner with Unilabs to deploy our AI solutions and empower their pathologists with faster turnaround times and quality diagnosis. This cooperation follows a thorough evaluation of our technology at Unilabs and demonstrates the robustness and utility of our platform for everyday clinical practice.”
Use of AI in Pathology Increases as Number of Actual Pathologists Declines
Developers like Unilabs and Ibex believe that DP platforms driven by AI image analysis algorithms can help pathologists be more productive and can shorten the time it takes for physicians to make diagnoses and issue reports to patients.
This may be coming at a critical time. As nations around the globe face increasing shortages of pathologists and histopathologists, the use of AI in digital pathology could become more critical for disease diagnosis and treatment.
A 2019 Medscape survey stated that “One-third of active pathologists are burned out,” and that many pathologists are on the road to retirement.
And in the same year, Fierce Healthcare noted that in a 2013 study, “researchers found that more than 40% of pathologists were 55 or older. They predicted that retirements would reach their apex in 2021. Consequently, by the end of next decade, the United States will be short more than 5,700 pathologists.”
Dark Daily previously reported on the growing global shortage of pathologists going back to 2011.
Even China is struggling to keep up with demand for anatomic pathologists. In 2017, Dark Daily wrote, “China is currently facing a severe shortage of anatomic pathologists, which blocks patients’ access to quality care. The relatively small number of pathologists are often overworked, even as more patients want access to specialty care for illnesses. Some hospitals in China do not even have pathologists on staff. Thus, they rely on understaffed anatomic pathology departments at other facilities, or they use imaging only for diagnoses.”
Thus, it may be time for an AI-driven digital platform to arrive that can speed up and increase the accuracy of the cancer diagnostics process for pathologists, clinical laboratories, and patients alike.
There are multiple companies rapidly developing AI, machine learning, and image analysis products for diagnosing diseases. Pathologists should expect progress in this field to be ongoing and new capabilities regularly introduced into the market.
