ASCRS News: YES Connect
December 2023
by Liz Hillman
Editorial Co-Director
We have all read about ways artificial intelligence (AI) will change the landscape of diagnostics and treatment in ophthalmology. I wanted to explore a different perspective in this article.
Diagnostic and treatment innovations are still very much in the experimental phase and subject to unknown regulatory hurdles. What is most exciting to me about AI, including developments with ChatGPT, are the potential for these tools to save us time as clinicians and improve our delivery of care.
Software that assists in practice management and patient communications can be implemented rapidly into existing workflows. Problems to be addressed include: reducing the burden of clinical documentation, communicating more efficiently, and writing educational content for patients. These products could reach us much sooner than algorithms that help us make clinical decisions or machines that help us perform surgery.
In this article, I wanted to focus our attention on opportunities to leverage AI to streamline our practices.
—Shawn Lin, MD, YES Connect Editor
When ChatGPT started making headlines, all sectors of the workforce experienced renewed interest in and discussion on how AI would change their profession, medicine included. Elements of AI have already made strides into medicine, and ophthalmology specifically, for some time.
Ophthalmology in many ways is ripe for AI due to it being an image-heavy field with large datasets.1 While there’s the potential for AI in diagnostic areas, James Wang, a partner at the early-stage venture capital firm Creative Ventures, which focuses on healthcare among other areas, said this application faces more regulatory hurdles. Where he thinks ophthalmology—and healthcare as a whole—will see applications for AI are in clinical aids and front-of-the-office areas.
There are already tools that use AI to function as digital scribes, for example, Mr. Wang said. He also said there are some AI systems in the OR. One company, he noted, has a system that counts all the scalpels and forceps in the surgical field (using a camera and AI software), making sure all are accounted for at the end of surgery.
The line starts to get blurred with clinical aids that start making recommendations, Mr. Wang said.
“I’ve seen an oncology AI that suggests drug cocktails or treatment plans that are most likely to fit a specific oncology patient. It’s not making a recommendation, it’s not prescribing anything, but it’s giving some statistical correlations based on what they’ve seen with genetics, based on what they’ve seen with medical history,” Mr. Wang said.
John Hovanesian, MD, envisioned similar AI tools for ophthalmology. He was involved in creating MDbackline, which originally helped physicians automate patient communications. Its first embodiment, Dr. Hovanesian said, did not involve AI, but Alcon, which purchased it in 2022, is working that into the system.
“[MDbackline] was designed to be situation specific,” he said. “If you asked the patient a question, it would respond with educational material that was appropriate. It wasn’t AI but it was algorithmic. The advantage of using a system like that and why I created it was not just so we could save time in the clinic but also to give us insights in a structured way on patient sentiment. For example, there are a lot of therapeutic areas where we don’t know well how products work. … We need to collect data on which characteristics have the best outcomes, and we need to turn that around and take those outcome characteristics and apply them to incoming patients.”
Dr. Hovanesian said Alcon is in the process of integrating MDbackline into its digital workflow, which is an evolving ecosystem with AI and structured logic to help physicians with cataract surgery.
Dr. Hovanesian said technology like this could be used for pharmaceuticals as well. He said when patients come in with dry eye, for example, they’re often prescribed a drug. Physicians, however, don’t routinely collect feedback on how these drugs work in the real world outside of formal studies.
“This is a narrow area in healthcare where AI can help us, and it’s an important one because it’s one where it would be offering quality assurance processes that we currently don’t do,” he said.
Dr. Hovanesian also gave an example of how AI could help in patient education. If a patient says their dry eye is more prevalent in the morning, for example, AI could serve up educational material about the unwanted side effects of fans blowing on the patient at night.
“That’s innocuous advice, nothing that would be high risk, but helpful to patients who haven’t thought about it. That kind of thing I could see us including and supplementing our care,” he said.
Another broad area is record searching. AI, Dr. Hovanesian said, could identify glaucoma patients who should come in for updated visual field imaging, for example. It also could alert physicians to things they need to address, for example, if a biopsy is conducted but there isn’t an indication that the result came in. “When we do a test, we’re responsible for following up on the results medicolegally,” he said. “AI could assist us by combing through our records and looking for patients who have fallen through the cracks.”
Dr. Hovanesian and Mr. Wang spoke about how chatbots, similar to ChatGPT, can be used in ophthalmology, such as for appointment scheduling, providing educational content, and triaging to the correct department. A study published earlier this year evaluated AI chatbots’ ability to respond to patient questions posted on a social media forum. The authors concluded that the chatbot “generated quality and empathetic responses.”2
“A lot of tools like that would be fairly easy applications of AI to enhance the practice of medicine. They’re not sexy but they would be great for patient care,” Dr. Hovanesian said.
Shawn Lin, MD, envisioned a future where AI was used not only for scribing and patient education and communication but also for combating insurance denials or preauthorization letters and real-time decision support. He said that an AI system could process the natural language of the physician in real time and suggest further testing to them.
As for ethics and best practices, Mr. Wang said there are several considerations. Overall, he said it’s a been a best practice in the chatbot and AI assistant world to explicitly tell people the response is driven by AI. This serves as a disclaimer and helps set expectations to the human being on the other side.
Mr. Wang said the FDA is struggling with the blurry line for some clinical aids that don’t specifically diagnose.
“It’s a bit of a gray zone, and the FDA has had questions of when you step over the line” in terms of a diagnosis, he said.
Another challenge for AI in practice will be getting all areas of the practice, from the front-of-the-office staff to technicians and nurses to the physician, on board with adopting certain technologies. Dr. Hovanesian said everyone has to do their piece of the puzzle for newly adopted technologies to succeed. Still, Dr. Hovanesian said nothing will substitute the experience and judgment of the human physician.
“AI is going to get better and better and may replace some physician function, but we need a level of judgment passed on it and some supervision of it for its initial rollout. There clearly needs to be some healthy skepticism about how we approach this and what we hand over. At the same time, we have to not be too skeptical. We need to recognize that our brains and bodies can only work so fast, and if we have tools that can help us deal with this growing population more effectively, we should look at it as an opportunity to do what we do best, which is use the skills that only experienced doctors have,” he said.
Mr. Wang said he expects AI in medicine to be more constrained than people may think. The challenge, especially from a diagnostic standpoint, is “how do you ensure that in rare cases and exceptions that [AI] always works?” he said.
AI in medicine is probably “less inevitable than the tech enthusiasts say, but it’s also not as scary and not as threatening as certain clinicians think it is,” Mr. Wang said.
About the sources
John Hovanesian, MD
Harvard Eye Associates
Laguna Hills, California
Shawn Lin, MD
Assistant Professor of Cataract and Refractive Surgery
Associate Residency Program Director
Medical Director
UCLA Stein Eye Center Calabasas
Los Angles, California
James Wang, MBA
General Partner
Creative Ventures
Oakland, California
References
- Lu W, et al. Applications of artificial intelligence in ophthalmology: general overview. J Ophthalmol. 2018:5278196.
- Ayers JW, et al. Comparing physician and artificial intelligence chatbot responses to patient questions posted to a public social media forum. JAMA Intern Med. 2023;183:589–596.
Relevant disclosures
Hovanesian: Alcon, various eyecare technology companies
Lin: None
Wang: None
Contact
Hovanesian: DrHovanesian@harvardeye.com
Lin: slin@jsei.ucla.edu
Wang: james@creativeventures.vc
