Harnessing Ocular Physiology: Fast and Sustained Tear Production With TRYPTYR® (acoltremon ophthalmic solution) 0.003% for Dry Eye Disease

Data from the pivotal COMET clinical trials reveal how the first topical ocular neuromodulator approved for the signs and symptoms of DED, TRYPTYR®, delivers a rapid increase in natural tear production.

John Hovanesian, MD (Moderator)*; Marjan Farid, MD*; Nate Lighthizer, OD*; Selina McGee, OD*

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A Potential Digital Co-Pilot in the Retina OR

AI systems may one day augment traditional surgical workflows to improve efficiency and patient outcomes.

Stanley Poh, MBBS, MMed (Ophth), FRCOphth, FAMS, and Daniel Ting, MD, PhD

Retina Today

KEY TAKEAWAYS By analyzing large volumes of data, AI could generate clinically meaningful insights to enable more informed decision making in the OR. AI could identify high-risk cases and flag factors such as suboptimal glycemic control, anticoagulant use, medical comorbidities, drug allergies, and mobility limitations. Agentic AI can act as a digital co-pilot by integrating preoperative, intraoperative, and postoperative capabilities. Vitreoretinal surgery is a complex discipline involving intricate and highly variable pathology. Many decisions depend on dynamic changes in anatomy and tissue behavior, surgical visualization, and potential complications. As such, vitreoretinal fellows face a steep learning curve. Years of training through residency, fellowship, and independent surgical practice are required to develop the judgement needed to determine whether surgery is warranted, which intervention is most appropriate, and how urgently it should be performed. As AI tools proliferate and improve, they could advance the evolution of the retina OR. By analyzing large volumes of clinical, imaging, and procedural data, AI could generate timely, clinically meaningful insights to enable more informed decision making across preoperative, intraoperative, and postoperative phases of care (Figure). In many ORs, digital visualization systems and intraoperative OCT have already broadened what surgeons can see and have influenced surgical decision making.

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What Aviation Can Teach Us About AI

Retina specialists looking to integrate new technology should seek a digital copilot, not autopilot.

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Retina Today

KEY TAKEAWAYS In aviation, the introduction of automation improved safety and efficiency but introduced increased reliance on automated systems and diminished situational awareness—and a similar phenomenon is beginning to emerge in medicine. To avoid the negative consequences of AI in medicine, the authors recommend viewing AI not as an autopilot capable of independently managing patient care, but as a digital copilot. Just as modern aviation combines automation with skilled pilots, the next generation of retina specialists should strive for a partnership in which AI enhances, rather than diminishes, human expertise. AI is rapidly transforming medicine, and few specialties illustrate this evolution better than retina.1 Modern retina care generates enormous quantities of data through OCT imaging (including OCT angiography), fundus photography, and fluorescein angiography. These highly standardized datasets are ideally suited for machine- and deep-learning applications. This has facilitated the development of AI algorithms with remarkable performance in detecting diabetic retinopathy, AMD, glaucoma, and numerous other diseases. Despite these impressive achievements, retina specialists know that clinical decision making rarely depends on imaging alone. Treatment decisions require consideration of symptoms and functional deficits, systemic disease, previous therapeutic responses, patient preferences, and subtle clinical findings that extend beyond image analysis. AI therefore represents an important complement—not a replacement—for clinical expertise. While AI developments promise substantial improvements in efficiency and patient care, they also raise an important question: How can we safely integrate AI into clinical practice without eroding physician expertise?

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