“The Promise of Artificial Intelligence in the East Asian...

“The Promise of Artificial Intelligence in the East Asian Medicine Clinic

or How I Learned to Stop Stressing and Love the Bot”

By Sarah E. Rivkin, DAHM, LAc, Dipl. OM

At a recent check-up for my dog, our veterinarian began by asking if she

could use AI (artificial intelligence) to record the appointment. That way, she

explained, she could give my anxious pup her full attention, without having to

switch back and forth between him and the computer to take notes. After the

appointment she or her assistant would then review and correct his chart to make

sure it was accurate. Despite my Luddite tendencies—and knee-jerk distrust of

AI—I said yes. My dog had the calmest and easiest appointment he’d had in years.

As I later learned, AI scribe services are an exploding sector of medical technology,

for both animals and humans. After this experience I could see why.

Perhaps it was time to learn more about the upside to AI. I started with two

recent bestsellers on the subject:

Nexus

, by Yuval Noah Harari and

Co-Intelligence

by Ethan Mollick. Harari’s premise is that “the rise of AI is arguably the biggest

information revolution in history” (p. xxx) however “to exercise our agency, we first

need to understand what the new technologies are and what they can do.” (p. 228)

Mollick predicts that AI will make us all into editors, like the veterinarian reviewing

her chart. He goes on to assert that “an AI future requires that we lean into building

our own expertise as human experts.” (pg. 191) Delving more into how AI is already

being used in East Asian medicine, I then learned that at the most recent Society for

Acupuncture Research (SAR) conference, there were presentations on using AI for

automated needle detection, tongue diagnosis, and other types of diagnosis

prediction.

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How could I ignore AI? And, moreover, could I integrate AI into my practice?

Could it make my professional life easier or even make me a better clinician? The

promise of the technology is that we could use it to offload mundane tasks, freeing

us up to focus on more meaningful aspects of our work. Perhaps a chatbot on my

website to answer simple questions or assistance with email queries? Better yet,

having run into difficulty following the arcane rules of “iron-clad” charting to satisfy

varying insurance requirements, might AI be able not only to record the patient

encounter, as at my veterinarian’s, but also ensure that it was charted correctly for

optimal reimbursement?

These kinds of applications seem relatively straightforward and

non-threatening, but what of using AI and large language models (LLMs) for

diagnosis? This is the fear that the machines may someday replace us. But as a lover

of case studies with mediocre Chinese language skills, this is also the potential

application that excites me the most: What if you trained an LLM on the complete

case studies (in Chinese) of your favorite historical physician. Many of these old

cases are freely available online and not subject to copyright. Maybe Liu Duzhou?

Now imagine you have a patient you are struggling with. Could you feed their signs

and symptoms into the model to learn what Professor Liu would do? Why not get

Xu Shuwei and Yi Tianshi’s takes as well, so you could compare their approaches?

These old Chinese doctors might serve as virtual guides, but they wouldn’t replace

the living clinician who would still need to sift through results and decide how best

to proceed. As Mollick says, AI can “mentor us to increase our own skills … helping

us fill the gaps in our own knowledge and pushing us to become better ourselves.”

(p. 191)

Finally, to data collection and analysis. All of us who chart electronically

potentially have an extraordinary amount of valuable information available to us, but

how to access and interpret it to increase our clinical efficacy? How do we sift