The potential of artificial intelligence in health care comes with both groundbreaking
advancements and critical challenges, as 黑料传送门 alumni navigate the evolving landscape.
鈥淎I holds immense promise in transforming health care, but its potential will only
be fully realized if its challenges are effectively managed.鈥
But don鈥檛 take our word for it. That鈥檚 how Chat-GPT concluded its response to the
prompt: 鈥淧rovide an overview of the challenges and promise of AI in medicine today.鈥
The forms of AI that are on the threshold of entering every aspect of our lives go
way beyond the generative version we know from Chat-GPT and its kin, or the algorithms
that process data from our wearable devices. Healthcare providers use AI-powered medical
note-taking apps. AI chatbots and virtual assistants help with patient triage. Hospitals
and health systems use AI to streamline administrative processes and improve scheduling.
Robotic surgical systems rely on AI enhancements. AI may (or already does) reduce
the cost and time of drug development, improve medical imaging and diagnostics, and
spur advancements in genomics and precision medicine.
Turning to the challenges AI poses, it鈥檚 worth noting that new inventions and new
technologies have often aroused as many fears as hopes. In a nutshell, though, the
two strongest threads of concern are these: Will AI allow computers to make decisions
for us, or even require them to do so? And will AI disrupt the labor market, taking
jobs previously held by human workers?
In the following pages, three in three very different settings offer a birds-eye view of how they work with AI
and think about the challenges it poses.
Barbara A. Crothers, DO 鈥86, is chief scientist for AIxMed, a cytopathology software
company. She is also associate professor of pathology, James H. Quillen Veterans Affairs
Medical Center, Tennessee. Dr. Crothers has served as president and executive board
member of the American Society of Cytopathology and as chair of the Cytopathology
Committee at the College of American Pathologists.
John Potts, DO, RES 鈥00, FAAFP, is vice president and chief medical information officer
at Main Line Health. Dr. Potts completed his family medicine internship and residency
at 黑料传送门 after earning his medical degree from the Chicago College of Osteopathic Medicine
of Midwestern University.
Prerak Adhuria, PharmD 鈥17, is a pharmacy solutions analyst at CaryHealth, which delivers
personalized digital healthcare experiences at scale. His cross-functional role provides
him with firsthand insights into the development/enhancement of products, interactions
with team leads and their feedback to improve technology, and differences in patient
outcomes via a digital pharmacy experience, health plans medical savings, and improved
efficiencies for clinicians and providers. He is a licensed pharmacist in 17 states.
Prerak Adhuria, PharmD 鈥17
Barbara A. Crothers, DO 鈥86
Barbara A. Crothers, DO 鈥86, is chief scientist for AlxMed, a cytopathology software
company based in Santa Clara, California, and Taipei, Taiwan. AlxMed鈥檚 niche is non-gynecologic
cytology鈥攁ll cytology other than Pap tests.
Cytopathology has thus far been an unmet need in digital pathology, Dr. Crothers explains,
because unlike surgical pathology, which was 鈥渁lready dealing with two-dimensional
images on a glass slide, tissue samples that are a couple of microns thick, and two
or three layers of cells,鈥 cytology requires much more scanning. 鈥淲e compress what
we call Z-layers into one image when we鈥檙e using a digital scanner, so that the pathologist
doesn鈥檛 have to use a microscope to focus up and down鈥攖hey鈥檒l see all of the cell鈥檚
features in one flat image.鈥
AlxMed uses artificial intelligence to deal with the quantitative diagnostic criteria
that pathologists have a hard time evaluating 鈥渂ecause we鈥檙e subjective,鈥 she explains.
鈥淔or the most part, when we鈥檙e doing microscopic work, we鈥檙e visually evaluating the
size of the nucleus and comparing it to the size of the cytoplasm. But we know that
certain cancer cells, for example, have a high nuclear-to-cytoplasmic ratio. And there
are some standardized nomenclature systems in cytology for certain body sites, like
urine cytology, that specify what that nuclear-to-cytoplasmic ratio should be in order
to define that cell as a cancer cell or a potential cancer cell. The computer can
do this very well when it鈥檚 trained with artificial intelligence.鈥
Software companies take a whole slide image and overlay it with AI training models
that look at particular components on that slide, 鈥渋n order to do tasks that pathologists
do every day,鈥 Dr. Crothers explains. 鈥淔or example, we have to count the number of
mitotic cells in a tumor by examining high-power fields. We know from studies that
we鈥檙e not very good at it鈥攚e don鈥檛 get very good concordance, but it鈥檚 important.
This can be done by AI very well and very quickly.
鈥淢ore complicated is training the software that this particular cell is probably malignant,
which we do by looking at the cell characteristics. In urine, sometimes you鈥檙e looking
for a needle in a haystack. You may not have many abnormal urothelial carcinoma cells
in a urine specimen, but AI can pick them up a lot quicker than any of us can looking
at the slide, and it can also categorize them quickly. The software we鈥檝e developed
presents those cells to you on a screen as thumbnail images, with the statistics for
each cell, so you know exactly what that cell鈥檚 characteristics are.鈥
The same model can be transformed slightly to fit other specimen types, such as thyroid
specimens. Down the line, says Dr. Crothers, 鈥渨e鈥檙e going to be looking at lung and
probably other systems, to create an entire suite of AI software modules.鈥
Much of AlxMed鈥檚 current research is in preparation for an FDA submission, since medical
devices can be used in clinical practice only with FDA approval. 鈥淭hat hurdle is very
high,鈥 says Dr. Crothers, 鈥渁nd there鈥檚 only been one approved AI in cytology to date,
just this past year. The FDA is very helpful in giving you guidance on what kind of
data they鈥檙e looking for. An FDA validation study is usually a very large study that
shows proof of concept and the device鈥檚 reliability in a medical setting.
鈥淭he device doesn鈥檛 make the diagnosis, the pathologist does. We have extensive quality
assurance programs in pathology, and we鈥檙e checking each other all the time. But we
could rely on AI to help check us as well, instead of needing another set of human
eyes鈥攎aybe have AI do a lot of that back-end quality assurance work that we do every
day.鈥
In that case, will pathologists lose their jobs?
鈥淚 saw this same concern arise with the advent of automated Pap tests,鈥 says Dr. Crothers.
鈥淪creening guidelines changed; test volumes dropped, and automation reduced the time
required for screening Pap tests. We thought cytotechnologists, who screened most
of the Pap tests, would lose their jobs. But meanwhile, there was an unmet need in
the lab for small biopsy, fine-needle aspiration specimen quality evaluation because
of the implementation of robotics and new methods of collecting specimens and cells.
We needed cytotechnologists to go to these procedures to evaluate the adequacy of
these specimens. Very few cytotechnologists lost their jobs.鈥
What鈥檚 more, she points out, 鈥渢here continues to be a decline in the number of individuals
who are doing cytotechnology. Schools are not putting out enough technologists to
meet the need, and the shortage is worldwide. The same is true for pathology, and
honestly, the same is true for medicine. We鈥檙e at a crisis point; we don鈥檛 have enough
individuals to do the work. AI could free us up from some of the time-consuming manual
tasks.鈥
Besides her involvement in AlxMed鈥檚 research studies, Dr. Crothers is responsible
for developing the training materials and for training investigators and future customers
on the use of the software. 鈥淚f I鈥檓 involved in the process,鈥 she says, 鈥淚 can help
to fine-tune how it will be helpful to pathologists in their day-to-day practices.鈥
John Potts, DO, RES 鈥00, FAAFP
As chief medical information officer at Main Line Health, John Potts, DO, RES 鈥00,
FAAFP, is responsible for facilitating the electronic medical record (EMR) strategy,
implementation, optimization and data analytics for users throughout the Main Line
Health Network. This means ensuring that people have the technology they need in their
roles and the knowledge to use it鈥攁nd AI increasingly is central to that technology.
About the concern that AI is going to take people鈥檚 jobs, Dr. Potts says, 鈥淎I offloads
tasks, whether from a physician, nurse, medical assistant or medical biller. I suppose
if you offload enough tasks for someone鈥檚 specific role, you might be able to repurpose
them to a different role. But I don鈥檛 see artificial intelligence replacing people.
In fact, as baby boomers get older, and sicker, and Gen X is starting to head into
the retirement years, we don鈥檛 have enough of Gen Z coming out of schools to replace
folks that are retiring. We can鈥檛 hire our way out of this.鈥
An example of AI providing diagnostic assistance is Main Line Health鈥檚 use of a platform
that reviews CAT scans to not only look for large vessel strokes, but to 鈥渋mmediately
alert our stroke team by phone to go see that patient, and it tells them where that
patient is,鈥 says Dr. Potts. 鈥淚t also alerts our radiologists and prioritizes that
film to be read immediately. If the patient truly is having a stroke, then we can
begin treatment right away. And that鈥檚 allowed us to decrease our times to treatment
for the appropriate patients.鈥 Besides neurology, the platform is being used in cardiology
and pulmonology.
Clinician assistance can take the form of AI generating replies to questions on patient
portals. It turns out that 鈥淎I is far more verbose than our clinicians,鈥 says Dr.
Potts. 鈥淪tudies have shown that patients often like the AI replies鈥斺楲ook how much
time the doctor spent writing back to me鈥欌攂ut a long reply might encourage a patient
to keep asking more questions over the portal as opposed to coming in for a visit.
That鈥檚 always been a feature of having patient portals, of course, but we had to work
with our AI and prompt training to dial down the length of the replies.鈥
What鈥檚 more, the clinicians might say that a reply doesn鈥檛 capture their 鈥渧oice.鈥
Dr. Potts recalls, 鈥淲e once had a patient call the office to ask, 鈥業s the doctor feeling
okay? Their reply to me doesn鈥檛 sound like them.鈥 The AI we use is a generic model鈥攁
HIPPA-compliant version of GPT-4. Training AI so it can mimic clinicians is extremely
expensive and very challenging. But companies are pursuing this, and our development
team is working on it as well, so that the AI would train off a model that would be
tailored to individual clinicians.鈥
Whether you鈥檙e a mid-size health system like Main Line Health or a larger network,
says Dr. Potts, the key question is 鈥渉ow do you scale whatever you鈥檙e bringing to
the clinical side. For example, we鈥檙e looking to create an innovation room鈥攁 patient
room that brings in all the new technologies to see how they work with the other equipment,
what the workflows are like. We want to bring in doctors and nurses to test innovations
and determine if they鈥檙e ready for rollout on a broad scale.鈥
Dr. Potts predicts that the hospital is going to radically change in the next three
years for patients and for staff and become a 鈥渟mart hospital room.鈥 鈥淭echnologies
using cameras can take a patient鈥檚 vital signs, contactless鈥攑ulse rate, heart rate,
temperature, and we鈥檙e told that blood pressure is coming in the near future. A nurse
or care tech won鈥檛 have to interrupt the patient, whether they鈥檙e with family during
the day or sleeping at night. No one will have to manually enter those vital sign
numbers into the EMR.
鈥淎I in that same camera can detect whether the patient is a fall risk, and if so,
alert somebody on the floor. It can show us if a patient is at risk for developing
pressure ulcers, or is smoking in the room and at risk of causing a fire, or if somebody
has brought a weapon into the room. There鈥檚 lots of use cases for AI in the room,
not only for the patient, but for the hospital staff as well.鈥
Dr. Potts points out that every specialty can benefit from AI. 鈥淭hink dermatology,
for example. AI models can do as well as a person looking at a lesion to determine
if it鈥檚 malignant or not. Precision medicine and human genomics鈥攖hey鈥檙e sequencing
patients鈥 DNA and then loading it back into the EMR. AI can look at a humongous amount
of data and help predict for us what cancer treatments are the best options for a
particular patient based on their genomics. That is really exciting, and that day
is coming.鈥
Prerak Adhuria, PharmD 鈥17
As a pharmacy solutions analyst at CaryHealth, Prerak Adhuria, PharmD 鈥17, explains,
鈥淚鈥檝e been working very closely with marketing sales as well as development enhancements
of products within our company that use AI鈥攊mproving our tech and seeing the patient
outcomes in the digital pharmacy experience鈥攁s well on the patient side and on the
health plan side, so payers and health systems can make better decisions. My role
is essentially to be a bridge that supports the C-suite, connecting my pharmacy background
with the technology background I鈥檓 learning and then tying it all together.鈥
Dr. Adhuria is currently the project manager for all CaryHealth conferences鈥攆or the
pharmaceuticals industry, for payers, for providers鈥攔elated to Clair: Clinical AI
Reference, a tool focused on fast, precise information retrieval. Besides attending
some of these conferences, he vets them and determines how the team will reach out
to attendees. Clair, which is designed for quick lookups of medical guidelines, evidence-based
practices and other clinical data without extensive patient-specific integration,
gives users answers within seconds, says Dr. Adhuria, rather than the minutes needed
by other resources.
Clair鈥檚 answers are geared toward whoever鈥檚 asking it a question. The app allows retail
pharmacists, physicians and other professionals who see patients throughout the day to answer patient questions
right away, face-to-face, rather than making an extra trip to look something up. 鈥淎
professional can ask Clair about a medication鈥檚 effects on blood pressure or drug
interactions,鈥 explains Dr. Adhuria. 鈥淏ut a lay person could ask Clair if it鈥檚 OK
to be taking three medications together. Meanwhile, the admin team could ask about
ICD-10 codes for billing purposes.鈥
Some people think of AI as 鈥渁n engine out there that does its own thing,鈥 Dr. Adhuria
says, 鈥渂ut with proper guardrails and monitoring, it makes people鈥檚 job鈥檚 easier,
allowing them to focus on the patient鈥檚 needs.鈥 Dr. Adhuria especially loves to connect
with students, and at a recent conference of the Academy of Managed Care Pharmacy,
鈥淚 got to see students鈥 reactions,鈥 he says, 鈥渁nd it was blowing their minds.鈥
OneDash, another CaryHealth offering, is a population health tool: an AI-driven clinical
automation platform designed to identify and automate the closure of care gaps. A
plan can, for example, immediately see if any patients with diabetes are not on a
statin, or pull all patients in a particular age range that have been taking a particular
medication over the past three months. Previously, that might take the health plan
a couple of weeks. Now, says Dr. Adhuria, 鈥渢he turnaround is 10 or 20 minutes. And
that allows decisions to happen faster.鈥 Automations save time, too, not only by sending
faxes to doctors鈥 offices but by making AI-assisted calls to patients 鈥渢hat sound
like a normal conversation,鈥 says Dr. Adhuria. He notes that a health plan in the
DC/Maryland/Virginia area has recently been able to see financial savings, improved
patient outcomes and increased patient adherence rates.
鈥淎 doctor can do their job,鈥 he continues, 鈥渨ithout having to handle prior authorizations
submitted by pharmacies, without having to reach out to the insurance company. It
was a mess, and now all this is automated. Doctors love this because now they can
spend more time taking care of patients; pharmacists love this because now they can
talk to patients who come to the pharmacy to get their prescription.鈥
Before he graduated from 黑料传送门, Dr. Adhuria worked for almost a decade as a technician
and intern with CVS. After he graduated, he did independent pharmacy work in a small
town, with a very small patient base鈥攈ome care, assisted living services, packing
medications in a bubble pack, delivering those medications at 8:00 p.m. if I had to.
I knew people had no other means of getting what they needed, and I thought, 鈥楾here
has to be a better way.鈥欌塏ext, working with a mail order retail pharmacy, he learned
鈥渉ow to improve on efficiencies and get 100 percent of our medications out every day
without sacrificing quality. So I went from delivering medication myself to someone鈥檚
house to being able to ensure they could have it mailed to them before their medications
run out.鈥
Now, with CaryHealth, Dr. Adhuria works with a digital pharmacy that is licensed in
50 states and 鈥渙ffers phenomenal patient experience. Whether you need to refill a
medication, or discontinue a medication, or want to check on something, you can just
do it from your mobile app.鈥