Digital pathology and AI—drivers, budgets, and jobs

You engage with many people in the field, from veterans to people who are getting started, and many look to you and Gestalt as an entry point and as someone to talk to as they contemplate this. What is uppermost in their minds, and how has that changed in the past year?

Lisa-Jean Clifford (Gestalt): Depending on the organization and its size, goals, and objectives, the questions vary widely. The smaller and midsize organizations have fear of missing out. But they’re also concerned because they have the tightest financial constraints and are trying to understand the differences in the hardware, scanners, and the technologies and platforms. Digital pathology providers vary greatly in what they provide—the components, whether they are an all-encompassing platform, whether they are a glorified viewer that makes it easier to adopt digital in small bites.

The questions and approaches have changed in the past 12 to 18 months. It is no longer, Should we do this? Is this something we want to spend time on? Should we find the budget for this? It has shifted to, How do we do this? When do we do it? What will our approach be? How do we justify the budget?

Katie Gillette, is this in sync with what you’ve heard in your research? How do people approach their budgeting in the tight financial environment we’re living in?
Katie Gillette (DeciBio): The budgeting is still tough. A lot of laboratories are working on figuring out what financial equation makes sense for digital pathology, depending on the type of lab and its priorities. A large reference lab can take advantage of the economies of scale whereas in a small community hospital, the biggest value is in remote consultations or as a solution to the pathologist shortage. Within academic centers, the financial aspect isn’t necessarily the first consideration; it’s more about the research opportunities that adoption of these tools enables.

It’s not a simple equation because direct reimbursement is not in place. It’s not: Do test A, get reimbursed for test A, we make money. Labs have to ask, Am I retaining pathologists whom I would otherwise be losing, so am I saving money there? Am I saving time per slide, even if it’s small, and does it make sense in the long run? Am I able to insource cases that I would otherwise not be able to do because I can look at things from a wider geographic area, and that could be revenue-generating for me?

Dr. de Baca (Sysmex): The CAP and American Medical Association have successfully added more than 30 category three CPT codes for digital pathology and are now working on AI codes. I hope CAP TODAY readers speak to people in their information systems service and facilitate implementation of category three CPT codes in their systems. This is the way that CMS gathers the data that informs the need for reimbursement for a certain new service. CMS doesn’t run to us with money; category three codes offer the only official way for us to let CMS know, Here’s what we are doing, and how much, and this is valuable work and we need to be reimbursed for it.

Katie Gillette (DeciBio): The Digital Pathology Association hosted a webinar earlier this year on this topic, looking at how category three codes are being adopted. There’s still work to be done on the billing side to make sure the category three codes that exist now are being used so their tracking purpose can be realized. Otherwise they are shouting into the abyss.

Years ago a friend of mine put together a huge proposition for total lab automation in the clinical laboratory and ran it up the flagpole to the financial people in the hospital. They shot it down, saying, “This is much too expensive, being that nobody is reimbursing you just because you can get the work out faster or more efficiently.” Yet less than a year later they went back to him and said, “Where is that plan to give us total automation in the clinical laboratory? We’re overwhelmed with volume now and can’t exist unless we put it in, and the sooner the better.” This reminds me of the digital pathology conundrum now. Eric Glassy, does it remind you of that?

Dr. Glassy (Affiliated Pathologists): It does. People who rejected it are now coming back and saying, “How do you help us implement?” The CAP can help, the DPA webinars are great, and attending the DPA meeting is a wonderful way to get indoctrinated and educated about the values of digital pathology, once you get the green light from administration. But there has to be caution about adoption. People need to better understand validation. They understand chemistry validation needs but may not understand anatomic pathology validation needs, and our professional organizations can provide educational opportunities.

Lisa-Jean Clifford, UPMC just brought in more computational pathology expertise, as has IU School of Medicine. Do you expect to see an ever-increasing influx of those types of experts as part of the pathology department?
Lisa-Jean Clifford (Gestalt): Yes, and having the pathologist involved in the process is fundamental to ensure patient safety and the correct application and adoption of the technologies. Part of my role at the Association for Pathology Informatics is making sure that industry and medicine mix as a whole. Having people who can bridge that gap and provide meaningful, accurate information while technologies are developed and tested is fundamental.

Mike Quick, let me ask you about the AI applications that will be and have been approved by the FDA. I often think of the liability element. I can imagine a lawyer in a medical liability case saying to someone on the stand, “Doctor, were you not aware there’s an FDA-approved AI algorithm that can back you up and support your diagnosis? And did you use that or have it available?” It would seem to be a slam dunk for adoption. Give us your thoughts about that line of argument.
Michael Quick (president-elect, DPA): We’ve lived through this for the past 20 years with the adoption of automated technology in cervical cancer, one of the most litigious areas in medicine. There’s still an understanding that it’s not the AI or the pathologist but a combination of the two that gives us the best diagnosis. But it’s still early days regarding regulatory-cleared AI algorithms. The FDA has cleared nearly 600 AI devices in medicine but only one algorithm in digital pathology to date. I expect we’ll see that accelerate but there is a gap now that people are aware of, and it’s leaving laboratories in a difficult position of wanting to implement these new technologies, get experience with them, and be involved in developing and validating them, but not knowing what the regulatory environment is going to look like, whether it will be superseded by an FDA-cleared application in the future. Having the FDA, industry, laboratories, and practitioners working together will move this forward.

Mike Rivers, what is your impression about the speed of approvals through the FDA? And is the FDA equipped to properly examine these submissions?
Michael Rivers (Roche): A lot of work has been done behind the scenes and in preparation for what I think will be an acceleration in approvals in the coming months and years. The FDA, with its guidance documents and engagement with manufacturers, is signaling an interest in this area, a desire to move forward but in a careful, safe way. And that’s appropriate; there are a lot of unknowns about this technology. We talk a lot about explainable AI. It’s important for the agency and for pathologists to be able to understand how AI is determining the decisions it’s making, and whether the pathologist can agree with that and sign their name on it.

Dr. Glassy (Affiliated Pathologists): We had labs in California that were Pap mills and patients were disadvantaged. And then Hologic came out with ThinPrep but it was more expensive and there was huge resistance from obstetricians. What helped is Hologic took the approach of getting patients involved. They advertised in Redbook and other magazines, and that spurred the discussion about the value of ThinPrep. That conversation with the obstetricians helped push, to some degree, the value of moving to this new technology and its commercialization.

There’s an attempt now to improve pathology with AI. I’ve had a urologist say to me, “Did you use AI in your prostate core biopsy diagnosis? Because I have a patient who read in one of the journals that AI could really help.” Here we have a patient talking to a urologist, who then says to me, “Have you put that on your report? Have you done something to help benefit the patient? Because you’ve got another set of artificial eyes reviewing the case.” So I can see this taking off from the commercial side—patients will be using ChatGPT to review their lab data and their pathology report and get interpretations from it. Pathologists need to be prepared for that new future, which will be here in months. We’re going to get inundated with this. I’d like to get Mike’s take on if I’m close to the history and if he sees the parallels with what’s potentially happening now.

Michael Quick (president-elect, DPA): Three things need to be in place before you involve the patient: The physician has to have access to that information and be educated about it, the technology has to be available in the laboratory, and insurance companies or payers have to say they will reimburse. We went direct to consumer, or direct to patient, wherever those three were in place. That foundation, which is what you saw in California, made pushing things forward successful.

Katie Gillette, in thinking about the field, what role can you ascribe to patients, imaginatively at least? After all, patients can make a difference, as we’ve seen over time.
Katie Gillette (DeciBio): The materials and conversations to date have been geared toward the pathologists and experts in this space. There’s a need for the types of materials, wording, framing, and the story around it that resonate not just with pathologists but with patients, where they can see the value of this. The same is true for oncologists and specialists who, when these decisions are made at an institution level and not at an individual pathologist level, can be an important piece of it.

Cancer biomarkers are crucial steps in patient care for targeted therapy. It seems that applications in that field would become an essential part of the further development of digital and computational pathology and AI algorithms, because cancer is central to everyone’s concern in this field. Lisa-Jean Clifford, do you agree with that?
Lisa-Jean Clifford (Gestalt): Absolutely. Digital pathology companies are looking at their road maps, products, and product combinations that they are bringing to market and making decisions on how they intersect. All of this available data is valuable for a variety of reasons, such as being able to identify patients who could be a good fit for specific clinical trials. This could allow pathologists to provide guidance, as part of their report, to oncologists that a patient could be a fit for a list of current trials that apply to them. Or that based on specific biomarkers, their patient is potentially a good fit for these therapeutic drugs currently on or new to the market.

Providing that information through AI, machine learning, and a variety of available algorithms that are not diagnostic algorithms can help provide more information to the pathologist, embedding them as a core participant in the central care team for that patient.

Doc de Baca, can you comment on that?
Dr. de Baca (Sysmex): These new technologies will likely help us harness huge quantities of information more manageably. Information is facts and details; information can be transformed into knowledge. But knowledge is still only knowing. Knowledge does not do anything; it does not change behavior or treat or care for patients.

Medicine is described as a science and an art. I see an acute need to teach physicians, ergo pathologists, not only the science but also the art of medicine. What makes us unique is the wisdom we derive from the knowledge—our results are given with perspective and in context; they then lead to correct actions. I do not believe that in my lifetime, in the arena of wisdom, human physicians will be conquered by machines. The value of augmented intelligence is proportional to the wisdom only we can provide.

Physicians have been trivialized in the past 20 years. We have been reduced to “providers,” as if we were vending machines for whatever drug the TV said you need. We are not that. We are very highly trained caretakers. I take care of you. I care about you, I care about your disease. I know your child likes basketball, that your mother has dementia. I care that if you can’t get to my office because your spouse is ill or your car is broken, your challenge of achieving better health is a higher hurdle than if not.

Machines are not going to care. More information is good, more knowledge is good. Yet it is the soft skills of wisdom, perspective, context, and judgment that make us irreplaceable. The value of our combined scientific knowledge and our human wisdom is something we need to discuss urgently.