Few areas of eye care have absorbed new technology as quickly, or as quietly, as contact lens practice. Most clinicianshave adapted to the changes in their own chairs without naming them collectively. While the exam looks similar from across the room, the data captured during it, the tools used to interpret that data, and the lens dispensed at the end have all shifted in many ways. This article takes the opportunity to take a step back and observe the coordinated transformation of contact lens fitting examinations with the addition technological advancements.

The shift is not driven by any single breakthrough. It is the result of a convergence of several technologies, each developed for its own reasons, maturing at roughly the same time and beginning to act on one another. Together,various technological advancements are reshaping what a contact lens practice looks like and providing improved options for each patient sitting in the chair.

Four Currents, One Direction

Four currents are driving the shift. Imaging technology now captures the anterior segment in dimensions that were unavailable a decade ago. Artificial intelligence (AI) is moving from research curiosity to clinical decision support. Workflow tools are automating the documentation and analysis process that once consumed the practitioner's attention.And the lens itself is evolving from a passive optical device into a platform capable of sensing and delivering therapy.

Each of these currents matters individually. Together, they describe an emerging model that has tremendous impact.Practitioners are currently caring for patients at a time when the contact lens and the clinician are supported by data in ways that reshape the possibilities of a contact lens practice.

The Imaging Revolution

From CentralK to a Wider Diagnostic Surface: For most of contact lens history, the geometric description of the cornearelied on a small set of numbers. For many years, contact lenses were fit based upon central keratometry (k) readings and an estimate of corneal diameter. Those numbers were enough to fit many lenses, and they remain useful. But they describe a small fraction of the surface that can play an important role when a contact lens sits on the ocular surface.

Modern corneal topography expanded to provide thousands of data points across the central and paracentral cornea, and the technology has now diffused well beyond specialty practice. Topographers have become standard in many primary eye care offices, used not only for refractive screening but for early detection of irregular astigmatism, post-refractive surgery changes, and ectatic disease before lens fitting begins.¹

The availability of this information matters greatly for general contact lens practices because the patient population presenting for lenses is more heterogeneous than it was a generation ago. Post-laser-assisted in situ keratomileusis (LASIK) eyes, undiagnosed early keratoconus, and atypical corneal shapes appear regularly in routine fittings. Imagingthat was once reserved for surgical co-management can now be viewed as a valuable screening tool in standard contact lens workups.

Elevation,Tomography, and the 3D Cornea: Imaging technology can also capture elevation rather than curvaturealone. Scheimpflug tomography and similar systems generate a 3D model of the cornea, including posterior surface data that flat curvature maps cannot provide.

Posterior elevation has become particularly important in early keratoconus detection, where anterior changes may lag behind posterior changes by months or years² Several validated indices now combine anterior and posterior data into composite scores that flag suspicious corneas before frank ectasia develops.³

For the contact lens practitioner, this layer of imaging informs decisions about candidacy and lens choice. A cornea flagged as suspicious on tomography is a cornea that warrants different lens selection and often a different conversationabout long-term management.

Profilometry and the Scleral Fitting Workflow: An exciting layer of imaging, and possibly the most transformative for specialty practice, extends past the limbus onto the sclera itself. Corneoscleral profilometry, whether captured opticallyor through impression-based methods, generates a topographic map of the entire ocular surface.

The clinical implication is significant. Scleral lens fitting began, and in many cases continues to be, a diagnostic-set process. A practitioner selects an initial lens based on known corneal parameters, evaluates the fit, and modifies the fit as needed. Profilometry-driven scleral lens design begins with a measured map of the patient's ocular surface andproduces a lens based upon that map.

Toric and quadrant-specific scleral shapes, once estimated by clinical observation, can now be designed to measured asymmetry. When using profilometry, the practitioner's role shifts from iterating toward a fit to verifying and refining a fit that the imaging has largely specified.

Anterior Segment Optical Coherence Tomography in the Fitting Chair: Anterior segment optical coherence tomography has added another dimension by visualizing the lens-cornea relationship directly. The depth of the fluid reservoir under a scleral lens, and the edge profile of a lens can be measured rather than estimated.⁴ What was once inferred from fluorescein patterns and clinical experience can now be quantified to the micron. The interpretationof fit has not become easier, but it has become considerably more precise.

AI in Diagnostics and Lens Selection

Pattern Recognition Where Humans Plateau: Some clinical tasks reward pattern recognition more than reasoning, and corneal disease detection is one of them. Experienced clinicians develop strong intuition for the topographic signature of keratoconus, but that intuition takes a long time to learn and varies between practitioners. In particular, subclinical disease often presents with changes too subtle for consistent human detection.⁵

Machine learning models trained on tomographic and biomechanical data have repeatedly outperformed conventional indices in detecting subclinical keratoconus and very asymmetric ectasia.^6,7 The mechanism is straightforward. The model considers many parameters at once and weights the interactions of these parameters in order to assist practitioners in recognizing less obvious patterns. The output is a single probability score that integrates information in ways that the practitioner can quickly and simply understand.

For contact lens practice, the implication is practical. A patient referred for soft lens fitting whose tomography returns an “elevated machine-learning ectasia score” is a patient whose lens choice and follow-up should change, often beforethe practitioner would have flagged the patient/cornea on visual inspection alone.

Data Infrastructure as the Prerequisite: Most clinicians have had a version of the same experience. A patient presents with a complex cornea, and the practitioner needs to determine the best lens for the eye. The information needed to support better lens decisions exists within fitting guides, parameter charts, manufacturer specifications, and decades of clinical experience. What has been missing is a way to bring it together and make it usable.

That gap is now closing with the evolution of AI. Across academic, clinical, and commercial settings, machine learning systems are pulling together fitting guides, parameter specifications, and outcome data into unified reviewable databases. This is a new way of converting information that was always present—into a form that software and practitioners can use.⁸ As databases mature, the contact lens field is acquiring the same foundation that made AI-driven diagnostics possible. The decision support tools described next are not theoretical projections. They are directconsequences of work already in progress.

From Fitting Guides to Decision Support

With machine learning infrastructure beginning to take shape, decision support becomes possible. Manufacturer fitting guides remain the foundation of the discipline, but they describe average behavior across average corneas. The patientsitting in the chair sometimes may have irregular astigmatism, post-surgical anatomy, atypical scleral shape, ocular surface disease, or a combination of these findings.

Two advances are now translating structured lens data into clinical utility. The first is the ability to query that data in plain language. Historically, a practitioner seeking a soft toric lens available in a high cylinder power on an oblique axis in a high-Dk material either chose a lens based upon experience or, by phone calls to lab consultants, or by manual cross-referencing of fitting guides. An AI system that can find the answer to clinical questions, like this, into structured retrieval against a lens database can now return that answer in seconds.⁹

The second advancement in AI is the formalization of clinical decision frameworks. The progression from soft toric to corneal GP to hybrid or scleral is well understood by experienced specialty practitioners, but it has rarely been codified in a way that is usable by less experienced clinicians or software. Encoding that decision tree turns expert clinical judgment into a resource that can be queried, taught, and improved with new outcomes. These applications can now connect the indications, contraindications, candidacy criteria, and failure modes that drive lens type selection.⁹

Workflow Automation and the AI-Augmented Exam

Ambient Documentation in the Exam Room: For most of the field's history, the contact lens exam has been a divided activity. The practitioner observes, asks, and examines. When that is done, the practitioner then turns to the keyboard todocument. The split between clinical attention and documentation has been so familiar that it stopped registering as a problem until tools became available to help.

Ambient AI scribes capture the spoken exchange of an exam in real time, transcribe it, and produce structured clinical notes that the practitioner reviews and signs. Adoption across medicine has accelerated rapidly in the past 2 years, with measurable improvements in documentation completeness, after-hours charting time, and clinician burnout scores.^10,11

In a contact lens practice, the gains are particularly visible during specialty fittings, where the conversation about lenshistory, wear schedule, comfort, and visual function generates documentation that is tedious to capture but clinically important. A scribe that captures this exchange while the practitioner remains engaged with the patient changes therhythm of the visit. The patient feels heard rather than recorded, and the practitioner finishes the visit with a note thatmay be more complete than what hurried typing would have produced.

The clinical effect compounds over a full day of patients. Time recovered from documentation does not have to befilled with more visits. It can be spent on the patients already on the schedule. Extra patient time can allow a for asecond look at a fluorescein pattern, a longer conversation about a lens that is not quite working, or provide time for a careful explanation of a change in wear schedule that affects compliance. These elements of contact lens care pay back in long-term outcomes.

The Lens as a Platform

Smart Lenses and Drug Delivery Therapeutic Lenses Move from Concept to Reality: For most of contact lens history, the lens has been regarded only as an optical device. That assumption is now being revisited, and the most concrete example is the drug-eluting lens.

A daily disposable lens infused with an antihistamine attempted to address the management of ocular itch associatedwith allergic conjunctivitis, becoming the first US Food and Drug Administration (FDA)-approved drug-eluting contact lens.¹² The product was subsequently discontinued globally without being commercially launched in the United States, but its FDA approval established the regulatory and clinical pathway for lens use in this category. The clinical implications extended beyond convenience. Sustained drug delivery from a lens worn during a symptomatic period may eliminate the timing problems associated with eye drop use and could lessen the compliance failures that limit topical therapy in real-world use.

Research into drug-eluting platforms for glaucoma medications, antibiotics, and anti-inflammatory agents is underway. The contact lens is increasingly transforming from a vehicle for vision correction alone into a substrate for sustained-release pharmacology. We are at a point where this technology will allow us to put the medication exactly where the disease is.

Sensing Lenses and Diagnostic Wear: The second frontier in technology-driven contact lenses is the “sensing lens.” Continuous intraocular pressure (IOP) monitoring occurring through a soft silicone lens equipped with embedded strain sensors has been clinically validated and is in selective use for glaucoma research and management. Studies of these lenses have captured data over 24 hours of wear, revealing IOP fluctuations that office measurements cannot detect, with implications for treatment decisions in progressive disease.^13,14

Glucose-sensing lenses for noninvasive diabetes monitoring have been the subject of sustained research, although the path to clinical use has proven harder than early projections suggested.¹⁵ Recent work in plasmonic nanostructure-based sensing has demonstrated that tear glucose can be detected with high accuracy at clinically meaningful concentrations, suggesting that the architectural problems that limited earlier generations of sensors may now be yielding to new approaches.¹⁶

These advances mark a meaningful expansion of how a contact lens can be used. While lenses have always corrected vision, these devices now show the possibilities of being able to monitor, to deliver, and to sense. Contact lenses are nolonger defined by optics alone, and that shift is one of the more interesting stories in the field’s recent history.

Practice at the Center of the Network

The 4 currents traced through this article (imaging, AI-driven decision support, ambient documentation, and the lens asa platform) are not converging by accident. Each is moving toward a contact lens practice in which the eye, the contact lens, the patient record, and the clinician are connected by data in ways that generate compounding value.

A topographer no longer just produces a map, it produces structured input for a decision support system. A scribe no longer just transcribes a note, it returns time to the practitioner. A drug-eluting lens no longer just corrects vision, it actson the disease at the same time. Each technology becomes more useful in the company of the others.

A worry that runs underneath much of this conversation deserves a direct answer. Many clinicians wonder whether thetechnologies described in this article will eventually replace them. There is good reason to believe they will not. Every advance traced through these pages (ie, pattern recognition in imaging, decision support for lens selection, ambient documentation, sensing and drug delivery) works by giving the clinician better information, more time, or a better tool. None of them performs the act that defines contact lens practice, which is sitting with a patient and recommending a course of action that the patient can trust. That act is irreducibly human.

The practitioner looks at the eye and the lenses and determines the next steps based upon their observations and the data generated. Practitioners also troubleshoot those unpredictable results that are so frequently associated with the human body. These new technologies are built around the practitioner and patient examination, not in place of it.

The most important thing to understand about the “connected eye” is that the connection itself is the point. The single technologies mentioned in this article are transformative together—as a network. Contact lens practice has always been a relationship between a clinician, a patient, and a polymer. It is becoming a relationship mediated and enriched by data, and the practice of the next decade will belong to the clinicians who learn to work fluently inside that network.

References

1. Kong AW, Ahmad TR, Turner ML, et al. Trends in corneal topography and tomography imaging for keratoconusmanagement. Clin Ophthalmol. 2022;16:1357-1363. doi: 10.2147/OPTH.S361352

2. Erol MA, Atalay E, Özalp O, Divarcı A, Yıldırım N. Superiority of baseline biomechanical properties over corneal tomography in predicting keratoconus progression. Turk J Ophthalmol. 2021;51(5):257-264. doi: 10.4274/tjo.galenos.2020.78949

3. Belin MW, Alizadeh R, Torres-Netto EA, Hafezi F, Ambrósio R Jr, Pajic B. Determining progression in ectatic corneal disease. Asia Pac J Ophthalmol (Phila). 2020;9(6):541-548. doi: 10.1097/APO.0000000000000333

4. Yeung D, Sorbara L. Scleral lens clearance assessment with biomicroscopy and anterior segment optical coherencetomography. OptomVisSci. 2018;95(1):13-20. doi: 10.1097/OPX.0000000000001164

5. Henriquez MA, Hadid M, Izquierdo L Jr. A Systematic Review of Subclinical Keratoconus and Forme FrusteKeratoconus. J Refract Surg. 2020;36(4):270-279. doi: 10.3928/1081597X-20200212-03

6. Maile H, Li JO, Gore D, et al. Machine learning algorithms to detect subclinical keratoconus: systematic review. JMIR Med Inform. 2021;9(12):e27363. doi: 10.2196/27363

7. Lopes BT, Ramos IC, Salomão MQ, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol. 2018;195:223-232. doi: 10.1016/j.ajo.2018.08.005

8. TheRightContact. Why contact lens practice has been waiting for AI. Accessed April 20, 2026.https://www.therightcontact.com/insights/why-contact-lens-practice-has-been-waiting-for-ai

9. TheRightContact. What practitioners can now ask: natural language and clinical frameworks in contact lens decision support. Accessed April 20, 2026. https://www.therightcontact.com/insights/what-practitioners-can-now-ask-natural-language-and-clinical-frameworks-in

10. Lukac PJ, Turner W, Vangala S, et al. Ambient AI scribes in clinical practice: a randomized trial. NEJM AI. 2025;2(12):10.1056/aioa2501000. doi: 10.1056/aioa2501000

11. Olson KD, Meeker D, Troup M, et al. Use of ambient AI scribes to reduce administrative burden and professional burnout. JAMA Netw Open. 2025;8(10):e2534976. doi: 10.1001/jamanetworkopen.2025.34976

12. Pall B, Gomes P, Yi F, Torkildsen G. Management of ocular allergy itch with an antihistamine-releasing contact lens.Cornea. 2019;38(6):713-717. doi: 10.1097/ICO.0000000000001911

13. Mansouri K, Medeiros FA, Tafreshi A, Weinreb RN. Continuous 24-hour monitoring of intraocular pressure patterns with a contact lens sensor: safety, tolerability, and reproducibility in patients with glaucoma. Arch Ophthalmol. 2012;130(12):1534-1539.

14. De Moraes CG, Mansouri K, Liebmann JM, Ritch R; Triggerfish Consortium. Association between 24-hour intraocular pressure monitored with contact lens sensor and visual field progression in older adults with glaucoma. JAMA Ophthalmol. 2018;136(7):779-785. doi: 10.1001/jamaophthalmol.2018.1746

15. Elsherif M, Moreddu R, Alam F, Salih AE, Ahmed I, Butt H. Wearable smart contact lenses for continual glucosemonitoring: a review. Front Med (Lausanne). 2022;9:858784. doi: 10.3389/fmed.2022.858784

16. Roostaei N, Hamidi SM. Plasmonic smart contact lens based on etalon nanostructure for tear glucose sensing. Sci Rep. 2025;15(1):14948. doi: 10.1038/s41598-025-99624-2 

JASON COMPTON, OD, owns a private practice (Compton Eye Associates) in the New York City area. He is the past chair of the American Optometric Association’s Contact Lens & Cornea Section. He has received remuneration from Johnson & Johnson, Alcon, and CooperVision.