Pearls for iTrack Canaloplasty With Goniotomy and iDose TR Implantation

I. Paul Singh, MD

Eyetube

In this case, a patient with glaucoma who had not undergone MIGS remained on three topical medications despite prior cataract surgery and selective laser trabeculoplasty. I. Paul Singh, MD, demonstrates canaloplasty with the iTrack (Nova Eye Medical), goniotomy, and implantation of the iDose TR (Glaukos), followed by insertion of Dextenza (dexamethasone ophthalmic insert 0.4 mg, Ocular Therapeutix).

Ophthalmic Executives Provide Company Updates (Part 1 of 2)

Eyetube

Leaders of several thalamic companies provide updates on their pipeline and new products in the special edition of Eyewire News the Podcast. Learn more about your ad choices. Visit megaphone.fm/adchoices

What Aviation Can Teach Us About AI

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

Andreas Pollreisz, MD; Ariel Yuhan Ong, MBChB, FRCOphth; and Josef Huemer, MD, FEBO

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?