Once upon a time, medical AI excelled at detecting early signs of disease and flagging suspicious lesions. Today, it's gone far beyond that—offering structural diagnoses, planning interventions, conducting post-op follow-ups, educating patients, and even managing medication compliance.

From interpreting OCT images and forecasting the progression of myopia in children, to remotely tracking vision changes via post-op apps, an increasing number of “detail tasks” are being handed over from doctors to algorithms and platform-based systems.

The question “Are doctors still the central players?” is no longer just aimed at surgical robots. It now concerns broader, more invisible support systems.

Ophthalmology, one of the first specialties to adopt medical AI, stands at the forefront of this shift. Surprisingly, it’s not newly minted doctors who feel the greatest impact, but rather seasoned professionals whose value has long been built on judgment, precision, and consistency.

Many are beginning to realize that certain skills they've honed for decades are now being absorbed—or replaced—by machines.

But is AI really here to take doctors’ jobs? Or is it merely forcing the entire profession to redefine and revalue its core contributions?

AI Is Taking Over the “Pre-Op Thinking”

In traditional ophthalmology, preoperative decision-making has been the ultimate demonstration of a doctor’s experience—recognizing subtle abnormalities in imaging, assessing surgical risks, and crafting personalized treatment plans all required both deep knowledge and sharp intuition.

Now, AI is becoming an active participant in this chain. Take intelligent OCT image analysis, for example—it can automatically identify and risk-grade structural changes such as macular holes, vitreomacular traction syndrome, and diabetic retinopathy. Some AI platforms even recommend surgical procedures and predict visual outcomes.

For doctors once renowned for their diagnostic prowess, this feels like a profound shift. Their once-core expertise is now being quantified, replicated, and standardized by machines.

Similarly, AI-powered tools for predicting myopia progression in children and adolescents can integrate data from eye exams, axial length measurements, and outdoor activity logs to forecast refractive changes over the next 12–24 months. While this empowers doctors to make more data-driven plans, it also erodes their authority in tailoring interventions on a case-by-case basis.

If pre-op judgment can be precisely modeled by AI, what remains uniquely human in that process?

AI Is Encroaching on Daily Patient Management

If pre-op AI decision-making is still seen as an assistive tool, then the digitalization of post-op care has already begun to reshape the doctor-patient relationship.

In areas such as refractive surgery, cataract procedures, and vitrectomy, leading institutions have launched post-op management apps. These platforms handle tasks like medication reminders, routine questionnaires, remote vision screening, and even triage for follow-up appointments using AI. Much of the time doctors once spent making calls, responding to messages, or reviewing feedback forms has now been delegated to software.

This goes beyond efficiency—it's a redefinition of post-op accountability. Previously, a doctor's meticulous follow-up care was seen as a reflection of responsibility and dedication, playing a key role in building patient trust and ensuring referrals.

Today, this phase is standardized, outsourced, and platformized: doctors move to the background, while AI systems take center stage, only escalating complex or urgent cases to human intervention.

The physician's role has shifted from being a continuous caregiver to a behind-the-scenes clinical consultant. While the breadth of management has expanded, its depth has thinned.

Even during surgery, changes are underway. While surgical robots aren’t yet mainstream in ophthalmology, digital microscopes and intraoperative navigation platforms are becoming more common. These technologies are beginning to replace the “surgeon’s touch” with real-time data. With the development of intraoperative AI systems, questions like “How much should be excised?”, “Where is residual tissue?”, and “Is there bleeding?” may soon be answered not by human instinct, but by algorithm-generated prompts.

These systems may not replace doctors outright, but they’re certainly redrawing the boundaries of technical expertise.

The Value of the Doctor Is Being Reconstructed

As AI permeates pre-op planning, intra-op execution, and post-op care, the role of ophthalmologists is undergoing fundamental change.

Traditionally, experience was a doctor’s most valuable asset and core professional barrier to entry. A senior physician could detect early signs of retinal damage from a blurry OCT image because of decades of clinical exposure. They could anticipate surgical risks thanks to years of handling borderline cases. This kind of knowledge—rooted in immersion and repetition—was hard to teach and harder to replace.

Now, AI is turning experience into data: explicit, transferable, and scalable. Algorithms may not “have experience,” but they process hundreds of thousands of cases daily. They may lack intuition, but they outperform it in consistency.

This doesn’t mean doctors are obsolete—it means the definition of a “good doctor” is changing. Experience is no longer an end goal, but an interface—something that can be amplified by tools and algorithms. Young doctors who rely solely on time-served learning—believing that “shadowing enough surgeries” will be enough—risk falling out of step. On the other hand, those who embrace AI tools, understand data structures, and contribute to care pathway design may find themselves at the forefront of this transformation.

When Doctors Become System Operators

AI in ophthalmology is not just a technological upgrade—it’s an identity shift. The doctor is no longer the sole decision-maker, but increasingly a bridge between patients, tools, and algorithms.

This is both exciting and unsettling.

Exciting because AI offloads repetitive, mentally draining tasks, allowing doctors to focus on complex cases that truly need human insight. Unsettling because the very skills that once defined professional dignity—judgment, meticulousness, clinical intuition—are quietly being diluted.

It’s time we seriously ask: In an AI-driven era, what defines a good doctor?

If experience can be quantified, decisions predicted, and relationships automated—how can doctors continue to remain central to the healing process?

This question doesn’t belong to the future. It’s already here.

By MedChina, June 23, 2025